NDLI: Prosthesis-guided training of pattern recognition-controlled myoelectric prosthesis

Content Provider	IEEE Xplore Digital Library
Author	Chicoine, C.L. Simon, A.M. Hargrove, L.J.
Copyright Year	2012
Description	Author affiliation: Center for Bionic Med., Rehabilitation Inst. of Chicago, Chicago, IL, USA (Chicoine, C.L.; Simon, A.M.; Hargrove, L.J.)
Abstract	Pattern recognition can provide intuitive control of myoelectric prostheses. Currently, screen-guided training (SGT), in which individuals perform specific muscle contractions in sync with prompts displayed on a screen, is the common method of collecting the electromyography (EMG) data necessary to train a pattern recognition classifier. Prosthesis-guided training (PGT) is a new data collection method that requires no additional hardware and allows the individuals to keep their focus on the prosthesis itself. The movement of the prosthesis provides the cues of when to perform the muscle contractions. This study compared the training data obtained from SGT and PGT and evaluated user performance after training pattern recognition classifiers with each method. Although the inclusion of transient EMG signal in PGT data led to decreased accuracy of the classifier, subjects completed a performance task faster than when compared to using a classifier built from SGT data. This may indicate that training data collected using PGT that includes both steady state and transient EMG signals generates a classifier that more accurately reflects muscle activity during real-time use of a pattern recognition-controlled myoelectric prosthesis.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	1876
Ending Page	1879
File Size	554443
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6346318
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prosthetics Electromyography Muscles Pattern recognition Transient analysis Training Steady-state
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

Adapting proportional myoelectric-controlled interfaces for prosthetic hands

Artificial proprioception for myoelectric control

Optimizing pattern recognition-based control for partial-hand prosthesis application

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

Improving myoelectric pattern recognition positional robustness using advanced training protocols

Acquisition of myoelectric signals to control a hand prosthesis with implantable epimysial electrodes

Protocol for site selection and movement assessment for the myoelectric control of a multi-functional upper-limb prosthesis

Influence of the weight actions of the hand prosthesis on the performance of pattern recognition based myoelectric control: Preliminary study

Development of a wireless electromyographically controlled electrolarynx voice prosthesis

Prosthesis-guided training of pattern recognition-controlled myoelectric prosthesis

Similar Documents

Adapting proportional myoelectric-controlled interfaces for prosthetic hands

Artificial proprioception for myoelectric control

Optimizing pattern recognition-based control for partial-hand prosthesis application

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

Improving myoelectric pattern recognition positional robustness using advanced training protocols

Acquisition of myoelectric signals to control a hand prosthesis with implantable epimysial electrodes

Protocol for site selection and movement assessment for the myoelectric control of a multi-functional upper-limb prosthesis

Influence of the weight actions of the hand prosthesis on the performance of pattern recognition based myoelectric control: Preliminary study

Development of a wireless electromyographically controlled electrolarynx voice prosthesis

Prosthesis-guided training of pattern recognition-controlled myoelectric prosthesis