Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

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

doi:10.1109/tbme.2010.2086456

Cited by 1,224 publications

(921 citation statements)

References 22 publications

(22 reference statements)

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“…Poh et al [1] show that heart rate, breath rate and heart rate variability can be determined by a laptop's built-in video camera with great accuracy. Scully et al [2] use the video of a subject's finger resting on a mobile phone's camera lens to extract heart rate, heart rate variability, breathing rate and blood oxygen saturation.…”

Section: Relation To Prior Workmentioning

confidence: 99%

Automatic recognition of physiological parameters in the human voice: Heart rate and skin conductance

Schuller¹,

Friedmann

Eyben

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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We show that high pulse/low pulse, heart rate and skin conductance recognition can reach good accuracies using classification on a large group of 4k audio features extracted from sustained vowels and breathing periods. A database containing audio, heart rate and skin conductance recordings from 19 subjects is established for evaluation of audio-based bio-signal recognition. On this database in speaker-dependent testing, heart rate and skin conductance can be determined with a correlation coefficient of .861/.960 and mean absolute error of 8.1 BPM/88.2 µMhO for regression based on sustained vowels recorded from a room microphone. Using the same set-up, a high pulse/low pulse classification can reach an unweighted accuracy of 82.7%. The results are largely independent from microphone type and the two bio-signals can be determined from breathing periods as well. Performance does, however, degrade in speaker-independent setting.

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Section: Relation To Prior Workmentioning

confidence: 99%

Automatic recognition of physiological parameters in the human voice: Heart rate and skin conductance

Schuller¹,

Friedmann

Eyben

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…Facial video recording using the smartphone camera is a novel method of detecting a pulsatile facial photoplethysmographic signal without physical contact,18, 19, 20 with the potential for distant screening. A small proof‐of‐concept study supported the feasibility of such a contact‐free method for AF detection,19 but the performance of facial photoplethysmography for the detection of AF in a prospective study has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Contact‐Free Screening of Atrial Fibrillation by a Smartphone Using Facial Pulsatile Photoplethysmographic Signals

Yan

Lai

Chan

et al. 2018

JAHA

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BackgroundWe aimed to evaluate a novel method of atrial fibrillation (AF) screening using an iPhone camera to detect and analyze photoplethysmographic signals from the face without physical contact by extracting subtle beat‐to‐beat variations of skin color that reflect the cardiac pulsatile signal.Methods and ResultsPatients admitted to the cardiology ward of the hospital for clinical reasons were recruited. Simultaneous facial and fingertip photoplethysmographic measurements were obtained from 217 hospital inpatients (mean age, 70.3±13.9 years; 71.4% men) facing the front camera and with an index finger covering the back camera of 2 independent iPhones before a 12‐lead ECG was recorded. Backdrop and background light intensity was monitored during signal acquisition. Three successive 20‐second (total, 60 seconds) recordings were acquired per patient and analyzed for heart rate regularity by Cardiio Rhythm (Cardiio Inc, Cambridge, MA) smartphone application. Pulse irregularity in ≥1 photoplethysmographic readings or 3 uninterpretable photoplethysmographic readings were considered a positive AF screening result. AF was present on 12‐lead ECG in 34.6% (n=75/217) patients. The Cardiio Rhythm facial photoplethysmographic application demonstrated high sensitivity (95%; 95% confidence interval, 87%–98%) and specificity (96%; 95% confidence interval, 91%–98%) in discriminating AF from sinus rhythm compared with 12‐lead ECG. The positive and negative predictive values were 92% (95% confidence interval, 84%–96%) and 97% (95% confidence interval, 93%–99%), respectively.ConclusionsDetection of a facial photoplethysmographic signal to determine pulse irregularity attributable to AF is feasible. The Cardiio Rhythm smartphone application showed high sensitivity and specificity, with low negative likelihood ratio for AF from facial photoplethysmographic signals. The convenience of a contact‐free approach is attractive for community screening and has the potential to be useful for distant AF screening.

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“…Early experiments have confirmed the viability of this technique (Poh et al, 2010;Farley et al, 2011;Poh et al, 2011). …”

Section: Absorption Coefficientmentioning

confidence: 87%

Heartbeat biometrics: a sensing system perspective

Israel¹,

Irvine²

2012

IJCB

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This paper reviews the emerging research into exploitation of heartbeat data as a biometric for human identification. A variety of methods have been proposed for acquiring heartbeat signatures and a range of processing methods has been examined. We approach the biometric identification and verification problem by characterising the three major factors affecting performance: individual variants, environmental variants, and sensor variants. The ability to collect and process the signal, exploit the data for individual identification or verification, and disseminate the information depends on all three of these factors. Within each component, we have identified the relevant research. Where possible, we have tied these research papers to practical examples using high resolution ECG data. The research indicates that the heartbeat contains rich information about the individual, their level of anxiety, and the cardiac state.Keywords: biometrics; heartbeat; ECG; sensing system; cardiovascular function; human identification. areas of interest include novel biometrics, signal and image processing, quantification and modelling of data quality, and cognitive modeling. He was a Principal Investigator for DARPA's Human Identification at a Distance Program (HumanID). He serves on planning committees for IEEE and SPIE, and has served on several advisory panels for the Department of Defence and the Department of Energy. He has authored over a hundred journal and conference papers and holds a PhD in Mathematical Statistics from Yale University.

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Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

Cited by 1,224 publications

References 22 publications

Automatic recognition of physiological parameters in the human voice: Heart rate and skin conductance

Automatic recognition of physiological parameters in the human voice: Heart rate and skin conductance

Contact‐Free Screening of Atrial Fibrillation by a Smartphone Using Facial Pulsatile Photoplethysmographic Signals

Heartbeat biometrics: a sensing system perspective

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