EMG-Based Gait Estimation Using Koopman-Inspired Method.

EMG-based state estimation and prediction in human-machine interaction,biomechanics, and robotics applications is an emerging approach offering potential improvements in control and user intent prediction. Koopman operator theory (KOT) is a powerful method for analyzing and transforming nonlinear dynamical systems into a higher-dimensional space so that they can be described using linear operations. In this study, first, we utilize the power of neural networks to capture the nonlinear relationships between surface electromyography (EMG) signals and lower-limb joint states. We use these relationships to estimate the current joint states purely from sEMG signals. The second stage of this study is to capture wearers' intentions in gaits with Koopman operators' powerful nonlinear system representation capabilities and use this framework to learn the temporal relationships between the current and near-future joint states. We start simply by implementing the EMG-based Koopman operator to estimate knee and ankle angles during different gaits, as a pioneer study. For intra-subject prediction, we achieved an RMSE of $3.61^{\circ }$ for the knee and $1.78^{\circ }$ for the ankle under same-gait conditions, and $3.78^{\circ }$ for the knee and $1.43^{\circ }$ for the ankle under transient-gait conditions. Cross-subject leave-one-subject-out (LOSO) generalisation yielded a mean RMSE of $7.79^{\circ }$ for the knee and $2.12^{\circ }$ for the ankle under same-gait conditions, and $8.77^{\circ }$ for the knee and $3.09^{\circ }$ for the ankle under transient-gait conditions.