Robust Non-Invasive Monitoring of Cumulative Tibia Load in Male Recreational Runners Using Multi-Modal Wearables and Deep Learning.

Tibial stress fractures are overuse injuries in runners and are associated with cumulative internal bone stress. Current wearable devices usually measure external biomechanical loads rather than tissue-level stress within the tibia. We developed and evaluated a wearable-sensor-based surrogate model for reconstructing the temporal evolution of tibial bone stress in healthy male recreational runners, using participant-specific dynamic finite element analysis (FEA) as the supervision source. Dynamic FEA-derived stress fields from 50 male runners, with heterogeneous and orthotropic material properties, were used as ground-truth regression targets for deep learning. The main model used synchronized inertial measurement unit and surface electromyography signals as inputs and a convolutional-transformer backbone to map surface signals to dynamic tibial stress outputs. A self-correction with adaptive mask training module was also evaluated as an auxiliary regularization strategy. The model predicted temporal stress waveforms (R2 = 0.90) and global peak magnitudes (Pearson r = 0.944), with low systematic bias but residual individual-level variability. Robustness training was evaluated under sensor noise and modality-loss conditions, and edge-device inference required less than 60 ms. These findings indicate that wearable sensors can support tibial stress reconstruction in male recreational runners, although external validation in female runners and other populations is required before broader application.