Online mapping of EMG signals into kinematics by autoencoding
File(s)Vujaklija_Online mapping of EMG_BMC.pdf (1.27 MB)
Published version
Author(s)
Type
Journal Article
Abstract
Background
In this paper, we propose a nonlinear minimally supervised method based on auto-encoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach.
Methods
Seven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized by six metrics.
Results
Both methods allowed a completion rate close to 100%, however AEN outperformed SOA for all other performance metrics, e.g. it allowed to perform the tasks on average in half the time with respect to SOA. Moreover, the amount of information transferred by the proposed method in bit/s was nearly twice the throughput of SOA.
Conclusions
These results show that autoencoders can map EMG signals into kinematics with the potential of providing intuitive and dexterous control of artificial limbs for amputees.
In this paper, we propose a nonlinear minimally supervised method based on auto-encoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach.
Methods
Seven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized by six metrics.
Results
Both methods allowed a completion rate close to 100%, however AEN outperformed SOA for all other performance metrics, e.g. it allowed to perform the tasks on average in half the time with respect to SOA. Moreover, the amount of information transferred by the proposed method in bit/s was nearly twice the throughput of SOA.
Conclusions
These results show that autoencoders can map EMG signals into kinematics with the potential of providing intuitive and dexterous control of artificial limbs for amputees.
Date Issued
2018-03-13
Date Acceptance
2018-03-05
Citation
Journal of NeuroEngineering and Rehabilitation, 2018, 15
ISSN
1743-0003
Publisher
BioMed Central
Journal / Book Title
Journal of NeuroEngineering and Rehabilitation
Volume
15
Copyright Statement
© The Author(s). 2018
Open Access
This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (
http://creativecommons.org/licenses/by/4.0/
), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(
http://creativecommons.org/publicdomain/zero/1.0/
) applies to the data made available in this article, unless otherwise stated.
Open Access
This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (
http://creativecommons.org/licenses/by/4.0/
), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(
http://creativecommons.org/publicdomain/zero/1.0/
) applies to the data made available in this article, unless otherwise stated.
Sponsor
Commission of the European Communities
Grant Number
687795
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Neurosciences
Rehabilitation
Engineering
Neurosciences & Neurology
Prosthetic control
Myoelectric signal processing
Regression
Online performance
Autoencoding
PROPORTIONAL MYOELECTRIC CONTROL
UPPER-LIMB PROSTHESES
INTRAMUSCULAR EMG
PATTERN-RECOGNITION
FORCE ESTIMATION
REAL-TIME
REGRESSION
FREEDOM
SYSTEMS
SURFACE
0903 Biomedical Engineering
1109 Neurosciences
Publication Status
Published
Article Number
21