Wavelet transform based EMG feature extraction and evaluation using scatter graphs

In the hand movement recognition system the most important step is feature extraction. Nowadays, the analysis of Electromyograhy signal using wavelet transform becoming the most powerful method. In this paper we have typically used the mathematical diagram tool i.e. scatter graph technique to evalua...

Full description

Saved in:
Bibliographic Details
Main Authors: Lolure, Amol, Thool, V. R.
Format: Conference Proceeding
Language:English
Subjects:
Bandwidth
Discrete wavelet transforms
Electromyograph
Electromyography
feature extraction
scatter graph
Thumb
wavelet transform
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 1277
container_issue
container_start_page 1273
container_title
container_volume
creator Lolure, Amol
Thool, V. R.
description In the hand movement recognition system the most important step is feature extraction. Nowadays, the analysis of Electromyograhy signal using wavelet transform becoming the most powerful method. In this paper we have typically used the mathematical diagram tool i.e. scatter graph technique to evaluate the performance of EMG features. The EMG signal corresponding to the different hand movements and finger movements are considered. Various features that are widely used are extracted from the different wavelet coefficient. The graphs obtained for MAV(Mean Absolute Value) from the reconstructed coefficient shows the better performance.
doi_str_mv 10.1109/IIC.2015.7150944
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_7150944</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7150944</ieee_id><sourcerecordid>7150944</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-31de1aa4a5c19264a1c200ce06f376d6270fb46d0756a66c3380338079508ab63</originalsourceid><addsrcrecordid>eNotj01Lw0AURceFoNbuBTfzBxLfy3x1llJqDVQEUVyWl-SlRtK0zEyK_nutdnG5HC4cuELcIOSI4O_Kcp4XgCZ3aMBrfSauUDvvHVoDF2Ia4ycAoNcOFF6Kl3c6cM9JpkBDbHdhKyuK3MjF01K2TGkMLPnrd61TtxskDY3kA_Uj_eEYu2EjY00pcZCbQPuPeC3OW-ojT089EW8Pi9f5Y7Z6Xpbz-1XWoTMpU9gwEmkyNfrCasK6AKgZbKucbWzhoK20bcAZS9bWSs3gGOcNzKiyaiJu_70dM6_3odtS-F6fbqsfyA9NSA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Wavelet transform based EMG feature extraction and evaluation using scatter graphs</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Lolure, Amol ; Thool, V. R.</creator><creatorcontrib>Lolure, Amol ; Thool, V. R.</creatorcontrib><description>In the hand movement recognition system the most important step is feature extraction. Nowadays, the analysis of Electromyograhy signal using wavelet transform becoming the most powerful method. In this paper we have typically used the mathematical diagram tool i.e. scatter graph technique to evaluate the performance of EMG features. The EMG signal corresponding to the different hand movements and finger movements are considered. Various features that are widely used are extracted from the different wavelet coefficient. The graphs obtained for MAV(Mean Absolute Value) from the reconstructed coefficient shows the better performance.</description><identifier>EISBN: 1479971650</identifier><identifier>EISBN: 9781479971657</identifier><identifier>DOI: 10.1109/IIC.2015.7150944</identifier><language>eng</language><publisher>IEEE</publisher><subject>Bandwidth ; Discrete wavelet transforms ; Electromyograph ; Electromyography ; feature extraction ; scatter graph ; Thumb ; wavelet transform</subject><ispartof>2015 International Conference on Industrial Instrumentation and Control (ICIC), 2015, p.1273-1277</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7150944$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7150944$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lolure, Amol</creatorcontrib><creatorcontrib>Thool, V. R.</creatorcontrib><title>Wavelet transform based EMG feature extraction and evaluation using scatter graphs</title><title>2015 International Conference on Industrial Instrumentation and Control (ICIC)</title><addtitle>IIC</addtitle><description>In the hand movement recognition system the most important step is feature extraction. Nowadays, the analysis of Electromyograhy signal using wavelet transform becoming the most powerful method. In this paper we have typically used the mathematical diagram tool i.e. scatter graph technique to evaluate the performance of EMG features. The EMG signal corresponding to the different hand movements and finger movements are considered. Various features that are widely used are extracted from the different wavelet coefficient. The graphs obtained for MAV(Mean Absolute Value) from the reconstructed coefficient shows the better performance.</description><subject>Bandwidth</subject><subject>Discrete wavelet transforms</subject><subject>Electromyograph</subject><subject>Electromyography</subject><subject>feature extraction</subject><subject>scatter graph</subject><subject>Thumb</subject><subject>wavelet transform</subject><isbn>1479971650</isbn><isbn>9781479971657</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2015</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj01Lw0AURceFoNbuBTfzBxLfy3x1llJqDVQEUVyWl-SlRtK0zEyK_nutdnG5HC4cuELcIOSI4O_Kcp4XgCZ3aMBrfSauUDvvHVoDF2Ia4ycAoNcOFF6Kl3c6cM9JpkBDbHdhKyuK3MjF01K2TGkMLPnrd61TtxskDY3kA_Uj_eEYu2EjY00pcZCbQPuPeC3OW-ojT089EW8Pi9f5Y7Z6Xpbz-1XWoTMpU9gwEmkyNfrCasK6AKgZbKucbWzhoK20bcAZS9bWSs3gGOcNzKiyaiJu_70dM6_3odtS-F6fbqsfyA9NSA</recordid><startdate>201505</startdate><enddate>201505</enddate><creator>Lolure, Amol</creator><creator>Thool, V. R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201505</creationdate><title>Wavelet transform based EMG feature extraction and evaluation using scatter graphs</title><author>Lolure, Amol ; Thool, V. R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-31de1aa4a5c19264a1c200ce06f376d6270fb46d0756a66c3380338079508ab63</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Bandwidth</topic><topic>Discrete wavelet transforms</topic><topic>Electromyograph</topic><topic>Electromyography</topic><topic>feature extraction</topic><topic>scatter graph</topic><topic>Thumb</topic><topic>wavelet transform</topic><toplevel>online_resources</toplevel><creatorcontrib>Lolure, Amol</creatorcontrib><creatorcontrib>Thool, V. R.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lolure, Amol</au><au>Thool, V. R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Wavelet transform based EMG feature extraction and evaluation using scatter graphs</atitle><btitle>2015 International Conference on Industrial Instrumentation and Control (ICIC)</btitle><stitle>IIC</stitle><date>2015-05</date><risdate>2015</risdate><spage>1273</spage><epage>1277</epage><pages>1273-1277</pages><eisbn>1479971650</eisbn><eisbn>9781479971657</eisbn><abstract>In the hand movement recognition system the most important step is feature extraction. Nowadays, the analysis of Electromyograhy signal using wavelet transform becoming the most powerful method. In this paper we have typically used the mathematical diagram tool i.e. scatter graph technique to evaluate the performance of EMG features. The EMG signal corresponding to the different hand movements and finger movements are considered. Various features that are widely used are extracted from the different wavelet coefficient. The graphs obtained for MAV(Mean Absolute Value) from the reconstructed coefficient shows the better performance.</abstract><pub>IEEE</pub><doi>10.1109/IIC.2015.7150944</doi><tpages>5</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISBN: 1479971650
ispartof 2015 International Conference on Industrial Instrumentation and Control (ICIC), 2015, p.1273-1277
issn
language eng
recordid cdi_ieee_primary_7150944
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Bandwidth
Discrete wavelet transforms
Electromyograph
Electromyography
feature extraction
scatter graph
Thumb
wavelet transform
title Wavelet transform based EMG feature extraction and evaluation using scatter graphs
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T03%3A07%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Wavelet%20transform%20based%20EMG%20feature%20extraction%20and%20evaluation%20using%20scatter%20graphs&rft.btitle=2015%20International%20Conference%20on%20Industrial%20Instrumentation%20and%20Control%20(ICIC)&rft.au=Lolure,%20Amol&rft.date=2015-05&rft.spage=1273&rft.epage=1277&rft.pages=1273-1277&rft_id=info:doi/10.1109/IIC.2015.7150944&rft.eisbn=1479971650&rft.eisbn_list=9781479971657&rft_dat=%3Cieee_6IE%3E7150944%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-31de1aa4a5c19264a1c200ce06f376d6270fb46d0756a66c3380338079508ab63%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=7150944&rfr_iscdi=true