Synergy-Based Estimation of Balance Condition During Walking Tests
In the area of human-machine interface research, the continuous estimation of the Center of Pressure (COP) in the human body can assess users' balance conditions, thereby effectively enhancing the safety and diversity of studies. This paper aims to present a novel method for continuous synergy-...
Gespeichert in:
Veröffentlicht in: | IEEE transactions on neural systems and rehabilitation engineering 2024-01, Vol.32, p.4063-4075 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 4075 |
---|---|
container_issue | |
container_start_page | 4063 |
container_title | IEEE transactions on neural systems and rehabilitation engineering |
container_volume | 32 |
creator | Li, Kaitai Wang, Heyuan Ye, Xuesong Zhou, Congcong |
description | In the area of human-machine interface research, the continuous estimation of the Center of Pressure (COP) in the human body can assess users' balance conditions, thereby effectively enhancing the safety and diversity of studies. This paper aims to present a novel method for continuous synergy-based estimation of human balance states during walking, and simultaneously analyze the impact of various factors on the estimation results. Specifically, we introduce muscle synergy coherence features and analyze the variations of these features in different balance conditions. Furthermore, we fuse temporal features extracted by a bidirectional long short-term memory (BILSTM) network with spatial features derived from the analysis of muscle synergy coherence to continuously estimate the mediolateral COP and Ground Reaction Force (GRF) during human walking tests. Then, we analyze the influence of different electromechanical delay compensation (EMD) time, the number of synergies, and different walking speeds on the estimation results. Finally, we validate the estimation capability of the proposed method on data collected in real-world walking tests. The results indicate a significant correlation between the proposed muscle synergy coherence features and balance conditions. The network structure combining muscle synergy coherence features and BILSTM features enables accurate continuous estimation of COP ( \mathbf {R}^{\mathbf {{2}}}= \,\, 0.87~\pm ~0.07 ) and GRF ( \mathbf {R}^{\mathbf {{2}}}= \,\, 0.83~\pm ~0.09 ) during walking tests. Our research introduces a novel approach to the continuous estimation of balance conditions in human walking, with potential implications in various applications within human-machine engineering, such as exoskeletons and prosthetics. |
doi_str_mv | 10.1109/TNSRE.2024.3495530 |
format | Article |
fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_ieee_primary_10749971</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10749971</ieee_id><doaj_id>oai_doaj_org_article_6f3267b859fb428386050d709ae1a76a</doaj_id><sourcerecordid>3128749494</sourcerecordid><originalsourceid>FETCH-LOGICAL-c271t-ed58cd2e10cecb26c744d8ba71d354177b7029ebee95471a2f2b0863a80a5593</originalsourceid><addsrcrecordid>eNpNkU1rGzEQhkVoaNK0fyCUsMde1hl9raRj7LptIKTQGHoUWmnWbLpepdL64H9f-aMhzGGG4Z1nNHoJuaYwoxTM7erx6ddyxoCJGRdGSg5n5JJKqWtgFN7tay5qwRlckA85PwNQ1Uj1nlxwI5kSjF2S-dNuxLTe1XOXMVTLPPUbN_VxrGJXzd3gRo_VIo6hPzS_blM_rqvfbvizzyvMU_5Izjs3ZPx0yldk9W25WvyoH35-v1_cPdSeKTrVGKT2gSEFj75ljVdCBN06RQOXgirVKmAGW0QjhaKOdawF3XCnwUlp-BW5P2JDdM_2JZV3pp2NrreHRkxr69LU-wFt03HWqFZL07WCaa4bkBAUGIfUqcYV1pcj6yXFv9tyhN302eNQzsW4zZZTppUwJYqUHaU-xZwTdq-rKdi9DfZgg93bYE82lKGbE3_bbjC8jvz_9yL4fBT0iPiGWJYaRfk_geSKXA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3128749494</pqid></control><display><type>article</type><title>Synergy-Based Estimation of Balance Condition During Walking Tests</title><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Li, Kaitai ; Wang, Heyuan ; Ye, Xuesong ; Zhou, Congcong</creator><creatorcontrib>Li, Kaitai ; Wang, Heyuan ; Ye, Xuesong ; Zhou, Congcong</creatorcontrib><description><![CDATA[In the area of human-machine interface research, the continuous estimation of the Center of Pressure (COP) in the human body can assess users' balance conditions, thereby effectively enhancing the safety and diversity of studies. This paper aims to present a novel method for continuous synergy-based estimation of human balance states during walking, and simultaneously analyze the impact of various factors on the estimation results. Specifically, we introduce muscle synergy coherence features and analyze the variations of these features in different balance conditions. Furthermore, we fuse temporal features extracted by a bidirectional long short-term memory (BILSTM) network with spatial features derived from the analysis of muscle synergy coherence to continuously estimate the mediolateral COP and Ground Reaction Force (GRF) during human walking tests. Then, we analyze the influence of different electromechanical delay compensation (EMD) time, the number of synergies, and different walking speeds on the estimation results. Finally, we validate the estimation capability of the proposed method on data collected in real-world walking tests. The results indicate a significant correlation between the proposed muscle synergy coherence features and balance conditions. The network structure combining muscle synergy coherence features and BILSTM features enables accurate continuous estimation of COP (<inline-formula> <tex-math notation="LaTeX">\mathbf {R}^{\mathbf {{2}}}= \,\, 0.87~\pm ~0.07 </tex-math></inline-formula>) and GRF (<inline-formula> <tex-math notation="LaTeX">\mathbf {R}^{\mathbf {{2}}}= \,\, 0.83~\pm ~0.09 </tex-math></inline-formula>) during walking tests. Our research introduces a novel approach to the continuous estimation of balance conditions in human walking, with potential implications in various applications within human-machine engineering, such as exoskeletons and prosthetics.]]></description><identifier>ISSN: 1534-4320</identifier><identifier>ISSN: 1558-0210</identifier><identifier>EISSN: 1558-0210</identifier><identifier>DOI: 10.1109/TNSRE.2024.3495530</identifier><identifier>PMID: 39527422</identifier><identifier>CODEN: ITNSB3</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>balance condition ; center of pressure ; Coherence ; coherence analysis ; Electrodes ; Estimation ; Feature extraction ; Force ; Kinematics ; Legged locomotion ; Muscle synergy ; Muscles ; Time series analysis ; Time-frequency analysis ; time-series estimation</subject><ispartof>IEEE transactions on neural systems and rehabilitation engineering, 2024-01, Vol.32, p.4063-4075</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0001-8397-1491 ; 0000-0002-3439-3733</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,2102,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39527422$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Kaitai</creatorcontrib><creatorcontrib>Wang, Heyuan</creatorcontrib><creatorcontrib>Ye, Xuesong</creatorcontrib><creatorcontrib>Zhou, Congcong</creatorcontrib><title>Synergy-Based Estimation of Balance Condition During Walking Tests</title><title>IEEE transactions on neural systems and rehabilitation engineering</title><addtitle>TNSRE</addtitle><addtitle>IEEE Trans Neural Syst Rehabil Eng</addtitle><description><![CDATA[In the area of human-machine interface research, the continuous estimation of the Center of Pressure (COP) in the human body can assess users' balance conditions, thereby effectively enhancing the safety and diversity of studies. This paper aims to present a novel method for continuous synergy-based estimation of human balance states during walking, and simultaneously analyze the impact of various factors on the estimation results. Specifically, we introduce muscle synergy coherence features and analyze the variations of these features in different balance conditions. Furthermore, we fuse temporal features extracted by a bidirectional long short-term memory (BILSTM) network with spatial features derived from the analysis of muscle synergy coherence to continuously estimate the mediolateral COP and Ground Reaction Force (GRF) during human walking tests. Then, we analyze the influence of different electromechanical delay compensation (EMD) time, the number of synergies, and different walking speeds on the estimation results. Finally, we validate the estimation capability of the proposed method on data collected in real-world walking tests. The results indicate a significant correlation between the proposed muscle synergy coherence features and balance conditions. The network structure combining muscle synergy coherence features and BILSTM features enables accurate continuous estimation of COP (<inline-formula> <tex-math notation="LaTeX">\mathbf {R}^{\mathbf {{2}}}= \,\, 0.87~\pm ~0.07 </tex-math></inline-formula>) and GRF (<inline-formula> <tex-math notation="LaTeX">\mathbf {R}^{\mathbf {{2}}}= \,\, 0.83~\pm ~0.09 </tex-math></inline-formula>) during walking tests. Our research introduces a novel approach to the continuous estimation of balance conditions in human walking, with potential implications in various applications within human-machine engineering, such as exoskeletons and prosthetics.]]></description><subject>balance condition</subject><subject>center of pressure</subject><subject>Coherence</subject><subject>coherence analysis</subject><subject>Electrodes</subject><subject>Estimation</subject><subject>Feature extraction</subject><subject>Force</subject><subject>Kinematics</subject><subject>Legged locomotion</subject><subject>Muscle synergy</subject><subject>Muscles</subject><subject>Time series analysis</subject><subject>Time-frequency analysis</subject><subject>time-series estimation</subject><issn>1534-4320</issn><issn>1558-0210</issn><issn>1558-0210</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkU1rGzEQhkVoaNK0fyCUsMde1hl9raRj7LptIKTQGHoUWmnWbLpepdL64H9f-aMhzGGG4Z1nNHoJuaYwoxTM7erx6ddyxoCJGRdGSg5n5JJKqWtgFN7tay5qwRlckA85PwNQ1Uj1nlxwI5kSjF2S-dNuxLTe1XOXMVTLPPUbN_VxrGJXzd3gRo_VIo6hPzS_blM_rqvfbvizzyvMU_5Izjs3ZPx0yldk9W25WvyoH35-v1_cPdSeKTrVGKT2gSEFj75ljVdCBN06RQOXgirVKmAGW0QjhaKOdawF3XCnwUlp-BW5P2JDdM_2JZV3pp2NrreHRkxr69LU-wFt03HWqFZL07WCaa4bkBAUGIfUqcYV1pcj6yXFv9tyhN302eNQzsW4zZZTppUwJYqUHaU-xZwTdq-rKdi9DfZgg93bYE82lKGbE3_bbjC8jvz_9yL4fBT0iPiGWJYaRfk_geSKXA</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Li, Kaitai</creator><creator>Wang, Heyuan</creator><creator>Ye, Xuesong</creator><creator>Zhou, Congcong</creator><general>IEEE</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8397-1491</orcidid><orcidid>https://orcid.org/0000-0002-3439-3733</orcidid></search><sort><creationdate>20240101</creationdate><title>Synergy-Based Estimation of Balance Condition During Walking Tests</title><author>Li, Kaitai ; Wang, Heyuan ; Ye, Xuesong ; Zhou, Congcong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c271t-ed58cd2e10cecb26c744d8ba71d354177b7029ebee95471a2f2b0863a80a5593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>balance condition</topic><topic>center of pressure</topic><topic>Coherence</topic><topic>coherence analysis</topic><topic>Electrodes</topic><topic>Estimation</topic><topic>Feature extraction</topic><topic>Force</topic><topic>Kinematics</topic><topic>Legged locomotion</topic><topic>Muscle synergy</topic><topic>Muscles</topic><topic>Time series analysis</topic><topic>Time-frequency analysis</topic><topic>time-series estimation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Kaitai</creatorcontrib><creatorcontrib>Wang, Heyuan</creatorcontrib><creatorcontrib>Ye, Xuesong</creatorcontrib><creatorcontrib>Zhou, Congcong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE transactions on neural systems and rehabilitation engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Kaitai</au><au>Wang, Heyuan</au><au>Ye, Xuesong</au><au>Zhou, Congcong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Synergy-Based Estimation of Balance Condition During Walking Tests</atitle><jtitle>IEEE transactions on neural systems and rehabilitation engineering</jtitle><stitle>TNSRE</stitle><addtitle>IEEE Trans Neural Syst Rehabil Eng</addtitle><date>2024-01-01</date><risdate>2024</risdate><volume>32</volume><spage>4063</spage><epage>4075</epage><pages>4063-4075</pages><issn>1534-4320</issn><issn>1558-0210</issn><eissn>1558-0210</eissn><coden>ITNSB3</coden><abstract><![CDATA[In the area of human-machine interface research, the continuous estimation of the Center of Pressure (COP) in the human body can assess users' balance conditions, thereby effectively enhancing the safety and diversity of studies. This paper aims to present a novel method for continuous synergy-based estimation of human balance states during walking, and simultaneously analyze the impact of various factors on the estimation results. Specifically, we introduce muscle synergy coherence features and analyze the variations of these features in different balance conditions. Furthermore, we fuse temporal features extracted by a bidirectional long short-term memory (BILSTM) network with spatial features derived from the analysis of muscle synergy coherence to continuously estimate the mediolateral COP and Ground Reaction Force (GRF) during human walking tests. Then, we analyze the influence of different electromechanical delay compensation (EMD) time, the number of synergies, and different walking speeds on the estimation results. Finally, we validate the estimation capability of the proposed method on data collected in real-world walking tests. The results indicate a significant correlation between the proposed muscle synergy coherence features and balance conditions. The network structure combining muscle synergy coherence features and BILSTM features enables accurate continuous estimation of COP (<inline-formula> <tex-math notation="LaTeX">\mathbf {R}^{\mathbf {{2}}}= \,\, 0.87~\pm ~0.07 </tex-math></inline-formula>) and GRF (<inline-formula> <tex-math notation="LaTeX">\mathbf {R}^{\mathbf {{2}}}= \,\, 0.83~\pm ~0.09 </tex-math></inline-formula>) during walking tests. Our research introduces a novel approach to the continuous estimation of balance conditions in human walking, with potential implications in various applications within human-machine engineering, such as exoskeletons and prosthetics.]]></abstract><cop>United States</cop><pub>IEEE</pub><pmid>39527422</pmid><doi>10.1109/TNSRE.2024.3495530</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-8397-1491</orcidid><orcidid>https://orcid.org/0000-0002-3439-3733</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1534-4320 |
ispartof | IEEE transactions on neural systems and rehabilitation engineering, 2024-01, Vol.32, p.4063-4075 |
issn | 1534-4320 1558-0210 1558-0210 |
language | eng |
recordid | cdi_ieee_primary_10749971 |
source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | balance condition center of pressure Coherence coherence analysis Electrodes Estimation Feature extraction Force Kinematics Legged locomotion Muscle synergy Muscles Time series analysis Time-frequency analysis time-series estimation |
title | Synergy-Based Estimation of Balance Condition During Walking Tests |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T22%3A18%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Synergy-Based%20Estimation%20of%20Balance%20Condition%20During%20Walking%20Tests&rft.jtitle=IEEE%20transactions%20on%20neural%20systems%20and%20rehabilitation%20engineering&rft.au=Li,%20Kaitai&rft.date=2024-01-01&rft.volume=32&rft.spage=4063&rft.epage=4075&rft.pages=4063-4075&rft.issn=1534-4320&rft.eissn=1558-0210&rft.coden=ITNSB3&rft_id=info:doi/10.1109/TNSRE.2024.3495530&rft_dat=%3Cproquest_ieee_%3E3128749494%3C/proquest_ieee_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3128749494&rft_id=info:pmid/39527422&rft_ieee_id=10749971&rft_doaj_id=oai_doaj_org_article_6f3267b859fb428386050d709ae1a76a&rfr_iscdi=true |