Next Article in Journal
Formation and Characterization of Various ZnO/SiO2-Stacked Layers for Flexible Micro-Energy Harvesting Devices
Next Article in Special Issue
Analysis of Behavioral Characteristics of Smartphone Addiction Using Data Mining
Previous Article in Journal
Road Vehicles Surroundings Supervision: Onboard Sensors and Communications
Previous Article in Special Issue
Developing a File System Structure to Solve Healthy Big Data Storage and Archiving Problems Using a Distributed File System
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(7), 1126; https://doi.org/10.3390/app8071126

Stacked Sparse Autoencoders for EMG-Based Classification of Hand Motions: A Comparative Multi Day Analyses between Surface and Intramuscular EMG

1
Department of Robotics & Artificial Intelligence, School of Mechanical & Manufacturing Engineering, National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan
2
Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, 9200 Aalborg, Denmark
3
Center for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand
4
Department of Computer Science, City University of London, London EC1V 0HB, UK
5
Department Bioengineering, Imperial College London, London SW7 2AZ, UK
6
Centre for Robotics Research, Department of Informatics, King’s College London, London WC2G 4BG, UK
*
Author to whom correspondence should be addressed.
Received: 12 June 2018 / Revised: 28 June 2018 / Accepted: 9 July 2018 / Published: 11 July 2018
(This article belongs to the Special Issue Deep Learning and Big Data in Healthcare)
Full-Text   |   PDF [4786 KB, uploaded 11 July 2018]   |  

Abstract

Advances in myoelectric interfaces have increased the use of wearable prosthetics including robotic arms. Although promising results have been achieved with pattern recognition-based control schemes, control robustness requires improvement to increase user acceptance of prosthetic hands. The aim of this study was to quantify the performance of stacked sparse autoencoders (SSAE), an emerging deep learning technique used to improve myoelectric control and to compare multiday surface electromyography (sEMG) and intramuscular (iEMG) recordings. Ten able-bodied and six amputee subjects with average ages of 24.5 and 34.5 years, respectively, were evaluated using offline classification error as the performance matric. Surface and intramuscular EMG were concurrently recorded while each subject performed 11 hand motions. Performance of SSAE was compared with that of linear discriminant analysis (LDA) classifier. Within-day analysis showed that SSAE (1.38 ± 1.38%) outperformed LDA (8.09 ± 4.53%) using both the sEMG and iEMG data from both able-bodied and amputee subjects (p < 0.001). In the between-day analysis, SSAE outperformed LDA (7.19 ± 9.55% vs. 22.25 ± 11.09%) using both sEMG and iEMG data from both able-bodied and amputee subjects. No significant difference in performance was observed for within-day and pairs of days with eight-fold validation when using iEMG and sEMG with SSAE, whereas sEMG outperformed iEMG (p < 0.001) in between-day analysis both with two-fold and seven-fold validation schemes. The results obtained in this study imply that SSAE can significantly improve the performance of pattern recognition-based myoelectric control scheme and has the strength to extract deep information hidden in the EMG data. View Full-Text
Keywords: deep networks; myocontrol; biomedical signal processing; surface EMG; intramuscular EMG; autoencoders deep networks; myocontrol; biomedical signal processing; surface EMG; intramuscular EMG; autoencoders
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zia ur Rehman, M.; Gilani, S.O.; Waris, A.; Niazi, I.K.; Slabaugh, G.; Farina, D.; Kamavuako, E.N. Stacked Sparse Autoencoders for EMG-Based Classification of Hand Motions: A Comparative Multi Day Analyses between Surface and Intramuscular EMG. Appl. Sci. 2018, 8, 1126.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top