Lower Limbs 3D Joint Kinematics Estimation From Force Plates Data and Machine Learning

This study investigated the possibility of using machine learning to estimate 3D lower-limb joint kinematics during a rehabilitation squat exercise from force plate data, that can be collected very simply outside of a laboratory and does not pose privacy issues. The proposed approach is based on a bidirectional-Long-Short-Term-Memory (Bi-LSTM) associated to a Multi-Layer-Perceptron (MLP) model. The use of MLP allows fast training and evaluation time. The model was trained and validated on nineteen healthy young volunteers using a stereophotogrammetric motion capture system to collect ground truth data. Volunteers performed squats in normal conditions and using an ankle brace to simulate pathological motion. Also additional loads were added onto lower limbs segments to study the influence atypical mass distribution. The root mean square differences between the estimated joint angles and those reconstructed with the stereophotogrammetric system were lower than 6deg with correlation coefficients higher than 0.9 in average. Furthermore, the inference time of the proposed approach was as low as $12 \mu \mathrm{~s}$ paving the way of future reliable real-time measurement tools.