PPGSpotter: Personalized Free Weight Training Monitoring Using Wearable PPG Sensor

Free weight training (FWT) is of utmost importance for physical well-being. However, the success of FWT depends on choosing the suitable workload, as improper selections can lead to suboptimal outcomes or injury. Current workload estimation approaches rely on manual recording and specialized equipment with limited feedback. Therefore, we introduce PPGSpotter, a novel PPG-based system for FWT monitoring in a convenient, low-cost, and fine-grained manner. By characterizing the arterial geometry compressions caused by the deformation of distinct muscle groups during various exercises and workloads in PPG signals, PPGSpotter can infer essential FWT factors such as workload, repetitions, and exercise type. To remove pulse-related interference that heavily contaminates PPG signals, we develop an arterial interference elimination approach based on adaptive filtering, effectively extracting the pure motion-derived signal (MDS). Furthermore, we explore 2D representations within the phase space of MDS to extract spatiotemporal information, enabling PPGSpotter to address the challenge of resisting sensor shifts. Finally, we leverage a multi-task CNN-based model with workload adjustment guidance to achieve personalized FWT monitoring. Extensive experiments with 15 participants confirm that PPGSpotter can achieve workload estimation (0.59 kg RMSE), repetitions estimation (0.96 reps RMSE), and exercise type recognition (91.57% F1-score) while providing valid workload adjustment recommendations.