EMG-based Estimation of Wrist Motion Using Polynomial Models

Document Type : RESEARCH PAPER


Biomedical Engineering Department, School of Medicine, Shahid Beheshti University of Medical Sciences, Velenjak, Tehran, Iran


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


1. Oskoei MA, Hu H. Myoelectric control systems—A
survey. Biomedical Signal Processing and Control.
2. Farina D, Jiang N, Rehbaum H, Holobar A, Graimann
B, Dietl H, et al. The extraction of neural information
from the surface EMG for the control of upper-limb
prostheses: emerging avenues and challenges. IEEE
Transactions on Neural Systems and Rehabilitation
Engineering. 2014;22(4):797-809.
3. Ortiz-Catalan M, Håkansson B, Brånemark R. Realtime
and simultaneous control of artificial limbs based
on pattern recognition algorithms. IEEE Transactions
on Neural Systems and Rehabilitation Engineering.
4. Ameri A, Akhaee MA, Scheme E, Englehart K. Real-time,
simultaneous myoelectric control using a convolutional
neural network. PloS one. 2018;13(9):e0203835.
5. Atzori M, Cognolato M, Müller H. Deep learning
with convolutional neural networks applied
to electromyography data: A resource for the
classification of movements for prosthetic hands.
Frontiers in neurorobotics. 2016;10:9.
6. Ameri A. EMG-based wrist gesture recognition
using a convolutional neural network. Tehran
University Medical Journal TUMS Publications.
7. Geng W, Du Y, Jin W, Wei W, Hu Y, Li J. Gesture
recognition by instantaneous surface EMG images.
Scientific reports. 2016;6:36571.
8. Nielsen JL, Holmgaard S, Jiang N, Englehart KB, Farina
D, Parker PA. Simultaneous and proportional force
estimation for multifunction myoelectric prostheses
using mirrored bilateral training. IEEE Transactions
on Biomedical Engineering. 2011;58(3):681-8.
9. Muceli S, Farina D. Simultaneous and proportional
estimation of hand kinematics from EMG during
mirrored movements at multiple degrees-offreedom.
IEEE transactions on neural systems and
rehabilitation engineering. 2012;20(3):371-8.
10. Ameri A, Akhaee MA, Scheme E, Englehart K.
Regression convolutional neural network for
improved simultaneous EMG control. Journal of
neural engineering. 2019;16(3):036015.
11. Hahne JM, Schweisfurth MA, Koppe M, Farina D.
Simultaneous control of multiple functions of bionic
hand prostheses: Performance and robustness in end
users. Science Robotics. 2018;3(19):eaat3630.
12. Ameri A, Scheme EJ, Kamavuako EN, Englehart KB,
Parker PA. Real-time, simultaneous myoelectric
control using force and position-based training
paradigms. IEEE Transactions on Biomedical
Engineering. 2014;61(2):279-87.
13. Oda S. Motor control for bilateral muscular 
contractions in humans. The Japanese journal of
physiology. 1997;47(6):487-98.
14. De Luca CJ, Erim Z. Common drive of motor units in
regulation of muscle force. Trends in neurosciences.
15. Hahne JM, Biessmann F, Jiang N, Rehbaum H,
Farina D, Meinecke F, et al. Linear and nonlinear
regression techniques for simultaneous and
proportional myoelectric control. IEEE Transactions
on Neural Systems and Rehabilitation Engineering.
16. Dwivedi SK, Ngeo JG, Shibata T. Extraction of Nonlinear
Synergies for Proportional and Simultaneous
Estimation of Finger Kinematics. IEEE Transactions
on Biomedical Engineering. 2020.
17. Blana D, Van Den Bogert AJ, Murray WM, Ganguly A,
Krasoulis A, Nazarpour K, et al. Model-based control
of individual finger movements for prosthetic hand
function. IEEE Transactions on Neural Systems and
Rehabilitation Engineering. 2020;28(3):612-20.
18. Hudgins B, Parker P, Scott RN. A new strategy for
multifunction myoelectric control. IEEE Transactions
on Biomedical Engineering. 1993;40(1):82-94.
19. Englehart K, Hudgins B. A robust, real-time control
scheme for multifunction myoelectric control. IEEE
transactions on biomedical engineering. 2003;