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

Content Provider	IEEE Xplore Digital Library
Author	Jianwei Liu Jiayuan He Xinjun Sheng Dingguo Zhang Xiangyang Zhu
Copyright Year	2013
Description	Author affiliation: Sch. of Mech. Eng., Shanghai Jiao Tong Univ., Shanghai, China (Jianwei Liu; Jiayuan He; Xinjun Sheng; Dingguo Zhang; Xiangyang Zhu)
Abstract	The feature extraction is an important step to achieve multifunctional prosthetic control based on surface electromyography (sEMG) pattern recognition. In this study, we propose a new sEMG feature extraction method which is based on autoregressive power spectrum (ARPS). An experiment with a task containing thirteen motion classes was developed to examine the effectiveness of this method. The results show that the new feature, ARPS, has better performance comparing with other two frequently used features, the time domain set (TDS) and autoregressive coefficients (ARC). The ARPS obtains the highest separability index (SI)-a metric measuring the discriminative ability of the sEMG feature. And the average classification errors of ARPS, TDS and ARC are 5.00%, 8.43% and 6.39% respectively. This suggests that the ARPS is suitable for the sEMG pattern recognition.
Starting Page	5746
Ending Page	5749
File Size	509005
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610856
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	Feature extraction Pattern recognition Wrist Biomedical engineering Mathematical model Prosthetics Electromyography
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

Residuals of autoregressive model providing additional information for feature extraction of pattern recognition-based myoelectric control

Enhancing classification accuracy of wrist movement by denoising sEMG signals

Classification of hand movements in amputated subjects by sEMG and accelerometers

Improving myoelectric pattern recognition positional robustness using advanced training protocols

Improving EMG based classification of basic hand movements using EMD

Feature extraction and classification of sEMG signals applied to a virtual hand prosthesis

Hidden Markov Multivariate Autoregressive (HMM-mAR) Modeling Framework for Surface Electromyography (sEMG) Data

Effects of non-training movements on the performance of motion classification in electromyography pattern recognition

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

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

Similar Documents

Residuals of autoregressive model providing additional information for feature extraction of pattern recognition-based myoelectric control

Enhancing classification accuracy of wrist movement by denoising sEMG signals

Classification of hand movements in amputated subjects by sEMG and accelerometers

Improving myoelectric pattern recognition positional robustness using advanced training protocols

Improving EMG based classification of basic hand movements using EMD

Feature extraction and classification of sEMG signals applied to a virtual hand prosthesis

Hidden Markov Multivariate Autoregressive (HMM-mAR) Modeling Framework for Surface Electromyography (sEMG) Data

Effects of non-training movements on the performance of motion classification in electromyography pattern recognition

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

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