A Machine Learning-Based Approach for the Design of Lower Limb Exoskeleton

Abstract

Active Exoskeletons can become a powerful tool for therapists for the rehabilitation of patients suffering from neurophysiological conditions. The mathematical modeling for estimating joint moments required for human walking movement proves difficult due to the high number of degrees of freedom (DoF) and the complexity of movement. Another factor that poses a problem is the unavailability of ground reaction force (GRF) data, which must be present as the external applied forces in the model. This paper presents a machine learning-based approach for predicting joint moments for walking that uses only the kinematic data of the subjects. The dataset used includes data available from published sources as well as data collected by the authors. The predictions have been compared with and validated using the joint moment results from optimization-based inverse dynamics model in OpenSim. Subsequently, a concept design of a lower limb exoskeleton has been presented and actuator requirements for the same are set according to the joint moment predictions for a specific human subject. The prototype design includes eight rotational degrees of freedom (DOF) in total, i.e., four degrees of freedom per leg: two at the hip joint, one at the knee joint and one at the ankle joint. The feasibility study of the prototype has been carried out with the help of finite element analysis (FEA) in Ansys software after utilizing the weight of the human being and joint rotations as inputs to the model. Based on the results obtained from the FEM, the design has been optimized to ensure structural stability.