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Updated: Nov 17, 2025
Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
Published on: June 5, 2019
HRVCam: robust camera-based measurement of heart rate variability
Amruta Pai1, Ashok Veeraraghavan1, Ashutosh Sabharwal1
1Rice University, Scalable Health Labs, Electrical and Computer Engineering Department, Houston, Texa, United States.
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.
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