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Camera-based physiological measurement enables vital signs to be captured unobtrusively without contact with the body. Remote, or imaging, photoplethysmography involves recovering peripheral blood flow from subtle variations in video pixel intensities. While the pulse signal might be easy to obtain from high quality uncompressed videos, the signal-to-noise ratio drops dramatically with video bitrate. Uncompressed videos incur large file storage and data transfer costs, making analysis, manipulation and sharing challenging. To help address these challenges, we use compression specific supervised models to mitigate the effect of temporal video compression on heart rate estimates. We perform a systematic evaluation of the performance of state-of-the-art algorithms across different levels, and formats, of compression. We demonstrate that networks trained on compressed videos consistently outperform other benchmark methods, both on stationary videos and videos with significant rigid head motions. By training on videos with the same, or higher compression factor than test videos, we achieve improvements in signal-to-noise ratio (SNR) of up to 3 dB and mean absolute error (MAE) of up to 6 beats per minute (BPM).
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This content will become publicly available on January 1, 2026
A Comparative Study of Principled rPPG-Based Pulse Rate Tracking Algorithms for Fitness Activities
Performance improvements obtained by recent principled approaches for pulse rate (PR) estimation from face videos have typically been achieved by adding or modifying certain modules within a reconfigurable system. Yet, evaluations of such remote photoplethysmography (rPPG) are usually performed only at the system level. To better understand each module's contribution and facilitate future research in explainable learning and artificial intelligence for physiological monitoring, this paper conducts a comparative study of video-based, principled PR tracking algorithms, with a focus on challenging fitness scenarios. A review of the progress achieved over the last decade and a half in this field is utilized to construct the major processing modules of a reconfigurable remote pulse rate sensing system. Experiments are conducted on two challenging datasets—an internal collection of 25 videos of two Asian males exercising on stationary-bike, elliptical, and treadmill machines, and 34 videos from a public ECG fitness database of 14 men and 3 women exercising on elliptical and stationary-bike machines. The signal-to-noise ratio (SNR), Pearson's correlation coefficient, error count ratio, error rate, and root mean squared error are used for performance evaluation. The top-performing configuration produces respective values of −0.8 dB, 0.86, 9%, 1.7%, and 3.3 beats per minute (bpm) for the internal dataset and 1.3 dB, 0.77, 28.6%, 6.0%, and 8.1 bpm for the ECG Fitness dataset, achieving significant improvements over alternative configurations. Our results suggest a synergistic effect between pulse color mapping and adaptive motion filtering, as well as the importance of a robust frequency tracking algorithm for PR estimation in low SNR settings.
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- PAR ID:
- 10630048
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Biomedical Engineering
- Volume:
- 72
- Issue:
- 1
- ISSN:
- 0018-9294
- Page Range / eLocation ID:
- 152-165
- Subject(s) / Keyword(s):
- Heart/pulse rate (HR/PR) remote photoplethysmography (rPPG) fitness exercise pulse color mapping motion compensation frequency tracking explainable AI
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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