Engineering › Biomedical Engineering

Non-Invasive Vital Sign Monitoring

Works Count: 45337

Wikipedia

Description

This cluster of papers focuses on advancements in non-contact physiological monitoring technology, including photoplethysmography, remote monitoring, wearable sensors, pulse oximetry, respiratory rate measurement, Doppler radar, heart rate variability, continuous blood pressure estimation, motion artifact reduction, and smart healthcare applications.

Keywords

Photoplethysmography; Remote Monitoring; Wearable Sensors; Pulse Oximetry; Respiratory Rate; Doppler Radar; Heart Rate Variability; Continuous Blood Pressure Estimation; Motion Artifact Reduction; Smart Healthcare

Validation of near-infrared spectroscopy in humans

1994-12-01

D. M. Mancini , Lizann Bolinger , Hongji Li +3 more

Near-infrared (NIR) spectroscopy is a noninvasive technique that uses the differential absorption properties of hemoglobin to evaluate skeletal muscle oxygenation. Oxygenated and deoxygenated hemoglobin absorb light equally at 800 nm, … Near-infrared (NIR) spectroscopy is a noninvasive technique that uses the differential absorption properties of hemoglobin to evaluate skeletal muscle oxygenation. Oxygenated and deoxygenated hemoglobin absorb light equally at 800 nm, whereas at 760 nm absorption is primarily from deoxygenated hemoglobin. Therefore, monitoring these two wavelengths provides an index of deoxygenation. To investigate whether venous oxygen saturation and absorption between 760 and 800 nm (760–800 nm absorption) are correlated, both were measured during forearm exercise. Significant correlations were observed in all subjects (r = 0.92 +/- 0.07; P < 0.05). The contribution of skin flow to the changes in 760–800 nm absorption was investigated by simultaneous measurement of skin flow by laser flow Doppler and NIR recordings during hot water immersion. Changes in skin flow but not 760–800 nm absorption were noted. Intra-arterial infusions of nitroprusside and norepinephrine were performed to study the effect of alteration of muscle perfusion on 760–800 nm absorption. Limb flow was measured with venous plethysmography. Percent oxygenation increased with nitroprusside and decreased with norepinephrine. Finally, the contribution of myoglobin to the 760–800 nm absorption was assessed by using 1H-magnetic resonance spectroscopy. At peak exercise, percent NIR deoxygenation during exercise was 80 +/- 7%, but only one subject exhibited a small deoxygenated myoglobin signal. In conclusion, 760–800 nm absorption is 1) closely correlated with venous oxygen saturation, 2) minimally affected by skin blood flow, 3) altered by changes in limb perfusion, and 4) primarily derived from deoxygenated hemoglobin and not myoglobin.

Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems

2002-04-01

R.E. Mayagoitia , Anand V. Nene , Petrus H. Veltink

View PDF Chat

On the Analysis of Fingertip Photoplethysmogram Signals

2012-06-01

Mohamed Elgendi

Photoplethysmography (PPG) is used to estimate the skin blood flow using infrared light. Researchers from different domains of science have become increasingly interested in PPG because of its advantages as … Photoplethysmography (PPG) is used to estimate the skin blood flow using infrared light. Researchers from different domains of science have become increasingly interested in PPG because of its advantages as non-invasive, inexpensive, and convenient diagnostic tool. Traditionally, it measures the oxygen saturation, blood pressure, cardiac output, and for assessing autonomic functions. Moreover, PPG is a promising technique for early screening of various atherosclerotic pathologies and could be helpful for regular GP-assessment but a full understanding of the diagnostic value of the different features is still lacking. Recent studies emphasise the potential information embedded in the PPG waveform signal and it deserves further attention for its possible applications beyond pulse oximetry and heart-rate calculation. Therefore, this overview discusses different types of artifact added to PPG signal, characteristic features of PPG waveform, and existing indexes to evaluate for diagnoses. Keywords: Photoplethysmography, acceleration plethysmogram, second derivative plethysmogram, digital volume pulse, ageing, artery, autonomic function, blood pressure, cardiovascular, heart rate, pulse wave analysis, vascular disease

View PDF Chat

Detecting Pulse from Head Motions in Video

2013-06-01

Guha Balakrishnan , Frédo Durand , John V. Guttag

We extract heart rate and beat lengths from videos by measuring subtle head motion caused by the Newtonian reaction to the influx of blood at each beat. Our method tracks … We extract heart rate and beat lengths from videos by measuring subtle head motion caused by the Newtonian reaction to the influx of blood at each beat. Our method tracks features on the head and performs principal component analysis (PCA) to decompose their trajectories into a set of component motions. It then chooses the component that best corresponds to heartbeats based on its temporal frequency spectrum. Finally, we analyze the motion projected to this component and identify peaks of the trajectories, which correspond to heartbeats. When evaluated on 18 subjects, our approach reported heart rates nearly identical to an electrocardiogram device. Additionally we were able to capture clinically relevant information about heart rate variability.

View PDF Chat

Non-contact, automated cardiac pulse measurements using video imaging and blind source separation

2010-05-07

Ming‐Zher Poh , Daniel McDuff , Rosalind W. Picard

Remote measurements of the cardiac pulse can provide comfortable physiological assessment without electrodes.However, attempts so far are non-automated, susceptible to motion artifacts and typically expensive.In this paper, we introduce a … Remote measurements of the cardiac pulse can provide comfortable physiological assessment without electrodes.However, attempts so far are non-automated, susceptible to motion artifacts and typically expensive.In this paper, we introduce a new methodology that overcomes these problems.This novel approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into independent components.Using Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to an FDA-approved finger blood volume pulse (BVP) sensor and achieved high accuracy and correlation even in the presence of movement artifacts.Furthermore, we applied this technique to perform heart rate measurements from three participants simultaneously.This is the first demonstration of a low-cost accurate video-based method for contact-free heart rate measurements that is automated, motion-tolerant and capable of performing concomitant measurements on more than one person at a time.

View PDF Chat

Remote Heart Rate Measurement from Face Videos under Realistic Situations

2014-06-01

Xiaobai Li , Jie Chen , Guoying Zhao +1 more

Heart rate is an important indicator of people's physiological state. Recently, several papers reported methods to measure heart rate remotely from face videos. Those methods work well on stationary subjects … Heart rate is an important indicator of people's physiological state. Recently, several papers reported methods to measure heart rate remotely from face videos. Those methods work well on stationary subjects under well controlled conditions, but their performance significantly degrades if the videos are recorded under more challenging conditions, specifically when subjects' motions and illumination variations are involved. We propose a framework which utilizes face tracking and Normalized Least Mean Square adaptive filtering methods to counter their influences. We test our framework on a large difficult and public database MAHNOB-HCI and demonstrate that our method substantially outperforms all previous methods. We also use our method for long term heart rate monitoring in a game evaluation scenario and achieve promising results.

Wearable Photoplethysmographic Sensors—Past and Present

2014-04-23

T. Tamura , Yuka Maeda , Masaki Sekine +1 more

Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of … Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.

View PDF Chat

Bedside technics for the evaluation of ventricular function in man

1969-04-01

Arnold M. Weissler , Willard S. Harris , Clyde D. Schoenfeld

Eulerian video magnification for revealing subtle changes in the world

2012-07-01

Haoyu Wu , Michael Rubinstein , Eugene Shih +3 more

Our goal is to reveal temporal variations in videos that are difficult or impossible to see with the naked eye and display them in an indicative manner. Our method, which … Our goal is to reveal temporal variations in videos that are difficult or impossible to see with the naked eye and display them in an indicative manner. Our method, which we call Eulerian Video Magnification, takes a standard video sequence as input, and applies spatial decomposition, followed by temporal filtering to the frames. The resulting signal is then amplified to reveal hidden information. Using our method, we are able to visualize the flow of blood as it fills the face and also to amplify and reveal small motions. Our technique can run in real time to show phenomena occurring at the temporal frequencies selected by the user.

View PDF Chat

A RAPID PLETHYSMOGRAPHIC METHOD FOR MEASURING THORACIC GAS VOLUME: A COMPARISON WITH A NITROGEN WASHOUT METHOD FOR MEASURING FUNCTIONAL RESIDUAL CAPACITY IN NORMAL SUBJECTS 1

1956-03-01

Arthur B. DuBois , Stella Y. Botelho , George N. Bedell +2 more

View PDF Chat

Remote plethysmographic imaging using ambient light

2008-12-12

Wim Verkruysse , Lars O. Svaasand , J. Stuart Nelson

Plethysmographic signals were measured remotely (> 1m) using ambient light and a simple consumer level digital camera in movie mode. Heart and respiration rates could be quantified up to several … Plethysmographic signals were measured remotely (> 1m) using ambient light and a simple consumer level digital camera in movie mode. Heart and respiration rates could be quantified up to several harmonics. Although the green channel featuring the strongest plethysmographic signal, corresponding to an absorption peak by (oxy-) hemoglobin, the red and blue channels also contained plethysmographic information. The results show that ambient light photo-plethysmography may be useful for medical purposes such as characterization of vascular skin lesions (e.g., port wine stains) and remote sensing of vital signs (e.g., heart and respiration rates) for triage or sports purposes.

View PDF Chat

TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise

2014-09-19

Zhilin Zhang , Zhouyue Pi , Benyuan Liu

Heart rate monitoring using wrist-type photoplethysmographic signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. … Heart rate monitoring using wrist-type photoplethysmographic signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this study, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, and the Pearson correlation between the estimates and the ground truth of heart rate was 0.992. This framework is of great values to wearable devices such as smartwatches which use PPG signals to monitor heart rate for fitness.

View PDF Chat

Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

2010-10-19

Ming‐Zher Poh , Daniel McDuff , Rosalind W. Picard

We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the … We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.

View PDF Chat

Direct Blood Pressure Measurement —Dynamic Response Requirements

1981-03-01

Reed M. Gardner

View PDF Chat

Mobile monitoring with wearable photoplethysmographic biosensors

2003-05-01

H. Harry Asada , P. Shaltis , Andrew Reisner +2 more

We address both technical and clinical issues of wearable biosensors (WBS). First, design concepts of a WBS are presented, with emphasis on the ring sensor developed by the author's group … We address both technical and clinical issues of wearable biosensors (WBS). First, design concepts of a WBS are presented, with emphasis on the ring sensor developed by the author's group at MIT. The ring sensor is an ambulatory, telemetric, continuous health-monitoring device. This WBS combines miniaturized data acquisition features with advanced photoplethysmographic (PPG) techniques to acquire data related to the patient's cardiovascular state using a method that is far superior to existing fingertip PPG sensors. In particular, the ring sensor is capable of reliably monitoring a patient's heart rate, oxygen saturation, and heart rate variability. Technical issues, including motion artifact, interference with blood circulation, and battery power issues, are addressed, and effective engineering solutions to alleviate these problems are presented. Second, based on the ring sensor technology the clinical potentials of WBS monitoring are addressed.

The velocity of pulse wave in man

1922-04-01

J. Crighton Bramwell , Archibald Vivian Hill

In an investigation now being carried out by us at Manchester observations are being made, under various conditions, upon the velocity of the pulse wave in man. As a preliminary … In an investigation now being carried out by us at Manchester observations are being made, under various conditions, upon the velocity of the pulse wave in man. As a preliminary to this investigation it was thought advisable to study the theory of the transmission of the pulse wave, and the following pages contain the results arrived at, together with an account of experiments upon the velocity of the pulse wave in an isolated human artery.

Cerebral Intravascular Oxygenation Correlates With Mean Arterial Pressure in Critically Ill Premature Infants

2000-10-01

Miles Tsuji , J. Philip Saul , Adré du Plessis +4 more

Premature infants experience brain injury, ie, germinal matrix-intraventricular hemorrhage (GMH-IVH) and periventricular leukomalacia (PVL), in considerable part because of disturbances in cerebral blood flow (CBF). Because such infants are susceptible … Premature infants experience brain injury, ie, germinal matrix-intraventricular hemorrhage (GMH-IVH) and periventricular leukomalacia (PVL), in considerable part because of disturbances in cerebral blood flow (CBF). Because such infants are susceptible to major fluctuations in mean arterial blood pressure (MAP), impaired cerebrovascular autoregulation would increase the likelihood for the changes in CBF that could result in GMH-IVH and PVL. The objectives of this study were to determine whether a state of impaired cerebrovascular autoregulation could be identified reliably and conveniently at the bedside, the frequency of any such impairment, and the relation of the impairment to the subsequent occurrence of severe GMH-IVH and PVL.To monitor the cerebral circulation continuously and noninvasively, we used near-infrared spectroscopy (NIRS) to determine quantitative changes in cerebral concentrations of oxygenated hemoglobin (HbO(2)) and deoxygenated hemoglobin (Hb) from the first hours of life. Our previous experimental study showed a strong correlation between a measure of cerebral intravascular oxygenation (HbD), ie, HbD = HbO(2) - Hb, determined by NIRS, and volemic CBF, determined by radioactive microspheres. We studied 32 very low birth weight premature infants (gestational age: 23-31 weeks; birth weight: 605-1870 g) requiring mechanical ventilation, supplemental oxygen, and invasive blood pressure monitoring by NIRS from 1 to 3 days of age. MAP measured by arterial catheter pressure transducer and arterial oxygen saturation measured by pulse oximetry were recorded simultaneously. The relationship of MAP to HbD was quantitated by coherence analysis.Concordant changes (coherence scores >. 5) in HbD and MAP, consistent with impaired cerebrovascular autoregulation, were observed in 17 of the 32 infants (53%). Eight of the 17 infants (47%) developed severe GMH-IVH or PVL or both. Of the 15 infants with apparently intact autoregulation, ie, coherence scores <.5, only 2 (13%) developed severe ultrasonographic lesions. Thus, for the entire study population of 32 infants, 8 of the 10 with severe lesions exhibited coherence scores >.5.We conclude that NIRS can be used in a noninvasive manner at the bedside to identify premature infants with impaired cerebrovascular autoregulation, that this impairment is relatively common in such infants, and that the presence of this impairment is associated with a high likelihood of occurrence of severe GMH-IVH/PVL.

All-organic optoelectronic sensor for pulse oximetry

2014-12-10

Claire M. Lochner , Yasser Khan , Adrien Pierre +1 more

Smart Homes that Monitor Breathing and Heart Rate

2015-04-17

Fadel Adib , Hongzi Mao , Zachary Kabelac +2 more

The evolution of ubiquitous sensing technologies has led to intelligent environments that can monitor and react to our daily activities, such as adapting our heating and cooling systems, responding to … The evolution of ubiquitous sensing technologies has led to intelligent environments that can monitor and react to our daily activities, such as adapting our heating and cooling systems, responding to our gestures, and monitoring our elderly. In this paper, we ask whether it is possible for smart environments to monitor our vital signs remotely, without instrumenting our bodies. We introduce Vital-Radio, a wireless sensing technology that monitors breathing and heart rate without body contact. Vital-Radio exploits the fact that wireless signals are affected by motion in the environment, including chest movements due to inhaling and exhaling and skin vibrations due to heartbeats. We describe the operation of Vital-Radio and demonstrate through a user study that it can track users' breathing and heart rates with a median accuracy of 99%, even when users are 8~meters away from the device, or in a different room. Furthermore, it can monitor the vital signs of multiple people simultaneously. We envision that Vital-Radio can enable smart homes that monitor people's vital signs without body instrumentation, and actively contribute to their inhabitants' well-being.

View PDF Chat

Robust Pulse Rate From Chrominance-Based rPPG

2013-06-04

Gerard de Haan , Vincent Jeanne

Remote photoplethysmography (rPPG) enables contactless monitoring of the blood volume pulse using a regular camera. Recent research focused on improved motion robustness, but the proposed blind source separation techniques (BSS) … Remote photoplethysmography (rPPG) enables contactless monitoring of the blood volume pulse using a regular camera. Recent research focused on improved motion robustness, but the proposed blind source separation techniques (BSS) in RGB color space show limited success. We present an analysis of the motion problem, from which far superior chrominance-based methods emerge. For a population of 117 stationary subjects, we show our methods to perform in 92% good agreement ( ±1.96σ) with contact PPG, with RMSE and standard deviation both a factor of 2 better than BSS-based methods. In a fitness setting using a simple spectral peak detector, the obtained pulse-rate for modest motion (bike) improves from 79% to 98% correct, and for vigorous motion (stepping) from less than 11% to more than 48% correct. We expect the greatly improved robustness to considerably widen the application scope of the technology.

Pulse oximetry: Understanding its basic principles facilitates appreciation of its limitations

2013-03-13

Edward D. Chan , Michael M. Chan , Mallory M. Chan

A Review on Recent Advances in Doppler Radar Sensors for Noncontact Healthcare Monitoring

2013-04-18

Changzhi Li , Victor M. Lubecke , Olga Boric‐Lubecke +1 more

This paper reviews recent advances in biomedical and healthcare applications of Doppler radar that remotely detects heartbeat and respiration of a human subject. In the last decade, new front-end architectures, … This paper reviews recent advances in biomedical and healthcare applications of Doppler radar that remotely detects heartbeat and respiration of a human subject. In the last decade, new front-end architectures, baseband signal processing methods, and system-level integrations have been proposed by many researchers in this field to improve the detection accuracy and robustness. The advantages of noncontact detection have drawn interests in various applications, such as energy smart home, baby monitor, cardiopulmonary activity assessment, and tumor tracking. While many of the reported systems were bench-top prototypes for concept verification, several portable systems and integrated radar chips have been demonstrated. This paper reviews different architectures, baseband signal processing, and system implementations. Validations of this technology in a clinical environment will also be discussed.

Respiration rate monitoring methods: A review

2011-01-31

Farah Q. Al-Khalidi , Reza Saatchi , D Burke +2 more

Abstract Respiration rate is an important indicator of a person's health, and thus it is monitored when performing clinical evaluations. There are different approaches for respiration monitoring, but generally they … Abstract Respiration rate is an important indicator of a person's health, and thus it is monitored when performing clinical evaluations. There are different approaches for respiration monitoring, but generally they can be classed as contact or noncontact. For contact methods, the sensing device (or part of the instrument containing it) is attached to the subject's body. For noncontact approaches the monitoring is performed by an instrument that does not make any contact with the subject. In this article a review of respiration monitoring approaches (both contact and noncontact) is provided. Concerns related to the patient's recording comfort, recording hygiene, and the accuracy of respiration rate monitoring have resulted in the development of a number of noncontact respiration monitoring approaches. A description of thermal imaging based and vision based noncontact respiration monitoring approaches we are currently developing is provided. Pediatr. Pulmonol. 2011; 46:523–529. © 2011 Wiley‐Liss, Inc.

View PDF Chat

Photoplethysmography and its application in clinical physiological measurement

2007-02-20

John Allen

Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to … Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile ('AC') physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.

Wearable Sensors for Human Activity Monitoring: A Review

2014-12-04

Subhas Chandra Mukhopadhyay

An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring … An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano technologies, and miniaturization makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This paper reviews the latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges.

Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity

2013-04-23

Vincent T. van Hees , Lukas Gorzelniak , Emmanuel Dean‐Leon +7 more

Human body acceleration is often used as an indicator of daily physical activity in epidemiological research. Raw acceleration signals contain three basic components: movement, gravity, and noise. Separation of these … Human body acceleration is often used as an indicator of daily physical activity in epidemiological research. Raw acceleration signals contain three basic components: movement, gravity, and noise. Separation of these becomes increasingly difficult during rotational movements. We aimed to evaluate five different methods (metrics) of processing acceleration signals on their ability to remove the gravitational component of acceleration during standardised mechanical movements and the implications for human daily physical activity assessment.An industrial robot rotated accelerometers in the vertical plane. Radius, frequency, and angular range of motion were systematically varied. Three metrics (Euclidian norm minus one [ENMO], Euclidian norm of the high-pass filtered signals [HFEN], and HFEN plus Euclidean norm of low-pass filtered signals minus 1 g [HFEN+]) were derived for each experimental condition and compared against the reference acceleration (forward kinematics) of the robot arm. We then compared metrics derived from human acceleration signals from the wrist and hip in 97 adults (22-65 yr), and wrist in 63 women (20-35 yr) in whom daily activity-related energy expenditure (PAEE) was available.In the robot experiment, HFEN+ had lowest error during (vertical plane) rotations at an oscillating frequency higher than the filter cut-off frequency while for lower frequencies ENMO performed better. In the human experiments, metrics HFEN and ENMO on hip were most discrepant (within- and between-individual explained variance of 0.90 and 0.46, respectively). ENMO, HFEN and HFEN+ explained 34%, 30% and 36% of the variance in daily PAEE, respectively, compared to 26% for a metric which did not attempt to remove the gravitational component (metric EN).In conclusion, none of the metrics as evaluated systematically outperformed all other metrics across a wide range of standardised kinematic conditions. However, choice of metric explains different degrees of variance in daily human physical activity.

View PDF Chat

Anaerobic threshold and respiratory gas exchange during exercise.

1973-08-01

Karlman Wasserman , Brian J. Whipp , S N Koyl +1 more

Range Correlation and&lt;tex&gt;$ I/ Q$&lt;/tex&gt;Performance Benefits in Single-Chip Silicon Doppler Radars for Noncontact Cardiopulmonary Monitoring

2004-03-01

Amy D. Droitcour , Olga Boric‐Lubecke , Victor M. Lubecke +2 more

Direct-conversion microwave Doppler-radar transceivers have been fully integrated in 0.25-/spl mu/m silicon CMOS and BiCMOS technologies. These chips, operating at 1.6 and 2.4 GHz, have detected movement due to heartbeat … Direct-conversion microwave Doppler-radar transceivers have been fully integrated in 0.25-/spl mu/m silicon CMOS and BiCMOS technologies. These chips, operating at 1.6 and 2.4 GHz, have detected movement due to heartbeat and respiration 50 cm from the subject, which may be useful in infant and adult apnea monitoring. The range-correlation effect on residual phase noise is a critical factor when detecting small phase fluctuations with a high-phase-noise on-chip oscillator. Phase-noise reduction due to range correlation was experimentally evaluated, and the measured residual phase noise was within 5 dB of predicted values on average. In a direct-conversion receiver, the phase relationship between the received signal and the local oscillator has a significant effect on the demodulation sensitivity, and the null points can be avoided with a quadrature (I/Q) receiver. In this paper, measurements that highlight the performance benefits of an I/Q receiver are presented. While the accuracy of the heart rate measured with the single-channel chip ranges from 40% to 100%, depending on positioning, the quadrature chip accuracy is always better than 80%.

Arctangent Demodulation With DC Offset Compensation in Quadrature Doppler Radar Receiver Systems

2007-05-01

Byung-Kwon Park , Olga Boric-Lubecke , Victor M. Lubecke

Direct-conversion microwave Doppler radar can be used to detect cardiopulmonary activity at a distance. One challenge for such detection in single channel receivers is demodulation sensitivity to target position, which … Direct-conversion microwave Doppler radar can be used to detect cardiopulmonary activity at a distance. One challenge for such detection in single channel receivers is demodulation sensitivity to target position, which can be overcome by using a quadrature receiver. This paper presents a mathematical analysis and experimental results demonstrating the effectiveness of arctangent demodulation in quadrature receivers. A particular challenge in this technique is the presence of dc offset resulting from receiver imperfections and clutter reflections, in addition to dc information related to target position and associated phase. These dc components can be large compared to the ac motion-related signal, and thus, cannot simply be included in digitization without adversely affecting resolution. Presented here is a method for calibrating the dc offset while preserving the dc information and capturing the motion-related signal with maximum resolution. Experimental results demonstrate that arctangent demodulation with dc offset compensation results in a significant improvement in heart rate measurement accuracy over quadrature channel selection, with a standard deviation of less than 1 beat/min

Noninvasive Pulse Wave Analysis for the Early Detection of Vascular Disease

1995-09-01

Jay N. Cohn , Stanley M. Finkelstein , Gary E. McVeigh +4 more

A noninvasive technique has been developed and validated for calculating capacitive and oscillatory systemic arterial compliance with the use of pulse wave analysis and a modified Windkessel model. Application of … A noninvasive technique has been developed and validated for calculating capacitive and oscillatory systemic arterial compliance with the use of pulse wave analysis and a modified Windkessel model. Application of the technique to subjects with hypertension, postmenopausal women with symptomatic coronary artery disease, and appropriate control subjects has confirmed a reduction of oscillatory compliance in the disease states and an increase in capacitive and oscillatory compliances in response to vasodilator drugs. This method should be useful in screening subjects for early evidence of vascular disease and in monitoring the response to therapy.

A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity

1997-03-01

Carlijn V. C. Bouten , K.T.M. Koekkoek , M. Verduin +2 more

The present study describes the development of a triaxial accelerometer (TA) and a portable data processing unit for the assessment of daily physical activity. The TA is composed of three … The present study describes the development of a triaxial accelerometer (TA) and a portable data processing unit for the assessment of daily physical activity. The TA is composed of three orthogonally mounted uniaxial piezoresistive accelerometers and can be used to register accelerations covering the amplitude and frequency ranges of human body acceleration. Interinstrument and test-retest experiments showed that the offset and the sensitivity of the TA were equal for each measurement direction and remained constant on two measurement days. Transverse sensitivity was significantly different for each measurement direction, but did not influence accelerometer output (<3% of the sensitivity along the main axis). The data unit enables the on-line processing of accelerometer output to a reliable estimator of physical activity over eight-day periods. Preliminary evaluation of the system in 13 male subjects during standardized activities in the laboratory demonstrated a significant relationship between accelerometer output and energy expenditure due to physical activity, the standard reference for physical activity (r=0.89). Shortcomings of the system are its low sensitivity to sedentary activities and the inability to register static exercise. The validity of the system for the assessment of normal daily physical activity and specific activities outside the laboratory should be studied in free-living subjects.

View PDF Chat

A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data

2008-11-06

Stephen Preece , John Y. Goulermas , Laurence Kenney +1 more

Driven by the demands on healthcare resulting from the shift toward more sedentary lifestyles, considerable effort has been devoted to the monitoring and classification of human activity. In previous studies, … Driven by the demands on healthcare resulting from the shift toward more sedentary lifestyles, considerable effort has been devoted to the monitoring and classification of human activity. In previous studies, various classification schemes and feature extraction methods have been used to identify different activities from a range of different datasets. In this paper, we present a comparison of 14 methods to extract classification features from accelerometer signals. These are based on the wavelet transform and other well-known time- and frequency-domain signal characteristics. To allow an objective comparison between the different features, we used two datasets of activities collected from 20 subjects. The first set comprised three commonly used activities, namely, level walking, stair ascent, and stair descent, and the second a total of eight activities. Furthermore, we compared the classification accuracy for each feature set across different combinations of three different accelerometer placements. The classification analysis has been performed with robust subject-based cross-validation methods using a nearest-neighbor classifier. The findings show that, although the wavelet transform approach can be used to characterize nonstationary signals, it does not perform as accurately as frequency-based features when classifying dynamic activities performed by healthy subjects. Overall, the best feature sets achieved over 95% intersubject classification accuracy.

View PDF Chat

A Wearable Health Care System Based on Knitted Integrated Sensors

2005-09-01

R. Paradiso , G. Loriga , N. Taccini

A comfortable health monitoring system named WEALTHY is presented. The system is based on a textile wearable interface implemented by integrating sensors, electrodes, and connections in fabric form, advanced signal … A comfortable health monitoring system named WEALTHY is presented. The system is based on a textile wearable interface implemented by integrating sensors, electrodes, and connections in fabric form, advanced signal processing techniques, and modern telecommunication systems. Sensors, electrodes and connections are realized with conductive and piezoresistive yarns. The sensorized knitted fabric is produced in a one step process. The purpose of this paper is to show the feasibility of a system based on fabric sensing elements. The capability of this system to acquire simultaneously several biomedical signals (i.e. electrocardiogram, respiration, activity) has been investigated and compared with a standard monitoring system. Furthermore, the paper presents two different methodologies for the acquisition of the respiratory signal with textile sensors. Results show that the information contained in the signals obtained by the integrated systems is comparable with that obtained by standard sensors. The proposed system is designed to monitor individuals affected by cardiovascular diseases, in particular during the rehabilitation phase. The system can also help professional workers who are subject to considerable physical and psychological stress and/or environmental and professional health risks.

Ballistocardiography and Seismocardiography: A Review of Recent Advances

2014-10-07

Omer T. Inan , Pierre‐François Migeotte , Kwangsuk Park +7 more

In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel … In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings, in the home, in the field, and even in microgravity. Customized signal processing algorithms have led to reduced measurement noise, clinically relevant feature extraction, and signal modeling. Finally, human subjects physiology studies have been conducted using these novel instruments and signal processing tools with promising results. This paper reviews the recent advances in these areas of modern BCG and SCG research.

View PDF Chat

Evaluation of Pulse Oximetry

1983-10-01

Mark Yelderman , William New

View PDF Chat

Detection of static and dynamic activities using uniaxial accelerometers

1996-12-01

Petrus H. Veltink , HansB.J. Bussmann , Wiebe de Vries +2 more

Rehabilitation treatment may be improved by objective analysis of activities of daily living. For this reason, the feasibility of distinguishing several static and dynamic activities (standing, sitting, lying, walking, ascending … Rehabilitation treatment may be improved by objective analysis of activities of daily living. For this reason, the feasibility of distinguishing several static and dynamic activities (standing, sitting, lying, walking, ascending stairs, descending stairs, cycling) using a small set of two or three uniaxial accelerometers mounted on the body was investigated. The accelerometer signals can be measured with a portable data acquisition system, which potentially makes it possible to perform online detection of static and dynamic activities in the home environment. However, the procedures described in this paper have yet to be evaluated in the home environment. Experiments were conducted on ten healthy subjects, with accelerometers mounted on several positions and orientations on the body, performing static and dynamic activities according to a fixed protocol. Specifically, accelerometers on the sternum and thigh were evaluated. These accelerometers were oriented in the sagittal plane, perpendicular to the long axis of the segment (tangential), or along this axis (radial). First, discrimination between the static or dynamic character of activities was investigated. This appeared to be feasible using an rms-detector applied on the signal of one sensor tangentially mounted on the thigh. Second, the distinction between static activities was investigated. Standing, sitting, lying supine, on a side and prone could be distinguished by observing the static signals of two accelerometers, one mounted tangentially on the thigh, and the second mounted radially on the sternum. Third, the distinction between the cyclical dynamic activities walking, stair ascent, stair descent and cycling was investigated. The discriminating potentials of several features of the accelerometer signals were assessed: the mean value, the standard deviation, the cycle time and the morphology. Signal morphology was expressed by the maximal cross-correlation coefficients with template signals for the different dynamic activities. The mean signal values and signal morphology of accelerometers mounted tangentially on the thigh and the sternum appeared to contribute to the discrimination of dynamic activities with varying detection performances. The standard deviation of the signal and the cycle time were primarily related to the speed of the dynamic activities, and did not contribute to the discrimination of the activities. Therefore, discrimination of dynamic activities on the basis of the combined evaluation of the mean signal value and signal morphology is proposed.

View PDF Chat

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

2015-09-15

Yu Sun , Nitish V. Thakor

Photoplethysmography (PPG) is a noninvasive optical technique for detecting microvascular blood volume changes in tissues. Its ease of use, low cost and convenience make it an attractive area of research … Photoplethysmography (PPG) is a noninvasive optical technique for detecting microvascular blood volume changes in tissues. Its ease of use, low cost and convenience make it an attractive area of research in the biomedical and clinical communities. Nevertheless, its single spot monitoring and the need to apply a PPG sensor directly to the skin limit its practicality in situations such as perfusion mapping and healing assessments or when free movement is required. The introduction of fast digital cameras into clinical imaging monitoring and diagnosis systems, the desire to reduce the physical restrictions, and the possible new insights that might come from perfusion imaging and mapping inspired the evolution of the conventional PPG technology to imaging PPG (IPPG). IPPG is a noncontact method that can detect heart-generated pulse waves by means of peripheral blood perfusion measurements. Since its inception, IPPG has attracted significant public interest and provided opportunities to improve personal healthcare. This study presents an overview of the wide range of IPPG systems currently being introduced along with examples of their application in various physiological assessments. We believe that the widespread acceptance of IPPG is happening, and it will dramatically accelerate the promotion of this healthcare model in the near future.

View PDF Chat

Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice

2015-06-05

Ramakrishna Mukkamala , Jin‐Oh Hahn , Omer T. Inan +4 more

Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time … Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.

View PDF Chat

Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring

2016-06-14

Mohammad Kachuee , Mohammad Mahdi Kiani , Hoda Mohammadzade +1 more

Continuous blood pressure (BP) monitoring can provide invaluable information about individuals' health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This paper presents … Continuous blood pressure (BP) monitoring can provide invaluable information about individuals' health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This paper presents an efficient algorithm, based on the pulse arrival time (PAT), for the continuous and cuffless estimation of the systolic BP, diastolic blood pressure (DBP), and mean arterial pressure (MAP) values.The proposed framework estimates the BP values through processing vital signals and extracting two types of features, which are based on either physiological parameters or whole-based representation of vital signals. Finally, the regression algorithms are employed for the BP estimation. Although the proposed algorithm works reliably without any need for calibration, an optional calibration procedure is also suggested, which can improve the system's accuracy even further.The proposed method is evaluated on about a thousand subjects using the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) standards. The method complies with the AAMI standard in the estimation of DBP and MAP values. Regarding the BHS protocol, the results achieve grade A for the estimation of DBP and grade B for the estimation of MAP.We conclude that by using the PAT in combination with informative features from the vital signals, the BP can be accurately and reliably estimated in a noninvasive fashion.The results indicate that the proposed algorithm for the cuffless estimation of the BP can potentially enable mobile health-care gadgets to monitor the BP continuously.

Algorithmic Principles of Remote PPG

2016-09-13

Wenjin Wang , A.C. den Brinker , Sander Stuijk +1 more

This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the algorithmic principles behind remote … This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the algorithmic principles behind remote photoplethysmography (rPPG). The model is used to explain the different choices that were made in existing rPPG methods for pulse extraction. The understanding that comes from the model can be used to design robust or application-specific rPPG solutions. We illustrate this by designing an alternative rPPG method, where a projection plane orthogonal to the skin tone is used for pulse extraction. A large benchmark on the various discussed rPPG methods shows that their relative merits can indeed be understood from the proposed model.

View PDF Chat

Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort

2017-05-24

Anna Shcherbina , C. Mikael Mattsson , Daryl Waggott +5 more

The ability to measure physical activity through wrist-worn devices provides an opportunity for cardiovascular medicine. However, the accuracy of commercial devices is largely unknown. The aim of this work is … The ability to measure physical activity through wrist-worn devices provides an opportunity for cardiovascular medicine. However, the accuracy of commercial devices is largely unknown. The aim of this work is to assess the accuracy of seven commercially available wrist-worn devices in estimating heart rate (HR) and energy expenditure (EE) and to propose a wearable sensor evaluation framework. We evaluated the Apple Watch, Basis Peak, Fitbit Surge, Microsoft Band, Mio Alpha 2, PulseOn, and Samsung Gear S2. Participants wore devices while being simultaneously assessed with continuous telemetry and indirect calorimetry while sitting, walking, running, and cycling. Sixty volunteers (29 male, 31 female, age 38 ± 11 years) of diverse age, height, weight, skin tone, and fitness level were selected. Error in HR and EE was computed for each subject/device/activity combination. Devices reported the lowest error for cycling and the highest for walking. Device error was higher for males, greater body mass index, darker skin tone, and walking. Six of the devices achieved a median error for HR below 5% during cycling. No device achieved an error in EE below 20 percent. The Apple Watch achieved the lowest overall error in both HR and EE, while the Samsung Gear S2 reported the highest. In conclusion, most wrist-worn devices adequately measure HR in laboratory-based activities, but poorly estimate EE, suggesting caution in the use of EE measurements as part of health improvement programs. We propose reference standards for the validation of consumer health devices (http://precision.stanford.edu/).

View PDF Chat

Remote patient monitoring: a comprehensive study

2017-10-26

Lakmini Malasinghe , Naeem Ramzan , Keshav Dahal

Healthcare is a field that is rapidly developing in technology and services. A recent development in this area is remote monitoring of patients which has many advantages in a fast … Healthcare is a field that is rapidly developing in technology and services. A recent development in this area is remote monitoring of patients which has many advantages in a fast aging world population with increasing health complications. With relatively simple applications to monitor patients inside hospital rooms, the technology has developed to the extent that the patient can be allowed normal daily activities at home while still being monitored with the use of modern communication and sensor technologies. Sensors for monitoring essential vital signs such as electrocardiogram reading, heart rate, respiration rate, blood pressure, temperature, blood glucose levels and neural system activity are available today. Range of remote healthcare varies from monitoring chronically ill patients, elders, premature children to victims of accidents. These new technologies can monitor patients based on the illness or based on the situation. The technology varies from sensors attached to body to ambient sensors attached to the environment and new breakthroughs show contactless monitoring which requires only the patient to be present within a few meters from the sensor. Fall detection systems and applications to monitor chronical ill patients have already become familiar to many. This study provides a review of the recent advances in remote healthcare and monitoring in both with-contact and contactless methods. With the review, the authors discuss some issues available in most systems. The paper also includes some directions for future research.

View PDF Chat

Wearable Health Devices—Vital Sign Monitoring, Systems and Technologies

2018-07-25

Duarte Dias , João Paulo Silva Cunha

Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data … Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.

View PDF Chat

Monitoring of the central blood pressure waveform via a conformal ultrasonic device

2018-09-04

Chonghe Wang , Xiaoshi Li , Hongjie Hu +7 more

Wearables and the Medical Revolution

2018-09-01

Jessilyn Dunn , Ryan Runge , M Snyder

Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and … Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.

View PDF Chat

A review on wearable photoplethysmography sensors and their potential future applications in health care

2018-01-01

Mohammad Ghamari

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes.PPG is a non-invasive technology that uses a light source and a … Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes.PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation.Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings.PPG signal's second derivative wave contains important health-related information.Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness.Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life.For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications.The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.

View PDF Chat

The use of photoplethysmography for assessing hypertension

2019-06-26

Mohamed Elgendi , R. Fletcher , Yongbo Liang +5 more

Abstract The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is … Abstract The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.

View PDF Chat

Review on Psychological Stress Detection Using Biosignals

2019-07-09

Giorgos Giannakakis , Dimitris Grigoriadis , Katerina Giannakaki +3 more

This review investigates the effects of psychological stress on the human body measured through biosignals. When a potentially threatening stimulus is perceived, a cascade of physiological processes occurs mobilizing the … This review investigates the effects of psychological stress on the human body measured through biosignals. When a potentially threatening stimulus is perceived, a cascade of physiological processes occurs mobilizing the body and nervous system to confront the imminent threat and ensure effective adaptation. Biosignals that can be measured reliably in relation to such stressors include physiological (EEG, ECG, EDA, EMG) and physical measures (respiratory rate, speech, skin temperature, pupil size, eye activity). A fundamental objective in this area of psychophysiological research is to establish reliable biosignal indices that reveal the underlying physiological mechanisms of the stress response. Motivated by the lack of comprehensive guidelines on the relationship between the multitude of biosignal features used in the literature and their corresponding behaviour during stress, in this paper, the impact of stress to multiple bodily responses is surveyed. Emphasis is put on the efficiency, robustness and consistency of biosignal data features across the current state of knowledge in stress detection. It is also explored multimodal biosignal analysis and modelling methods for deriving accurate stress correlates. This paper aims to provide a comprehensive review on biosignal patterns caused during stress conditions and reliable practical guidelines towards more efficient detection of stress.

DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks

2018-01-01

Weixuan Chen , Daniel McDuff

View PDF Chat

Investigating sources of inaccuracy in wearable optical heart rate sensors

2020-02-10

Brinnae Bent , Benjamin A. Goldstein , Warren A. Kibbe +1 more

As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact … As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. Accuracy of wearable technologies has been a hotly debated topic in both the research and popular science literature. Currently, wearable technology companies are responsible for assessing and reporting the accuracy of their products, but little information about the evaluation method is made publicly available. Heart rate measurements from wearables are derived from photoplethysmography (PPG), an optical method for measuring changes in blood volume under the skin. Potential inaccuracies in PPG stem from three major areas, includes (1) diverse skin types, (2) motion artifacts, and (3) signal crossover. To date, no study has systematically explored the accuracy of wearables across the full range of skin tones. Here, we explored heart rate and PPG data from consumer- and research-grade wearables under multiple circumstances to test whether and to what extent these inaccuracies exist. We saw no statistically significant difference in accuracy across skin tones, but we saw significant differences between devices, and between activity types, notably, that absolute error during activity was, on average, 30% higher than during rest. Our conclusions indicate that different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity. This has implications for researchers, clinicians, and consumers in drawing study conclusions, combining study results, and making health-related decisions using these devices.

View PDF Chat

SpO2 Prediction Using Machine Learning Approach by Comparing Habitual Activity and Sleep Stages with Improved Accuracy for Walk Inside Dwelling Environment

2025-06-25

K. Krishnakumar , S. Latha | Sensing and Imaging

Photoplethysmography Feature Extraction for Non-Invasive Glucose Estimation by Means of MFCC and Machine Learning Techniques

2025-06-24

Christian Salamea , Melissa Montalvo , Raquel Orellana-Peralta +1 more | Biosensors

Diabetes Mellitus is considered one of the most widespread diseases in the world. Traditional glucose monitoring devices carry discomfort and risks associated with the frequent extraction of blood from users. … Diabetes Mellitus is considered one of the most widespread diseases in the world. Traditional glucose monitoring devices carry discomfort and risks associated with the frequent extraction of blood from users. The present article proposes a noninvasive glucose estimation system based on the application of Mel Frequency Cepstral Coefficients (MFCCs) for the characterization of photoplethysmographic signals (PPG). Two variants of the MFCC feature extraction methods are evaluated along with three machine learning techniques for the development of an effective regression function for the estimation of glucose concentration. A comparison between the performance of the algorithms revealed that the best combination achieved a mean absolute error of 9.85 mg/dL and a correlation of 0.94 between the estimated concentration and the real glucose values. Similarly, 99.53% of the validation samples were distributed within zones A and B of the Clarke Error Grid Analysis. The proposed system achieves levels of correlation comparable to analogous technologies that require earlier calibration for its operation, which indicates a strong potential for the future use of the algorithm as an alternative to invasive monitoring devices.

A Wearable Dual-Modal Patch for Rapid Pre-Hospital Diagnosis of Acute Myocardial Infarction

2025-06-24

Kuo Yang , Qianqian Dong , Zhibing Yao +6 more | ACS Nano

Timely diagnosis of acute myocardial infarction (AMI) during the prehospital phase is crucial to decrease mortality rates. Given that certain patients may not exhibit typical alterations in their electrocardiogram (ECG) … Timely diagnosis of acute myocardial infarction (AMI) during the prehospital phase is crucial to decrease mortality rates. Given that certain patients may not exhibit typical alterations in their electrocardiogram (ECG) patterns during the initial phases, the diagnosis of AMI is typically achieved by simultaneously assessing ECG results and myocardial injury biomarkers. This procedure requires the use of specialized equipment and trained personnel that are only available in hospitals, which may lead to possible delays of several hours. The development of a device that can detect both ECG and acute myocardial injury markers in the prehospital setting remains a significant challenge. In this study, a wearable dual-modal patch that combines a surface-enhanced Raman scattering (SERS) microneedle array with flexible electronics is introduced for the prehospital diagnosis of AMI. The patch allows for the noninvasive and rapid monitoring of both ECG and the levels of three myocardial injury markers in the interstitial fluid (ISF) by a portable Raman spectrometer, in accordance with the established clinical standard. This strategy was validated through experiments conducted on rats induced with AMI. The time required for diagnosing ischemia was significantly reduced to 50 min after its onset. The patch is optimally integrated into a stamp-sized band-aid, accompanied by a smartphone app for data visualization and real-time analysis. This initiative aims to facilitate the prompt delivery of interventions to reduce ischemic events.

The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis

2025-06-23

Hatice Kabaoğlu , Fecir Duran , Emine Uçar | Sakarya University Journal of Computer and Information Sciences

This study focuses on developing a digital twin for baby incubators in neonatal intensive care units to enhance monitoring and care for premature infants. The digital twin employs a hybrid … This study focuses on developing a digital twin for baby incubators in neonatal intensive care units to enhance monitoring and care for premature infants. The digital twin employs a hybrid model integrating Long Short-Term Memory (LSTM) and Random Forest (RF) algorithms to predict potential errors and alarms. The LSTM algorithm was trained using sensor data provided by a health technology company to predict future measurements. Subsequently, the RF algorithm classifies these predictions into specific error conditions. The hybrid model demonstrates success with mean squared error and mean absolute error values of 1540533.6 and 160.8 for the LSTM model and an 86.44% accuracy rate for the RF model. The study's key findings emphasize the effectiveness of the hybrid model in predicting future sensor values and classifying errors, representing a significant step towards improving premature baby care. Integrating LSTM and RF algorithms offers an innovative approach to error prediction, minimizing risks and improving premature infant health outcomes. In summary, this study successfully develops a digital twin for baby incubators, offering a promising solution for advancing newborn healthcare services and providing a foundation for future research.

Bladder Dysfunction Diagnosis Using Surface EMG: A Novel Approach with Convolution-Based Signal Processing and Bladder Vector Hypothesis

2025-06-23

Prasun Adroja | INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

Bladder dysfunctions, including overactive bladder (OAB), detrusor underactivity (DU), and neurogenic bladder disorders, require precise diagnostic methods for effective treatment. Traditional urodynamic studies (UDS) rely on invasive catheterization to measure … Bladder dysfunctions, including overactive bladder (OAB), detrusor underactivity (DU), and neurogenic bladder disorders, require precise diagnostic methods for effective treatment. Traditional urodynamic studies (UDS) rely on invasive catheterization to measure bladder pressure, which is uncomfortable and unsuitable for long-term monitoring. This study proposes surface electromyography (sEMG) as a non-invasive alternative to assess bladder function by recording detrusor muscle activity from the lower abdomen and pelvic floor. To enhance signal quality, adaptive filtering techniques and convolution-based signal processing are employed, effectively reducing noise and artifacts. Additionally, a novel Bladder Vector Hypothesis is introduced, inspired by ECG vector recording, to analyze multi-electrode spatial contraction patterns for improved diagnostics. This approach has the potential to enable non-invasive, real-time urodynamic assessments, revolutionizing the diagnosis of bladder dysfunctions. Key words: Electromyography (EMG), bladder dysfunction, convolution filtering, non-invasive diagnostics, bladder vector hypothesis.

UR-Sense: Ubiquitous respiration sensing with UWB radar

2025-06-21

Ju‐Hee Kim , Seungku Kim | Computers in Biology and Medicine

Traditional respiration measurement methods require attaching devices or sensors to the subject's body, which can cause discomfort, skin irritation, and the risk of infection. Contactless respiration measurement methods overcome these … Traditional respiration measurement methods require attaching devices or sensors to the subject's body, which can cause discomfort, skin irritation, and the risk of infection. Contactless respiration measurement methods overcome these issues, enabling more efficient and convenient health monitoring. However, most existing contactless respiration monitoring studies assume that subjects remain stationary in a fixed position. This paper introduces UR-Sense, a system that continuously finds a subject's location in daily life without restricting their movements or activities. Experimental results demonstrate that UR-Sense achieves an average respiration estimation accuracy of over 92 % in everyday scenarios, regardless of the subject's mobility and movements.

Automated Digit Recognition and Measurement-Type Classification from Blood Pressure Monitor Images Using Deep Learning

2025-06-20

Nur Ahmadi , Hutomo Tanoto , Rinaldi Munir | Algorithms

Blood pressure is a vital indicator of cardiovascular health and plays a crucial role in the early detection and management of heart-related diseases. However, current practices for recording blood pressure … Blood pressure is a vital indicator of cardiovascular health and plays a crucial role in the early detection and management of heart-related diseases. However, current practices for recording blood pressure readings are still largely manual, leading to inefficiencies and data inconsistencies. To address this challenge, we propose a deep learning-based method for automated digit recognition and measurement-type classification (systolic, diastolic, and pulse) from images of blood pressure monitors. A total of 2147 images were collected and expanded to 3649 images using data augmentation techniques. We developed and trained three YOLOv8 variants (small, medium, and large). Post-training quantization (PTQ) was employed to optimize the models for edge deployment in a mobile health (mHealth) application. The quantized INT8 YOLOv8-small (YOLOv8s) model emerged as the optimal model based on the trade-off between accuracy, inference time, and model size. The proposed model outperformed existing approaches, including the RT-DETR (Real-Time DEtection TRansformer) model, achieving 99.28% accuracy, 96.48% F1-score, 641.40 ms inference time, and a compact model size of 11 MB. The model was successfully integrated into the mHealth application, enabling accurate, fast, and automated blood pressure tracking. This end-to-end solution provides a scalable and practical approach for enhancing blood pressure monitoring via an accessible digital platform.

A comprehensive review of heart rate measurement using remote photoplethysmography and deep learning

2025-06-20

Uday Debnath , Sungho Kim | BioMedical Engineering OnLine

With the widespread availability of consumer-grade cameras, interest in heart rate (HR) measurement using remote photoplethysmography (rPPG) has grown significantly. rPPG is a noninvasive optical technique that uses camera to … With the widespread availability of consumer-grade cameras, interest in heart rate (HR) measurement using remote photoplethysmography (rPPG) has grown significantly. rPPG is a noninvasive optical technique that uses camera to measure heart rate by analyzing light reflectance due to blood flow changes beneath the skin from any parts of the body, mostly facial regions. However, it faces challenges such as motion artifacts and sensitivity to varying lighting conditions. The rapid advancement of deep learning techniques in recent years has driven numerous studies to integrate these models with rPPG for HR detection in remote health monitoring systems. This study provides a comprehensive review of both conventional approaches and recent developments in rPPG and deep learning algorithms. A comparative analysis highlighted the superior accuracy of deep learning methods over conventional techniques in non-contact HR estimation. Based on a review of 145 articles encompassing different methodologies, signal processing strategies, and deep learning algorithms, our study identifies existing research gaps and explores future research opportunities for real-world applications.

FMCW radar-based heartbeat recognition using SE-DenseNet for vital signs monitoring

2025-06-20

Long Jin , Jiamin Pu , Hongjian Li | AEU - International Journal of Electronics and Communications

An Analysis of Arterial Pulse Wave Time Features and Pulse Wave Velocity Calculations Based on Radial Electrical Bioimpedance Waveforms in Patients Scheduled for Coronary Catheterization

2025-06-20

Kristina Lotamõis , Tiina Uuetoa , Andrei Krivošei +6 more | Journal of Cardiovascular Development and Disease

The monitoring of peripheral electrical bioimpedance (EBI) variations is a promising method that has the potential to replace invasive or burdensome techniques for cardiovascular measurements. Segmental or continuous recording of … The monitoring of peripheral electrical bioimpedance (EBI) variations is a promising method that has the potential to replace invasive or burdensome techniques for cardiovascular measurements. Segmental or continuous recording of peripheral pulse waves can serve as a basis for calculating prognostic markers like pulse wave velocity (PWV) or include parameters such as pulse transit time (PTT) or pulse arrival time (PAT) for noninvasive blood pressure (BP) estimation, as well as potentially novel cardiovascular risk indicators. However, several technical, analytical, and interpretative aspects need to be resolved before the EBI method can be adopted in clinical practice. Our goal was to investigate and improve the application of EBI, executing its comparison with other cardiovascular assessment methods in patients hospitalized for coronary catheterization procedures. Methods: We analyzed data from 44 non-acute patients aged 45–74 years who were hospitalized for coronary catheterization at East Tallinn Central Hospital between 2020 and 2021. The radial EBI and electrocardiogram (ECG) were measured simultaneously with central and contralateral pressure curves. The Savitzky–Golay filter was used for signal smoothing. The Hankel matrix decomposer was applied for the extraction of cardiac waveforms from multi-component signals. After extracting the cardiac component, a period detection algorithm was applied to EBI and blood pressure curves. Results: Seven points of interest were detected on the pressure and EBI curves, and four with good representativeness were selected for further analysis. The Spearman correlation coefficient was low for all but the central and distal pressure curve systolic upstroke time points. A high positive correlation was found between PWV measured both invasively and with EBI. The median value of complimentary pulse wave velocity (CPWV), a parameter proposed in the paper, was significantly lower in patients with normal coronaries compared to patients with any stage of coronary disease. Conclusions: With regard to wearable devices, the EBI-derived PAT can serve as a substrate for PWV calculations and cardiovascular risk assessment, although these data require further confirmation.

Research on nighttime IPPG algorithm based on ROI delay expansion and fundamental frequency constrained FastICA

2025-06-19

Jiang Wu , Jian Qiu , Li Peng +4 more | Physiological Measurement

Objective.This study aims to enhance the accuracy and reliability of imaging photoplethysmography (IPPG) for heart rate (HR) measurements during nighttime by introducing an innovative approach that combines fast independent component … Objective.This study aims to enhance the accuracy and reliability of imaging photoplethysmography (IPPG) for heart rate (HR) measurements during nighttime by introducing an innovative approach that combines fast independent component analysis (FastICA) with aTime-DelayedMulti-DimensionalExtendedRegionsofInterestExtraction (TDMDE-ROI-Ex) technique, specifically tailored to overcome the challenges posed by motion artefacts and the difficulty in identifying regions of interest (ROIs).Approach.This research employs a dual-method strategy for the precise extraction of ROIs and robust processing of HR signals in nighttime IPPG scenarios. Initially, a face detection algorithm is integrated with a grayscale clustering technique to pinpoint optimal ROIs. This is followed by the application of the mutual information delay method to synthesize multi-channel IPPG signals. Concurrently, theHR'sFundamentalFrequency is leveraged as a priorConstraint within the iterative process ofFastICA(HRFFC-FastICA), mitigating the susceptibility to initial value fluctuations inherent in FastICA. The synergistic application of these methodologies substantially bolsters the stability and robustness of nighttime HR measurements, particularly in conditions characterized by significant motion.Main results.The efficacy of the proposed method, which incorporates HRFFC-FastICA, is initially validated through performance testing using the MR-NIRP dataset. This step serves to assess the practicality of the approach for nighttime IPPG HR measurements. Following this, a series of modular ablation studies and comparative evaluations against current nighttime IPPG algorithms are executed. The statistical outcomes demonstrate that our method achieves a mean absolute error (MAE) of 4.57 beats per minute (bpm) and a root mean squared error (RMSE) of 5.95 bpm. In direct comparison with prominent algorithms such as SparsePPG and PhysNet, the method exhibits a notable enhancement in MAE by up to 8.39 bpm and a significant decrease in RMSE by 17.83 bpm. The 95% confidence interval of the Bland-Altman graph of this method is between 9.5 and -12.8 bpm. Compared to other comparable methods, this interval is significantly narrower, with a width nearly half that of alternative approaches, indicating superior precision and reliability.Significance.The significance of this research is highlighted by the experimental outcomes that demonstrate the considerable advantages of the TDMDE-ROI-Ex method. This technique significantly reduces reliance on facial motion, which is crucial for accurately identifying facial skin colour regions of interest. Moreover, integrating the HRFFC-FastICA method effectively counteracts the effects of motion artefacts and the initial value sensitivity inherent in the FastICA process. The introduction of this methodology into nighttime IPPG monitoring significantly strengthens the system's robustness and stability, thereby extending the range of IPPG technology applications and improving its overall measurement performance.

HBE2F-CNN: Hybrid Bhattacharya and Euclidean Distance-Based Deep Learning Model for Heart Rate Estimation

2025-06-19

Minal Chandrakant Toley , Vishal Shirsath , Ajitkumar Pundge | Deleted Journal

A new algorithm for removing random motion noise of human body based on total variation

2025-06-19

Yunqing Liu , Jiaqi Shang , Wei-Ta Chu +5 more | Biomedical Signal Processing and Control

Heart rate informed detection of cardiac events using the Kalman filter

2025-06-19

Ömer Kılıç , Afra Nawar , Cem Okan Yaldiz +4 more | Computers in Biology and Medicine

Multi-datasets transfer multitask learning for simultaneous blood glucose and blood pressure monitoring using common PPG features

2025-06-18

Noor Faris Ali , Ibrahim M. Elfadel , Mohamed Atef | Computers in Biology and Medicine

The simultaneous monitoring of both blood glucose level (BGL) and blood pressure (BP) has rarely been studied directly. The exploitation of physiological interactions between them will advance the learning of … The simultaneous monitoring of both blood glucose level (BGL) and blood pressure (BP) has rarely been studied directly. The exploitation of physiological interactions between them will advance the learning of either task. However, the lack of available datasets with labels of both targets presents an obstacle. Therefore, in this paper, we propose three methods for multi-dataset (MD) learning. First, we extract PPG features from three datasets: a source dataset comprising diabetes mellitus (DM) and hypertension (HTN) classes labels and two target datasets for each BP and BGL. Subsequently, we select a common merged feature set for all datasets tasks. This study experiments with the three proposed multi-dataset methods: 1) transfer learning (MD-TL) to transfer knowledge from a source task-DM and HTN single-task classifier, to a target task, BGL and BP single-task regression, respectively, 2) multitask learning (MD-MTL) of both targets via a shared two-way TL, and 3) combined TL with MTL (MD-TL-MTL) to transfer knowledge from a source multitasking classifier to the target tasks in the MD-MTL network. The final proposed MD-TL-MTL achieves an MAE±SD of 2.53 ± 3.73 for SBP, 1.47 ± 1.84 for DBP, and MAE of 1.36 for BGL. Clarke error grid analysis shows that 99.86 % of samples fall into zone A. Overall, the MD-TL-MTL improves performance in all tasks compared to baseline models. An interpretability analysis using Shapley Additive Explanations (SHAP) and permutation importance is conducted to facilitate the clinical understanding behind predictions.

A Comprehensive Review of Unobtrusive Biosensing in Intelligent Vehicles: Sensors, Algorithms, and Integration Challenges

2025-06-18

Shiva Maleki Varnosfaderani , Muskan Shaikh , Mohamad Forouzanfar | Bioengineering

Unobtrusive in-vehicle measurement and the monitoring of physiological signals have recently attracted researchers in industry and academia as an innovative approach that can provide valuable information about drivers' health and … Unobtrusive in-vehicle measurement and the monitoring of physiological signals have recently attracted researchers in industry and academia as an innovative approach that can provide valuable information about drivers' health and status. The main goal is to reduce the number of traffic accidents caused by driver errors by monitoring various physiological parameters and devising appropriate actions to alert the driver or to take control of the vehicle. The research on this topic is in its early stages. While there have been several publications on this topic and industrial prototypes made by car manufacturers, a comprehensive and critical review of the current trends and future directions is missing. This review examines the current research and findings in in-vehicle physiological monitoring and suggests future directions and potential uses. Various physiological sensors, their potential locations, and the results they produce are demonstrated. The main challenges of in-vehicle biosensing, including unobtrusive sensing, vehicle vibration and driver movement cancellation, and privacy management, are discussed, and possible solutions are presented. The paper also reviews the current in-vehicle biosensing prototypes built by car manufacturers and other researchers. The reviewed methods and presented directions provide valuable insights into robust and accurate biosensing within vehicles for researchers in the field.

Validation of the EmotiBit wearable sensor for heart-based measures under varying workload conditions

2025-06-18

Anna Vorreuther , Nektaria Tagalidou , Mathias Vukelić | Frontiers in Neuroergonomics

Introduction The EmotiBit photoplethysmography (PPG) device allows user-owned data collection for measures of cardiovascular activity (CVA) and electrodermal activity (EDA) in naturalistic settings. The aim of this study was to … Introduction The EmotiBit photoplethysmography (PPG) device allows user-owned data collection for measures of cardiovascular activity (CVA) and electrodermal activity (EDA) in naturalistic settings. The aim of this study was to evaluate the validity of this device for collecting high-quality data while participants experience varying levels of cognitive workload. Methods Using a standardized criterion validity protocol, recordings of 15 participants performing a cognitive workload task were compared for the EmotiBit and a reference electrocardiography (ECG) device (BITalino PsychoBit). Multiple preprocessing pipelines and a signal quality check were implemented. Parameters of interest including heart rate (HR), heart rate variability (HRV) measures, skin conductance level (SCL), and skin conductance response (SCR) measures were assessed using Bland-Altman plot and ratio (BAr) analyses, as well as cross-correlations of the EDA signal time series of both devices. Results BAr results indicated good agreement between devices regarding HR with an average difference of 1–2 beats per minute (bpm). HRV measures yielded an insufficient BAr, albeit most data points lay within a priori boundaries of agreement. EDA measures yielded insufficient agreement for comparing SCL and SCR number and amplitude. Discussion The results are comparable to the validation of similar wearable PPG devices and extend the validation of the EmotiBit by assessing the acquired signals during varying levels of cognitive workload. While the device may be used to collect HR for scientific data analysis, its quality regarding HRV and EDA measures is not comparable to a standard ECG. Significance This study provides the first systematic validation following a standardized protocol of the EmotiBit PPG device relative to an ECG when considering recordings collected during cognitive workload induction.

Enhanced human motion recognition via PPG signals

2025-06-18

Wendong Zhao , Mengna Jia , Kaihua Liu +4 more | Signal Image and Video Processing

FPGA-based SqueezeNet transformer with multi-scale attention fusion for real-time respiration rate estimation using PPG signals

2025-06-18

Immaculate Joy Selvam , Srinivasan Chandran , C Saravanakumar | The Journal of Supercomputing

Continuous respiratory rate monitoring through temporal fusion of ECG and PPG signals

2025-06-17

Yonghong Lin , Xinyue Song , Yan Zhao +2 more | PLoS ONE

Respiratory rate (RR) is an important vital sign indicating various pathological conditions, such as clinical deterioration, pneumonia, and adverse cardiac arrest. Traditional RR measurement methods are normally intrusive and inconvenient … Respiratory rate (RR) is an important vital sign indicating various pathological conditions, such as clinical deterioration, pneumonia, and adverse cardiac arrest. Traditional RR measurement methods are normally intrusive and inconvenient for ubiquitous continuous monitoring. There have been studies on RR estimation by extracting respiratory modulated components (RMCs) from wearable accessible noninvasive cardiovascular signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG), with RR estimated from each RMC or fused RMCs derived from either ECG or PPG. However, there is few study on robust continuous RR estimation with the combination of all kinds of RMCs from both ECG and PPG in the time domain. In this study, we propose the temporal fusion of RMCs extracted from both ECG and PPG signals to estimate RR with the aim to improve estimation performance. We extracted six RMCs from ECG and PPG, identified those RMCs of high quality with the respiratory quality index, fused the identified ones into one respiratory signal with principal component analysis, and estimated the RR from the fused signal. Validation on two public datasets - the Capnobase dataset (42 subjects) and the BIDMC dataset (53 subjects) - showed that the proposed method attained a mean absolute error (MAE) of 1.39 breaths/min and 3.29 breaths/min for RR estimation, respectively, achieving an average 11.61% reduction in MAE compared to existing state-of-the-art approaches. This demonstrates that temporal fusion of the RMCs of wearable ECG and PPG can improve the performance of RR estimation.

A Pilot Study: Sleep and Activity Monitoring of Newborn Infants by GRU-Stack-Based Model Using Video Actigraphy and Pulse Rate Variability Features

2025-06-17

Ádám Nagy , Zita Lilla Róka , Imre Jánoki +5 more | Applied Sciences

We introduce a novel system for automatic assessment of newborn and preterm infant behavior—including activity levels, behavioral states, and sleep–wake cycles—in clinical settings for streamlining care and minimizing healthcare professionals’ … We introduce a novel system for automatic assessment of newborn and preterm infant behavior—including activity levels, behavioral states, and sleep–wake cycles—in clinical settings for streamlining care and minimizing healthcare professionals’ workload. While vital signs are routinely monitored, the previously mentioned assessments require labor-intensive direct observation. Research so far has already introduced non- and minimally invasive solutions. However, we developed a system that automatizes the preceding evaluations in a non-contact way using deep learning algorithms. In this work, we provide a Gated Recurrent Unit (GRU)-stack-based solution that works on a dynamic feature set generated by computer vision methods from the cameras’ video feed and patient monitor to classify the activity phases of infants adapted from the NIDCAP (Newborn Individualized Developmental Care Program) scale. We also show how pulse rate variability (PRV) data could improve the performance of the classification. The network was trained and evaluated on our own database of 108 h collected at the Neonatal Intensive Care Unit, Dept. of Neonatology of Pediatrics, Semmelweis University, Budapest, Hungary.

Stress detection through wearable EEG technology: A signal-based approach

2025-06-17

Rakesh Kumar , Dushyant Kumar Singh | Computers & Electrical Engineering

Temporal attention to estimate continuous blood pressure using photoplethysmography signals

2025-06-16

Sumbal Maqsood , Shuxiang Xu , Matthew Springer +3 more | Cluster Computing

iSleep: Continuous, binocular pupil tracking in sleep and reduced consciousness for physiological monitoring, predictions and interventions

2025-06-15

Özge Yüzgeç , Guillaume Legendre , Amirhossein Asadian +7 more | bioRxiv (Cold Spring Harbor Laboratory)

Monitoring pupil dynamics is a key tool in understanding arousal. Pupil size can serve as a biomarker for the autonomic nervous system balance as well as for identifying brain states. … Monitoring pupil dynamics is a key tool in understanding arousal. Pupil size can serve as a biomarker for the autonomic nervous system balance as well as for identifying brain states. While internal states can also be self-reported when awake, automated detection and non-invasive monitoring is crucial during sleep and reduced consciousness. Here, we introduce iSleep, an innovative pupil tracking and analysis framework for sleep in humans. It features comfortable, humidified eye-tracking goggles and a platform for integrated analysis and prediction capabilities. We show that iSleep allows safe and continuous access to binocular pupil size and ocular dynamics during sleep and anesthesia. iSleep reveals that pupillary fluctuations correlate tightly with brain activity, heartbeat, and breathing, and can reliably predict brain states. Pupil constrictions reflect parasympathetic drive and likely serve a protective function for deep sleep stability; while dilations indicate arousals. Unexpectedly, we observed a decoupling of binocular movements during periods of sleep, indicating alterations in reflexes which usually govern voluntary eye movements. Finally, iSleep was tested in surgery patients under general anesthesia, revealing dynamic pupil changes to noxious stimuli, suggesting the potential for nociception monitoring during surgeries. In summary, iSleep offers an easy-to-use, robust alternative to read out brain states during sleep and anesthesia, opening new avenues in monitoring, diagnostics, and treatments, previously obscured by closed eyelids.

Principal Component Analysis Based Quaternion-Valued Medians for Non-Invasive Blood Glucose Estimation

2025-06-15

Jianbo Feng , Bingo Wing‐Kuen Ling | Sensors

For four-channel photoplethysmograms (PPGs), this paper employs quaternion-valued medians as features for performing non-invasive blood glucose estimation. However, as the PPGs are contaminated by noise, the quaternion-valued medians are also … For four-channel photoplethysmograms (PPGs), this paper employs quaternion-valued medians as features for performing non-invasive blood glucose estimation. However, as the PPGs are contaminated by noise, the quaternion-valued medians are also contaminated by noise. To address this issue, principal component analysis (PCA) is employed for performing the denoising. In particular, the covariance matrix of the four-channel PPGs is computed and the eigen vectors of the covariance matrix are found. Then, the quaternion-valued medians of the four-channel PPGs are found and these quaternion-valued medians are represented as the four-channel real-valued vectors. By applying the PCA to these four-channel real-valued vectors and reconstructing the denoised four-dimensional real-valued vectors, these four-dimensional real-valued vectors are denoised. Next, these denoised four-dimensional real-valued vectors are represented as the denoised quaternion-valued medians. Compared to the traditional denoising methods and the traditional feature extraction methods that are performed in the individual channels, the quaternion-valued medians and the PCA are computed via fusing all of these four-channel PPGs together. Hence, the hidden relationships among these four channels of the PPGs are exploited. Finally, the random forest is used to estimate the blood glucose levels (BGLs). Our proposed PCA-based quaternion-valued medians are compared to the median of each channel of the PPGs and other features such as the time-domain features and the frequency-domain features. Here, the effectiveness and robustness of our proposed method is demonstrated using two datasets. The computer numerical simulation results indicate that our proposed PCA-based quaternion-valued medians outperform the existing quaternion-valued medians and the other features for performing non-invasive blood glucose estimation.

Empirical mode decomposition in clinical signal analysis: A systematic review

2025-06-14

Shanglin Yang , Hsien‐Tsai Wu , Xuwei Liao +2 more | Computers in Biology and Medicine

230-OR: A Real-Time Recalibration Algorithm to Improve the Accuracy of CGM Sensors in Newborns

2025-06-13

Francesco Prendin , SIMONE DELFAVERO , SARA CHERKERZIAN +6 more | Diabetes

Introduction and Objective: Over one third of infants in the U.S. are considered at-risk for neonatal hypoglycemia (NH), which is associated with long-term neurodevelopmental sequelae. Currently, neonatal blood glucose is … Introduction and Objective: Over one third of infants in the U.S. are considered at-risk for neonatal hypoglycemia (NH), which is associated with long-term neurodevelopmental sequelae. Currently, neonatal blood glucose is assessed intermittently, missing over 25% of NH events. Continuous glucose monitoring (CGM) sensors, providing glycemic data every 5 minutes, are standard care for individuals with diabetes and represent a possible solution to improve the monitoring of NH. However, CGM sensors are not optimized for the physiology of neonates. Here we present interim data aimed to develop and validate an algorithm that allows the real-time recalibration of CGM sensors when applied to newborns. Methods: Pregnant women with diabetes and non-diabetic controls were recruited in the third trimester to participate in this IRB-approved study. A factory calibrated Dexcom G6 sensor was placed within 2 hours of birth and point of care blood glucoses (POCBG) were measured per standard of care guidelines with an Abbott Freestyle Precision Pro glucometer. We developed a recalibration algorithm, based on a linear regression model, that exploits the first 2 POCBG and can be applied right after the second measurement. The accuracy of original and recalibrated CGM was measured via Mean Absolute Relative Difference (MARD). Results: On the 12 individuals, a total of 59 POCBG references were available to assess sensor accuracy. The MARD of original CGM data was 40.8%, with a mean error (ME) of 25.2 mg/dL. The recalibration algorithm reduced the MARD of CGM to 12.5% and the ME to 3.5 mg/dL, which was a statistically significant improvement (p&lt;0.01). Conclusion: Application of CGM devices for newborns is key for NH management, but original CGM data do not provide sufficient accuracy to guide clinical care. The proposed real-time recalibration algorithm enhances the accuracy of CGM, approaching the approved accuracy of the devices, potentially enabling a better detection of NH and evaluation of glycemic-control metrics. Disclosure F. Prendin: None. S. DelFavero: Research Support; Dexcom, Inc. Other Relationship; Dexcom, Inc. S. Cherkerzian: None. A. Coburn-Sanderson: None. R. Abdel-Rahman: None. C. Monthe-Dreze: None. A. Galderisi: None. S. Sen: Other Relationship; Dexcom, Inc., nova. A. Facchinetti: None.

Heart Rate Variations During Two Historic Geomagnetic Storms: October and November 2003

2025-06-12

M. Papailiou , H. Mavromichalaki | Atmosphere

The investigation of the two phenomena of Space Weather, i.e., Forbush decreases in the cosmic ray intensity and geomagnetic storms, is a highly developing field of modern scientific research, since … The investigation of the two phenomena of Space Weather, i.e., Forbush decreases in the cosmic ray intensity and geomagnetic storms, is a highly developing field of modern scientific research, since these two phenomena can affect not only technological activities, e.g., electronics, telecommunications, navigations, etc., but also, as evidenced by recent studies, human life as well. This study analyses data of heart rate of volunteers of the Polyclinico Tor Vergata, Rome, Italy, with regard to geomagnetic field’s variations (i.e., geomagnetic storms) and cosmic ray intensity’s fluctuations (i.e., Forbush decreases). Data concerning geomagnetic (Dst- and Ap-index values) and cosmic ray activity derived from the Rome Cosmic Ray Station (Studio Variazioni Intensità Raggi Cosmici: S.V.I.R.CO.) were analyzed. The analysis expands from 24 April 2003 to 12 May 2004 and includes October–November 2003, which was a period of severe activity, when extreme events were recorded (i.e., the Great Halloween Solar Storms and the super storm on November 2003). The variations in heart rate were studied using the ANalysis Of Variance—ANOVA (for various levels of activity of the geophysical environment) and the superimposed epochs methods (during an event’s temporal evolution). Results revealed that high geomagnetic (defined by Dst-index values) and cosmic rays activity is related to heart rate increase. Moreover, the most significant heart rate variations are observed two days before until two days after the development of an event (either geomagnetic storm or a variation in the cosmic ray intensity). The results are in agreement with conclusions presented in the international scientific literature.

Advanced design and classification of wearable near-infrared spectroscopy device using temporal channel reconfiguration multi-graph convolutional neural networks for motor activity

2025-06-12

V. Akila , A. Shirly Edward , J. Anita Christaline | Computer Methods in Biomechanics & Biomedical Engineering

In this paper, advanced design and classification of wearable near-infrared spectroscopy device using temporal channel reconfiguration multi-graph convolutional neural networks for motor activity (WNISD-TRMCNN) are proposed. Input data is collected … In this paper, advanced design and classification of wearable near-infrared spectroscopy device using temporal channel reconfiguration multi-graph convolutional neural networks for motor activity (WNISD-TRMCNN) are proposed. Input data is collected from real-time fNIRS data. The input data are pre-processed using event-triggered consensus Kalman filtering (ETCKF) to remove motion artefacts. Then, the pre-processed data is fed to TRMCNN for classifying wearable NIRS as oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR). To enhance classification, Young's double slit experiment optimization algorithm (YDSEOA) is applied. Performance metrics such as accuracy, precision, AUC, and processing time demonstrate the proposed method's superiority over existing techniques.

Real Time Heart Rate Monitoring System

2025-06-12

Md. Ekhlas Uddin , Sarder Nasir Uddin , Mohammed Omer Ali +1 more | International jounal of information technology and computer engineering.

Heart rate monitoring systems are critical in modern healthcare, enabling continuous tracking of cardiovascular health. Traditional cloud-based solutions pose privacy risks due to centralized data storage and transmission vulnerabilities. To … Heart rate monitoring systems are critical in modern healthcare, enabling continuous tracking of cardiovascular health. Traditional cloud-based solutions pose privacy risks due to centralized data storage and transmission vulnerabilities. To address these concerns, integrating homomorphic encryption with edge computing offers a secure and efficient framework for real-time heart rate monitoring. Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide. Wearable devices and IoT-based health monitoring systems provide continuous heart rate tracking, but data security and latency issues hinder widespread adoption. Heartbeat Sensor is an electronic device that is used to measure the heart rate i.e. speed of the heartbeat. Monitoring body temperature, heart rate and blood pressure are the basic things that we do in order to keep us healthy. In order to measure the body temperature, we use thermometers and a sphygmomanometer to monitor the Arterial Pressure or Blood Pressure. Heart Rate can be monitored in two ways: one way is to manually check the pulse either at wrists or neck and the other way is to use a Heartbeat Sensor. Pulse oximetry is used in this project to detect the heartbeat using fingers. When the heart expands (diastole) the volume of blood inside the fingertip increases and when the heart contracts (systole) the volume of blood inside the fingertip decreases. The resultant pulsing of blood volume inside the fingertip is directly proportional to the heart rate and if you could somehow count the number of pulses in one minute, that's the heart rate in beats per minute (bpm). For this an IR transmitter/receiver pair (LED) placed in close contact with the fingertip. When the heart beats, the volume of blood cells under the sensor increases and this reflects more IR waves to sensor and when there is no beat the intensity of the reflected beam decreases. The pulsating reflection is converted to a suitable current or voltage pulse by the sensor. The sensor output is processed by suitable electronic circuits to obtain a visible indication (digital display).

The Development and Validation of a High-Resolution Photonic and Wireless System for Knee Gait Cycle Monitoring

2025-06-11

R. P. Rocha , Márcio Luís Munhoz Amorim , Melkzedekue Moraes Alcântara Moreira +5 more | Applied System Innovation

This paper presents the development and validation of a high-resolution photonic and wireless monitoring system for knee-referenced gait cycle analysis. The proposed system integrates a single optical Fiber Bragg Grating … This paper presents the development and validation of a high-resolution photonic and wireless monitoring system for knee-referenced gait cycle analysis. The proposed system integrates a single optical Fiber Bragg Grating (FBG) sensor with a resonance wavelength of 1547.76 nm and electronic modules with inertial and magnetic sensors, achieving a 10 p.m. wavelength resolution and 1° angular accuracy. The innovative combination of these components enables a direct correlation between wavelength variations and angular measurements without requiring goniometers or motion capture systems. The system’s practicality and versatility were demonstrated through tests with seven healthy individuals of varying physical attributes, showcasing consistent performance across different scenarios. The FBG sensor, embedded in a polymeric foil and attached to an elastic knee band, maintained full sensing capabilities while allowing easy placement on the knee. The wireless modules, positioned above and below the knee, accurately measured the angle formed by the femur and tibia during the gait cycle. The experimental prototype validated the system’s effectiveness in providing precise and reliable knee kinematics data for clinical and sports-related applications.

RF-AE: Single-site Arterial Elasticity Estimation Using UWB Signals

2025-06-09

Zhi Wang , Beihong Jin , Yuhui Chen +5 more | Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

Arterial elasticity is the capability of the arteries to dilate and constrict in response to fluctuations in blood pressure as the heart circulates blood throughout the body, serving as an … Arterial elasticity is the capability of the arteries to dilate and constrict in response to fluctuations in blood pressure as the heart circulates blood throughout the body, serving as an important indicator for assessing arterial health. However, conventional medical detection methods are often complex, costly, and require direct contact between the user's skin and the device, making them impractical for regular monitoring. In this paper, we propose a contactless single-site arterial elasticity assessment approach using ultra-wideband (UWB) signals, where we estimate the brachial-ankle pulse wave velocity, a key indicator of arterial health, by analyzing the UWB signals reflected off the back of a subject sitting still. Specifically, we first propose preprocessing methods to reduce noise in the received UWB signals, and then design a deep generative adversarial network to generate pulse wave signals from these UWB signals. The generated signals are of high quality and fidelity, exhibiting characteristics of vasoconstriction and vasodilation. Further, we analyze the pulse wave and extract the features related to arterial elasticity from the generated pulse waves. Finally, we employ random forest regression to predict pulse transit time and a body-height based method to estimate the length of the pulse transit path, so as to achieve single-site arterial elasticity assessment. We build the corresponding system named RF-AE and conduct extensive experiments to evaluate its performance. The experimental results show that RF-AE can accurately predict the arterial elasticity of users.

RingBP: Towards Continuous, Comfortable, and Generalized Blood Pressure Monitoring Using a Smart Ring

2025-06-09

Guanzhou Zhu , Dong Zhao , Yixuan Song +7 more | Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

The utilization of wearable devices with integrated photoplethysmography (PPG) sensors for continuous and comfortable Arterial Blood Pressure (ABP) monitoring has gained popularity. However, our analysis on a large-scale self-collected dataset … The utilization of wearable devices with integrated photoplethysmography (PPG) sensors for continuous and comfortable Arterial Blood Pressure (ABP) monitoring has gained popularity. However, our analysis on a large-scale self-collected dataset reveals a significant challenge: approximately 56.7% of individuals experience the absence of the dicrotic notch (a vital morphology feature) in wrist-PPG data, while 23.8% face the same issue in finger-PPG data, which poses a big obstacle to generalized ABP monitoring. To address this challenge, we propose a morphology-independent ABP estimation algorithm based on the Frank-Starling law. We also devise techniques to address calibration and PPG data drift issues commonly faced in continuous ABP monitoring. Further, a system called RingBP is developed towards continuous, comfortable, and generalized ABP monitoring using a smart ring. We design and implement a smart ring prototype and conduct extensive experiments involving 85 participants, demonstrating its superiority over other solutions.