An ensemble-based regression approach for continuous estimation of wrist and fingers movements from surface electromyography.

Late development and evolution of high degree-of-freedom (DOF) robotic hands have seen great technological strides to enhance the quality of life for amputated people. A robust hand kinematic estimation mechanisms have shown promising results to control robotic hands that can mimic the human hand functions and perform daily life hand dexterous tasks. In this paper, we propose an ensemble-based regression approach for continuous estimation of wrist and fingers movements from surface Electromyography (sEMG) signals. The proposed approach extracts time-domain features from the sEMG signals, and uses Gradient Boosted Regression Tree (GBRT) ensembles to estimate the kinematics of the wrist and fingers. Furthermore, we propose two different performance evaluation procedures to demonstrate the efficacy of the approach in providing a feasible approach towards accurately estimating hand kinematics.