EMG-based Simultaneous Estimations of Joint Angle and Torque during Hand
Interactions with Environments
Abstract
It is necessary to control contact force through modulation of joint
stiffness in addition to the position of our limb when manipulating an
object. This is achieved by contracting the agonist muscles in an
appropriate magnitude, as well as, balancing it with contraction of the
antagonist muscles. Here 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-antagonistic muscle
pairs using electromyography in real time. The long short-term memory
(LSTM) network that is capable of learning time series of a long-time
span with varying time lags is employed as the core processor of the
proposed technique. We tested both the unidirectional LSTM network and
bidirectional LSTM network. A validation was conducted on the wrist
joint moving along a given trajectory under resistance generated by a
robot. The decoding approach provided an agreement of greater than 93%
in kinetics (i.e. torque) estimation and an agreement of greater than
83% in kinematics (i.e. angle) estimation, between the actual and
estimated variables, during interactions with an environment. We found
no significant differences in performance between the unidirectional
LSTM and bidirectional LSTM as the learning device of the proposed
decoding method.