EMG-based Estimation of Wrist Motion Using Polynomial Models

Background: Myoelectric control is a method of decoding the motor intent from the electromyogram (EMG) data and
using the estimated intent to control prostheses and robots. This work investigates estimation of the wrist kinematics
from EMG signals using polynomial models. Due to their low complexity, polynomial models are potentially the perfect
choice for EMG-kinematics modeling.
Methods: Ten ablebodied individuals participated in this study, where the EMG signals from the forearm and the
wrist kinematics from the contralateral wrist were measured during mirrored contractions. Two sets of EMG features
were employed including the time domain (TD) set, and TD features along with autoregressive coefficients (TDAR).
Polynomial models of order 1 to 4 were applied to map the EMG signals to the wrist motions. The performance was
directly compared to that of a multilayer perceptron (MLP) neural network.
Results: The estimation accuracy of the wrist kinematics improved with increasing the order of the model, but saturated
at the 4th order. When using the TD set, the MLP significantly outperformed all polynomial models. However, when
using the TDAR set, the polynomial models’ performance improved so that the 4th order model performance was not
significantly different than that of the MLP in two DoFs, although it was lower than MLP in one DoF.
Conclusion: These results indicate that polynomial models are not as effective as more complex models such as
neural networks, in learning the highly nonlinear mapping between the EMG data and motion intent. However, using a
sufficiently high number of various EMG features, would reduce the mapping nonlinearities, and thereby may increase
the polynomial models’ performance to levels similar to those of complex black box models.
Level of evidence: I