NDLI: Classification of hand movements in amputated subjects by sEMG and accelerometers

Content Provider	IEEE Xplore Digital Library
Author	Atzori, M. Gijsberts, A. Muller, H. Caputo, B.
Copyright Year	2014
Description	Author affiliation: Univ. of Rome La Sapienza, Rome, Italy (Caputo, B.) \|\| Inst. de Rech. Idiap, Martigny, Switzerland (Gijsberts, A.) \|\| Inf. Syst. Inst., Univ. of Appl. Sci. Western Switzerland, Sierre, Switzerland (Atzori, M.; Muller, H.)
Abstract	Numerous recent studies have aimed to improve myoelectric control of prostheses. However, the majority of these studies is characterized by two problems that could be easily fulfilled with recent resources supplied by the scientific literature. First, the majority of these studies use only intact subjects, with the unproved assumption that the results apply equally to amputees. Second, usually only electromyography data are used, despite other sensors (e.g., accelerometers) being easy to include into a real life prosthesis control system. In this paper we analyze the mentioned problems by the classification of 40 hand movements in 5 amputated and 40 intact subjects, using both sEMG and accelerometry data and applying several different state of the art methods. The datasets come from the NinaPro database, which supplies publicly available sEMG data to develop and test machine learning algorithms for prosthetics. The number of subjects can seem small at first sight, but it is not considering the literature of the field (which has to face the difficulty of recruiting trans-radial hand amputated subjects). Our results indicate that the maximum average classification accuracy for amputated subjects is 61.14%, which is just 15.86% less than intact subjects, and they show that intact subjects results can be used as proxy measure for amputated subjects. Finally, our comparison shows that accelerometry as a modality is less affected by amputation than electromyography, suggesting that real life prosthetics performance may easily be improved by inclusion of accelerometers.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	3545
Ending Page	3549
File Size	837397
Page Count	5
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944388
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Electromyography Accelerometers Prosthetics Feature extraction Kernel Conferences
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Recognition of forearm muscle activity by continuous classification of multi-site mechanomyogram signals

Natural control capabilities of robotic hands by hand amputated subjects

A new feature extraction method based on autoregressive power spectrum for improving sEMG classification

Combined use of sEMG and accelerometer in hand motion classification considering forearm rotation

A multi-modal approach for hand motion classification using surface EMG and accelerometers

Task discrimination for non-weight-bearing movements using muscle synergies

Processing of surface EMG through pattern recognition techniques aimed at classifying shoulder joint movements

Improving transient state myoelectric signal recognition in hand movement classification using gyroscopes

On the challenge of classifying 52 hand movements from surface electromyography

Classification of hand movements in amputated subjects by sEMG and accelerometers

Similar Documents

Recognition of forearm muscle activity by continuous classification of multi-site mechanomyogram signals

Natural control capabilities of robotic hands by hand amputated subjects

A new feature extraction method based on autoregressive power spectrum for improving sEMG classification

Combined use of sEMG and accelerometer in hand motion classification considering forearm rotation

A multi-modal approach for hand motion classification using surface EMG and accelerometers

Task discrimination for non-weight-bearing movements using muscle synergies

Processing of surface EMG through pattern recognition techniques aimed at classifying shoulder joint movements

Improving transient state myoelectric signal recognition in hand movement classification using gyroscopes

On the challenge of classifying 52 hand movements from surface electromyography

Classification of hand movements in amputated subjects by sEMG and accelerometers