An Explainable Machine Learning Framework for Lower Limb Exoskeleton Robot System

The lower limb exoskeleton robot system is one of the significant tools for the rehabilitation of patients with knee arthritis, which helps to enhance the health of patients and upgrade their quality of life. However, the unexplained gait recognition model decreases the prediction accuracy of the exoskeleton system. The existing explainable models are seldom used in the domain of gait recognition due to their high complexity and large computation. To strengthen the transparency of the model, SHapley Additive exPlanations (SHAP) is applied to gait recognition for the first time in this paper, and an interpretable model framework that can be applied to any lower limb exoskeleton is proposed. Compared with the existing methods, SHAP has a more solid theoretical basis and more efficient calculation methods. The proposed framework can find the relationship between input features and gait prediction, to identify the optimal sensor combination. Additionally, The structure of the gait recognition model can be optimized by adjusting the feature attention of the model with the feature crossover method, and the accuracy of the model can be upgraded by more than 7.12% on average.