Toward EMG-controlled force field generation for training and rehabilitation: From movement data to muscle geometry

EMG signals are often used to control prostheses or assistive devices, but have been rarely used in rehabilitation. We propose a novel approach to personalised rehabilitation, based on EMG-driven force field adaptation. As a step toward this direction, here we show how EMG activity and movement data during a robot-assisted motor task can be used to estimate muscle geometry. We compare three different models of muscle geometry, characterised by (i) constant moment arms (CM); (ii) a normative model, based on polynomial functions of joint angles with fixed coefficients (normative polynomial, NP); and (iii) a person-adaptive model, in which the same polynomial model is fitted to individual subjects data (fitted polynomial, FP). We found that the FP model has the best performance, specially for subjects whose size is farther from 'average'. The fitting results also emphasise the adverse effect of muscles co-contraction.