Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment

Dimitra Blana* (Corresponding Author), Theocharis Kyriacou, Joris M. Lambrecht, Edward K Chadwick

*Corresponding author for this work

Research output: Contribution to journalArticle

19 Citations (Scopus)
1 Downloads (Pure)

Abstract

Transhumeral amputation has a significant effect on a person's independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person's proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.

Original languageEnglish
Pages (from-to)21-27
Number of pages7
JournalJournal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Volume29
Early online date9 Jul 2015
DOIs
Publication statusPublished - Aug 2016

Fingerprint

Biomechanical Phenomena
Prostheses and Implants
Pronation
Supination
Humerus
Elbow
Amputation
Forearm
Upper Extremity
Quality of Life
Muscles

Keywords

  • Amputation
  • Prosthesis
  • Myoelectric
  • Transhumeral
  • Electromyography
  • Artificial neural network
  • Control

Cite this

@article{2777d0f6fc3a4f61af418e1c41b69c1e,
title = "Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment",
abstract = "Transhumeral amputation has a significant effect on a person's independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person's proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78{\%} and minimal overshoot (1.5{\%}). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.",
keywords = "Amputation, Prosthesis, Myoelectric, Transhumeral, Electromyography, Artificial neural network, Control",
author = "Dimitra Blana and Theocharis Kyriacou and Lambrecht, {Joris M.} and Chadwick, {Edward K}",
note = "Acknowledgment This study was partly supported by a UK Medical Research Council Centenary Award to Keele University.",
year = "2016",
month = "8",
doi = "10.1016/j.jelekin.2015.06.010",
language = "English",
volume = "29",
pages = "21--27",
journal = "Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology",
issn = "1050-6411",
publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Feasibility of using combined EMG and kinematic signals for prosthesis control

T2 - A simulation study using a virtual reality environment

AU - Blana, Dimitra

AU - Kyriacou, Theocharis

AU - Lambrecht, Joris M.

AU - Chadwick, Edward K

N1 - Acknowledgment This study was partly supported by a UK Medical Research Council Centenary Award to Keele University.

PY - 2016/8

Y1 - 2016/8

N2 - Transhumeral amputation has a significant effect on a person's independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person's proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.

AB - Transhumeral amputation has a significant effect on a person's independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person's proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.

KW - Amputation

KW - Prosthesis

KW - Myoelectric

KW - Transhumeral

KW - Electromyography

KW - Artificial neural network

KW - Control

U2 - 10.1016/j.jelekin.2015.06.010

DO - 10.1016/j.jelekin.2015.06.010

M3 - Article

C2 - 26190031

VL - 29

SP - 21

EP - 27

JO - Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology

JF - Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology

SN - 1050-6411

ER -