Simultaneous Estimations of Joint Angle and Torque in Interactions with Environments using EMG

We develop a decoding technique that estimates both the position and torque of a joint of the limb in interaction with an environment based on activities of the agonist-antagonist pair of muscles using electromyography in real time. The long short-term memory (LSTM) network is employed as the core processor of the proposed technique that is capable of learning time series of a long-time span with varying time lags. A validation that is conducted on the wrist joint shows that the decoding approach provides an agreement of greater than 95% in kinetics (i.e. torque) estimation and an agreement of greater than 85% in kinematics (i.e. angle) estimation, between the actual and estimated variables, during interactions with an environment. Also demonstrated is the fact that the proposed decoding method inherits the strengths of the LSTM network in terms of the capability of learning EMG signals and the corresponding responses with time dependency.