All-in-One: Smartphone-Based Intelligent Photonic mHealth System for Plantar and Cardiorespiratory Monitoring

In elderly remote healthcare, accurate measurement of plantar and cardiorespiratory functions is essential for early detection of mobility decline and chronic disease management, but traditional electrical wearable sensors typically cannot support integrated multimodal monitoring that such care requires. In this work, an all-in-one photonic solution is developed by embedding flexible polydimethylsiloxane polymer optical fiber (PDMS-POF) channels into an insole and chest belt, which are integrated with a smartphone via a custom 3D-printed connector to simultaneously capture gait, respiration, and heart rate. This system employs a series of signal processing techniques to automatically remove motion artifacts and optical noise, achieving clinical-grade accuracy (≤0.6% error in stance/swing detection; ≤5% error in heart and respiratory rates during static activities). Building on this foundation, we introduce TodyNet-Pro, a temporal dynamic graph neural network (GNN) designed to fuse multiple physiological signals and learn activity labels end to end using sparsity constraints and temporal weighting. In trials involving ten volunteers and validated against clinical standards, TodyNet-Pro achieved 95.5% accuracy in identifying seven static and dynamic activities, and over 80% accuracy in detecting four abnormal gait patterns, outperforming both standard deep learning models and recent studies on elderly activity recognition.