Dynamic motion phase segmentation using sEMG during countermovement jump based on hidden semi-Markov model

Dynamic motion of human shows kinematic aspects related to storing elastic energy in skeletal muscle. This results from joint stiffness modulation and as a consequence, countermovement which is opposite to the intended motion is observed. We propose a segmentation algorithm based on a hidden semi-Markov model that infers dynamic motion phases probabilistically from sEMG observations during countermovement jump. In addition, parameter re-estimation of both left-right state transition and restriction of state duration is applied to reduce frequent state transition due to large variation of sEMG observation probability. In experiments, the segmentation of motion phases using sEMG identified the phases of the vertical position of torso successfully and the parameter re-estimation reduced both the error rate and the transition occurrence.