NDLI: Real-time implementation of a self-recovery EMG pattern recognition interface for artificial arms

Content Provider	IEEE Xplore Digital Library
Author	Xiaorong Zhang He Huang Qing Yang
Copyright Year	2013
Description	Author affiliation: Dept. of Electr., Comput., & Biomed. Eng., Univ. of Rhode Island, Kingston, RI, USA (Xiaorong Zhang; He Huang; Qing Yang)
Abstract	EMG pattern classification has been widely studied for decoding user intent for intuitive prosthesis control. However, EMG signals can be easily contaminated by noise and disturbances, which may degrade the classification performance. This study aims to design a real-time self-recovery EMG pattern classification interface to provide reliable user intent recognition for multifunctional prosthetic arm control. A novel self-recovery module consisting of multiple sensor fault detectors and a fast LDA classifier retraining strategy has been developed to immediately recover the classification performance from signal disturbances. The self-recovery EMG pattern recognition (PR) system has been implemented on an embedded system as a working prototype. Experimental evaluation has been performed on an able-bodied subject in real-time to classify three arm movements while signal disturbances were manually introduced. The results of this study may propel the clinical use of EMG PR for multifunctional prosthetic arm control.
Starting Page	5926
Ending Page	5929
File Size	270779
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610901
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electromyography Real-time systems Pattern recognition Algorithm design and analysis Feature extraction Classification algorithms Training
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

An open and configurable embedded system for EMG pattern recognition implementation for artificial arms

Classification of EMG signals using artificial neural networks for virtual hand prosthesis control

Surface EMG classification during dynamic contractions for multifunction transradial prostheses

Artifact removal algorithm for an EMG-based Silent Speech Interface

Classification of multichannel uterine EMG signals

Forearm motion discrimination technique using real-time EMG signals

Improving EMG based classification of basic hand movements using EMD

Motion discrimination technique by EMG signals using hyper-sphere model

A special purpose embedded system for neural machine interface for artificial legs

Real-time implementation of a self-recovery EMG pattern recognition interface for artificial arms

Similar Documents

An open and configurable embedded system for EMG pattern recognition implementation for artificial arms

Classification of EMG signals using artificial neural networks for virtual hand prosthesis control

Surface EMG classification during dynamic contractions for multifunction transradial prostheses

Artifact removal algorithm for an EMG-based Silent Speech Interface

Classification of multichannel uterine EMG signals

Forearm motion discrimination technique using real-time EMG signals

Improving EMG based classification of basic hand movements using EMD

Motion discrimination technique by EMG signals using hyper-sphere model

A special purpose embedded system for neural machine interface for artificial legs

Real-time implementation of a self-recovery EMG pattern recognition interface for artificial arms