Estimating Wrist Joint Torque Using Regression Ensemble of Bagged Trees under Multiple Wrist Postures

Powered prostheses and orthoses are expected to significantly improve quality of life of individuals with motor impairment or limb amputation. Despite the advances in the mechatronic design of powered prostheses and orthoses, their use is not yet widespread because of limitations in their control schemes. Methods that use Electromyography (EMG) measurements to estimate a physical signal related to joint kinematics or kinetics to be used as a control variable are promising as these methods may enable robust control schemes for high-dimensional powered prostheses and orthoses. In this study, we propose a method to estimate joint torques about two degrees of freedom of the wrist joint. The developed method uses regression ensemble of bagged trees, and is applied to isometric tasks under multiple wrist postures. The proposed regression model estimated Flexion/Extension and Radial/Ulnar Deviation torques with coefficient of determination equal to 0.94 and 0.88, respectively, when training and testing at the neutral wrist posture. The analysis showed that even using only one wrist posture for training, good results can be obtained in estimating joint torque under different postures. The outcome of this study suggests that a single-posture calibration combined with the proposed estimation method can be used as a paradigm to derive a linear multi-dimensional control signal that could be suitable for EMG control of prosthetic or exoskeletal devices.