Abstract
Wearable sensors combined with machine learning (ML) offer a promising approach for estimating joint kinetics in real-world settings, with potential applications in athlete monitoring and injury prevention. However, the variety of sensor configurations in previous studies complicates comparisons and optimal configuration selection. This study compared different wearable sensor configurations, comprising inertial measurement units (IMUs) and pressure insoles (PIs), to determine their influence on the accuracy of ML – based predictions of 3D knee moments during running. Sensor configurations ranged from one to four IMUs, with and without PIs. The dataset consisted of wearable and ground truth knee moment data from 19 recreational runners during treadmill running. Model performance of the convolutional neural networks was evaluated on an independent test set. Hyperparameter optimization (HPO) was applied to refine model architectures and training parameters. Performance gains by PIs and a greater number of IMUs were small but significant. The results after HPO confirmed similar performances between single- and multi-sensor configurations, suggesting only small benefits from additional sensors. Our findings highlight that both sensor configuration and model optimization play critical roles in achieving optimal performance. We provide practical recommendations for sensor selection, balancing accuracy and feasibility, to enable biomechanical assessments in real-world environments.