A Wearable PPG-Based Monitoring System for Personalized Free Weight Training

Free weight training (FWT) is of utmost importance for physical well-being. The success of FWT depends largely 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 wearable PPG-based FWT monitoring system in a convenient, low-cost, and fine-grained manner. By characterizing the arterial geometry compressions caused by the deformation of distinct muscle groups, PPGSpotter can infer essential FWT factors such as current workload, repetitions, and exercise type and provide recommendations for workload adjustment. To remove pulse-related interference, we develop an arterial interference elimination approach based on adaptive filtering, effectively extracting the pure motion-derived signal (MDS). Furthermore, we explore 2D representations of MDS within the phase space to extract spatiotemporal information, enabling PPGSpotter to address the challenge of resisting sensor shifts. Finally, we leverage a multi-task CNN-based network and workload adjustment guidance to achieve personalized FWT monitoring. Extensive experiments with 15 participants confirm that PPGSpotter can achieve promising 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 (0.22 kg RMSE).