A Comprehensive Review of Smart Exercise Tracking with Remote Photoplethysmography (rPPG) for Heart Rate Monitoring

Abstract

The modern fitness industry is highly technological, with a reliance on solutions to enrich user experience and safety during exercise. Smart exercise trackers are widely used to track movement and count repetitions. But still, these lack real-time physiological assessment, especially during body motion. Recent developments have also focused on remote photoplethysmography for monitoring non-invasive heart rate, BPM, through facial video analysis, thus bypassing traditional sensor-based methods. This review discusses the integration of rPPG with computer vision-based pose estimation techniques for fitness tracking, evaluating its effectiveness in key challenges such as motion artifacts, lighting variations, and real-time performance. The evaluation of previous studies highlights benefits, limitations, and potential improvements in AI-driven fitness technology. This work contributes to the ongoing research in non-invasive health monitoring, providing insight into the future development of scalable and hardware-agnostic exercise tracking systems.