MEASUREMENT, PREDICTION AND VALIDATION OF HUMAN GAIT TORQUE FOR LOWER LIMB ASSISTIVE DEVICES

Abstract

The lower limb rehabilitation process requires external control inputs based on joint kinematics and kinetics to its actuator for generating the motion of a robotic assistive device. This paper describes the procedures for measuring, predicting and validating the joint kinematics and kinetics of human gait for the lower limb exoskeleton. The high-speed multi-camera-based video system associated with passive optical markers and a force plate sensor is used to measure the gait data. The lower limb joint torques have also been predicted using three different machine learning regression models, viz. Decision Tree (DT), Gaussian Process Regression (GPR) and Support Vector Machine (SVM). The analysis is further validated with the geometry-based analytical method. Three lower limb joint torques have been predicted by utilizing gait period, joint angle, joint velocity and joint acceleration as the predictors. The lowest average 10-fold cross-validation Root Mean Square Error (RMSE) using the DT model between measured and predicted torques have been recorded for the ankle, knee and hip joint as 1.9521, 1.5527 and 1.5684[Formula: see text]Nm, respectively. This study enhances the field of medical rehabilitation by providing the required knowledge of human gait kinematics/kinetics and predicting the joint torque profile without any force sensor.