Toward Wearable Joint Load Monitoring: Concurrent Estimation of Hip, Knee, and Ankle Sagittal Contact Forces from Sparse IMU Measurements.
Ma T, Fan X, Yu N, Han J, Guan J, Fan T et al. · IEEE transactions on bio-medical engineering · 2026
Objective: Continuous monitoring of lower extremity joint contact forces is critical for rehabilitation medicine, sports injury prevention, and assistive device development. Yet, current wearable solutions often lack multi-joint integration or require cumbersome sensor arrays. This study
Aims: to concurrently estimate hip, knee, and ankle sagittal compressive contact forces using a sparse inertial measurement unit (IMU) configuration across diverse daily activities.
Methods: We collected data from 14 healthy adults equipped with four IMU sensors performing various daily movements, with reference contact forces captured using optical motion capture and force platforms. To estimate joint contact forces, we developed a domain-informed multi-task temporal convolutional network (Ada-Att-MTCN). This architecture integrates a shared temporal convolutional network encoder, task-specific attention mechanisms, homoscedastic uncertainty-based dynamic task weighting, and a soft physiological-bound penalty. Performance was evaluated using 14-fold leave-one-subject-out nested cross-validation against 4 baseline
methods, with root mean square error (RMSE) and coefficient of determination (R²) as evaluation metrics.
Results: On the self-collected dataset, Ada-Att-MTCN achieved the highest accuracy across all activities, with mean RMSEs of 0.328 ± 0.13 BW (hip), 0.380 ± 0.09 BW (knee), and 0.365 ± 0.15 BW (ankle), and an average R2 of 0.822 ± 0.10. On the external dataset, Ada-Att-MTCN likewise performed best among all
methods (average R² = 0.754 ± 0.05).
Conclusion: The proposed framework captures complex inter-joint dynamics, enabling robust simultaneous load estimation with drastically reduced hardware complexity.
Significance: This approach provides a practical, non-invasive solution for transitioning lab-grade multi-joint load monitoring to wearable deployment in rehabilitation and sports medicine.