Abstract
Coverage and accuracy of unobtrusively measured biosignals are generally relatively low compared to clinical modalities. This can be improved by exploiting redundancies in multiple channels with methods of sensor fusion. In this paper, we demonstrate that two modalities, skin color variation and head motion, can be extracted from the video stream recorded with a webcam. Using a Bayesian approach, these signals are fused with a ballistocardiographic signal obtained from the seat of a chair with a mean absolute beat-to-beat estimation error below 25 milliseconds and an average coverage above 90% compared to an ECG reference.
Highlights
Unobtrusive acquisition of biosignals for health- and wellness applications has experienced increasing popularity in recent years [1,2,3,4]
The motion and photoplethysmographic component originating from cardiac activity are extracted from a webcam video stream and fused using a Bayesian approach
This improved the coverage of the beat-to-beat interval estimation from 25 % and 50 % to 75 % while maintaining a low error of 32 ms compared to an ECG reference
Summary
Unobtrusive acquisition of biosignals for health- and wellness applications has experienced increasing popularity in recent years [1,2,3,4]. Monitoring of the heart rate and its variability outside the classical scenarios such as hospitals and sleep laboratories is an active area of research. It offers great medical potential, as the heart rate variability (HRV) has a wide range of applications from work stress analysis [5] to the prediction of sudden cardiac death in chronic heart failure patients [6]. When analyzing unobtrusively acquired measurement data, episodes that contain no valid information can occur and must be excluded from subsequent processing. An unobtrusive measurement system is often evaluated in terms of accuracy and coverage, as one can often only be improved at the cost of the other
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