Abstract
.Significance: Non-contact, camera-based heart rate variability estimation is desirable in numerous applications, including medical, automotive, and entertainment. Unfortunately, camera-based HRV accuracy and reliability suffer due to two challenges: (a) darker skin tones result in lower SNR and (b) relative motion induces measurement artifacts.Aim: We propose an algorithm HRVCam that provides sufficient robustness to low SNR and motion-induced artifacts commonly present in imaging photoplethysmography (iPPG) signals.Approach: HRVCam computes camera-based HRV from the instantaneous frequency of the iPPG signal. HRVCam uses automatic adaptive bandwidth filtering along with discrete energy separation to estimate the instantaneous frequency. The parameters of HRVCam use the observed characteristics of HRV and iPPG signals.Results: We capture a new dataset containing 16 participants with diverse skin tones. We demonstrate that HRVCam reduces the error in camera-based HRV metrics significantly (more than 50% reduction) for videos with dark skin and face motion.Conclusion: HRVCam can be used on top of iPPG estimation algorithms to provide robust HRV measurements making camera-based HRV practical.
Highlights
The nervous and the cardiac systems in the human body are intimately connected, primarily through the autonomous nervous system
We demonstrate that HRVCam reduces the error in camera-based heart rate variability (HRV) metrics significantly for videos with dark skin and face motion
We investigated the use of pulse frequency demodulation (PFDM) to improve the accuracy of HRV metrics measured from a low signal quality imaging photoplethysmography (iPPG) signal
Summary
The nervous and the cardiac systems in the human body are intimately connected, primarily through the autonomous nervous system. This dynamic interplay is reflected in the beat-to-beat variation of the heart rate, formally labeled as heart rate variability (HRV). Several quantitative HRV metrics such as root mean square of successive differences in interbeat intervals (RMSSD) and standard deviation of interbeat intervals (SDNN) summarize the changes in the IBIs.[1,2]. A low-baseline HRV is a symptom of poor autonomic function seen in diseases such as sudden cardiac death[3] and diabetic autonomic neuropathy.[4] Normal values of short term HRV metrics are 32 to 93 ms for SDNN and 19 to 75 ms for RMSSD.[5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
