Modeling dynamic high-DOF finger postures from surface EMG using nonlinear synergies in latent space representation.

Accurate proportional myoelectric control of the hand is important in replicating dexterous manipulation in robot prostheses and orthoses. However, this is still difficult to achieve due to the complex and high degree-of-freedom (DOF) nature present in the governing musculoskeletal system. To address this problem, we suggest using a low dimensional encoding based on nonlinear synergies to represent both the high-DOF finger joint kinematics and the coordination of muscle activities taken from surface electromyographic (EMG) signals. Generating smooth multi-finger movements using EMG inputs is then done by using a shared Gaussian Process latent variable model that learns a dynamical model between both the kinematic and EMG data represented in a shared latent space. The experimental results show that the method is able to synthesize continuous movements of a full five-finger hand model, with total dimensions as large as 69 (although highly redundant and correlated). Finally, by comparing the estimation performances when the number of EMG latent dimensions are varied, we show that these synergistic features can capture the variance, shared and specific to the observed kinematics.