MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation

<!DOCTYPE html> <html lang="en" dir="ltr"> <head> <meta charset="utf-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="google-site-verification" content="5fPGCLllnWrvFxH9QWI0l1TadV7byeEvfPcyK2VkS_s"/> <meta name="google-site-verification" content="Rp5zp04IKW-s1IbpTOGB7Z6XY60oloZD5C3kTM-AiY4"/> <meta name="generator" content="InvenioRDM 13.0"/> <meta name="robots" content="noindex, nofollow"> <meta name="description" content="This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process." /> <meta name="citation_title" content="MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation" /> <meta name="citation_doi" content="10.5281/zenodo.1340176" /> <meta name="citation_abstract_html_url" content="https://zenodo.org/records/1340176" /> <meta property="og:title" content="MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation" /> <meta property="og:description" content="This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process." /> <meta property="og:url" content="https://zenodo.org/records/1340176" /> <meta property="og:site_name" content="Zenodo" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:site" content="@zenodo_org" /> <meta name="twitter:title" content="MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation" /> <meta name="twitter:description" content="This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process." /> <meta name="citation_pdf_url" content="https://zenodo.org/records/1340176/files/10006973.pdf"/> <link rel="alternate" type="application/pdf" href="https://zenodo.org/records/1340176/files/10006973.pdf"> <link rel="canonical" href="https://zenodo.org/records/1340176"> <title>MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation</title> <link rel="shortcut icon" type="image/x-icon" href="/static/favicon.ico"/> <link rel="apple-touch-icon" sizes="120x120" href="/static/apple-touch-icon-120.png"/> <link rel="apple-touch-icon" sizes="152x152" href="/static/apple-touch-icon-152.png"/> <link rel="apple-touch-icon" sizes="167x167" href="/static/apple-touch-icon-167.png"/> <link rel="apple-touch-icon" sizes="180x180" 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class="ui label horizontal small neutral mb-5" aria-label="Resource type" > Journal article </span> <span role="note" class="ui label horizontal small access-status open mb-5" data-tooltip="The record and files are publicly accessible." data-inverted="" aria-label="Access status" > <i class="icon unlock" aria-hidden="true"></i> <span aria-label="The record and files are publicly accessible."> Open </span> </span> </div> </div> </div> </section> <div class="ui divider hidden"></div><section id="record-title-section" aria-label="Record title and creators"> <h1 id="record-title" class="wrap-overflowing-text">MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation</h1> <section id="creatibutors" aria-label="Creators and contributors"> <div class="ui grid"> <div class="row ui accordion affiliations"> <div class="sixteen wide mobile twelve wide tablet thirteen wide computer column"> <h3 class="sr-only">Creators</h3> <ul class="creatibutors"> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22Alexandros+Lioulemes%22" > <span class="creatibutor-name">Alexandros Lioulemes</span></a> </li> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22Michail+Theofanidis%22" > <span class="creatibutor-name">Michail Theofanidis</span></a> </li> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22Varun+Kanal%22" > <span class="creatibutor-name">Varun Kanal</span></a> </li> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22Konstantinos+Tsiakas%22" > <span class="creatibutor-name">Konstantinos Tsiakas</span></a> </li> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22Maher+Abujelala%22" > <span class="creatibutor-name">Maher Abujelala</span></a> </li> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22Chris+Collander%22" > <span class="creatibutor-name">Chris Collander</span></a> </li> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22William+B.+Townsend%22" > <span class="creatibutor-name">William B. Townsend</span></a> </li> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22Angie+Boisselle%22" > <span class="creatibutor-name">Angie Boisselle</span></a> </li> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22Fillia+Makedon%22" > <span class="creatibutor-name">Fillia Makedon</span></a> </li> </ul> </div> </div> </div> </section> </section> <section id="description" class="rel-mt-2 rich-input-content" aria-label="Record description"> <h2 id="description-heading" class="sr-only">Description</h2> <div style="word-wrap: break-word;"> <p>This paper presents a home-based robot-rehabilitation<br> instrument, called ”MAGNI Dynamics”, that utilized a vision-based<br> kinematic/dynamic module and an adaptive haptic feedback<br> controller. The system is expected to provide personalized<br> rehabilitation by adjusting its resistive and supportive behavior<br> according to a fuzzy intelligence controller that acts as an inference<br> system, which correlates the user’s performance to different stiffness<br> factors. The vision module uses the Kinect’s skeletal tracking to<br> monitor the user’s effort in an unobtrusive and safe way, by estimating<br> the torque that affects the user’s arm. The system’s torque estimations<br> are justified by capturing electromyographic data from primitive<br> hand motions (Shoulder Abduction and Shoulder Forward Flexion).<br> Moreover, we present and analyze how the Barrett WAM generates<br> a force-field with a haptic controller to support or challenge the<br> users. Experiments show that by shifting the proportional value,<br> that corresponds to different stiffness factors of the haptic path, can<br> potentially help the user to improve his/her motor skills. Finally,<br> potential areas for future research are discussed, that address how<br> a rehabilitation robotic framework may include multisensing data, to<br> improve the user’s recovery process.</p> </div> </section> <section id="record-files" class="rel-mt-2 rel-mb-3" aria-label="Files" ><h2 id="files-heading">Files</h2> <div class="ui accordion panel mb-10 open" href="#files-preview-accordion-panel"> <h3 class="active title panel-heading open m-0"> <div role="button" id="files-preview-accordion-trigger" aria-controls="files-preview-accordion-panel" aria-expanded="true" tabindex="0" class="trigger" aria-label="File preview" > <span id="preview-file-title">10006973.pdf</span> <i class="angle right icon" aria-hidden="true"></i> </div> </h3> <div role="region" id="files-preview-accordion-panel" aria-labelledby="files-preview-accordion-trigger" class="active content preview-container pt-0 open" > <div> <iframe title="Preview" class="preview-iframe" id="preview-iframe" 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The vision module uses the Kinect\u0026rsquo;s skeletal tracking to\u003cbr\u003e\nmonitor the user\u0026rsquo;s effort in an unobtrusive and safe way, by estimating\u003cbr\u003e\nthe torque that affects the user\u0026rsquo;s arm. The system\u0026rsquo;s torque estimations\u003cbr\u003e\nare justified by capturing electromyographic data from primitive\u003cbr\u003e\nhand motions (Shoulder Abduction and Shoulder Forward Flexion).\u003cbr\u003e\nMoreover, we present and analyze how the Barrett WAM generates\u003cbr\u003e\na force-field with a haptic controller to support or challenge the\u003cbr\u003e\nusers. Experiments show that by shifting the proportional value,\u003cbr\u003e\nthat corresponds to different stiffness factors of the haptic path, can\u003cbr\u003e\npotentially help the user to improve his/her motor skills. Finally,\u003cbr\u003e\npotential areas for future research are discussed, that address how\u003cbr\u003e\na rehabilitation robotic framework may include multisensing data, to\u003cbr\u003e\nimprove the user\u0026rsquo;s recovery process.", "languages": [{"id": "eng", "title": {"en": "English"}}], "publication_date": "2017-03-03", "publisher": "Zenodo", "resource_type": {"id": "publication-article", "title": {"de": "Zeitschriftenartikel", "en": "Journal article"}}, "rights": [{"description": {"en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited."}, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": {"scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode"}, "title": {"en": "Creative Commons Attribution 4.0 International"}}], "title": "MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation", "version": "10006973"}, "parent": {"access": {"owned_by": {"user": "32148"}, "settings": {"accept_conditions_text": null, "allow_guest_requests": false, "allow_user_requests": false, "secret_link_expiration": 0}}, "communities": {}, "id": "1340175", "pids": {"doi": {"client": "datacite", "identifier": "10.5281/zenodo.1340175", "provider": "datacite"}}}, "pids": {"doi": {"client": "datacite", "identifier": "10.5281/zenodo.1340176", "provider": "datacite"}, "oai": {"identifier": "oai:zenodo.org:1340176", "provider": "oai"}}, "revision_id": 7, "stats": {"all_versions": {"data_volume": 23402167.0, "downloads": 37, "unique_downloads": 34, "unique_views": 258, "views": 261}, "this_version": {"data_volume": 23402167.0, "downloads": 37, "unique_downloads": 34, "unique_views": 258, "views": 261}}, "status": "published", "ui": {"access_status": {"description_l10n": "The record and files are publicly accessible.", "embargo_date_l10n": null, "icon": "unlock", "id": "open", "message_class": "", "title_l10n": "Open"}, "created_date_l10n_long": "August 7, 2018", "creators": {"affiliations": [], "creators": [{"person_or_org": {"family_name": "Alexandros Lioulemes", "name": "Alexandros Lioulemes", "type": "personal"}}, {"person_or_org": {"family_name": "Michail Theofanidis", "name": "Michail Theofanidis", "type": "personal"}}, {"person_or_org": {"family_name": "Varun Kanal", "name": "Varun Kanal", "type": "personal"}}, {"person_or_org": {"family_name": "Konstantinos Tsiakas", "name": "Konstantinos Tsiakas", "type": "personal"}}, {"person_or_org": {"family_name": "Maher Abujelala", "name": "Maher Abujelala", "type": "personal"}}, {"person_or_org": {"family_name": "Chris Collander", "name": "Chris Collander", "type": "personal"}}, {"person_or_org": {"family_name": "William B. 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The vision module uses the Kinect\u2019s skeletal tracking to\nmonitor the user\u2019s effort in an unobtrusive and safe way, by estimating\nthe torque that affects the user\u2019s arm. The system\u2019s torque estimations\nare justified by capturing electromyographic data from primitive\nhand motions (Shoulder Abduction and Shoulder Forward Flexion).\nMoreover, we present and analyze how the Barrett WAM generates\na force-field with a haptic controller to support or challenge the\nusers. Experiments show that by shifting the proportional value,\nthat corresponds to different stiffness factors of the haptic path, can\npotentially help the user to improve his/her motor skills. Finally,\npotential areas for future research are discussed, that address how\na rehabilitation robotic framework may include multisensing data, to\nimprove the user\u2019s recovery process.", "is_draft": false, "languages": [{"id": "eng", "title_l10n": "English"}], "publication_date_l10n_long": "March 3, 2017", "publication_date_l10n_medium": "Mar 3, 2017", "publishing_information": {"journal": "International Journal of Medical, Medicine and Health Sciences, 10.0(4), 2017."}, "resource_type": {"id": "publication-article", "title_l10n": "Journal article"}, "rights": [{"description_l10n": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited.", "icon": "cc-by-icon", "id": "cc-by-4.0", "props": {"scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode"}, "title_l10n": "Creative Commons Attribution 4.0 International"}], "updated_date_l10n_long": "August 2, 2024", 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{"person_or_org": {"family_name": "Michail Theofanidis", "name": "Michail Theofanidis", "type": "personal"}}, {"person_or_org": {"family_name": "Varun Kanal", "name": "Varun Kanal", "type": "personal"}}, {"person_or_org": {"family_name": "Konstantinos Tsiakas", "name": "Konstantinos Tsiakas", "type": "personal"}}, {"person_or_org": {"family_name": "Maher Abujelala", "name": "Maher Abujelala", "type": "personal"}}, {"person_or_org": {"family_name": "Chris Collander", "name": "Chris Collander", "type": "personal"}}, {"person_or_org": {"family_name": "William B. Townsend", "name": "William B. Townsend", "type": "personal"}}, {"person_or_org": {"family_name": "Angie Boisselle", "name": "Angie Boisselle", "type": "personal"}}, {"person_or_org": {"family_name": "Fillia Makedon", "name": "Fillia Makedon", "type": "personal"}}], "description": "This paper presents a home-based robot-rehabilitation\u003cbr\u003e\ninstrument, called \u0026rdquo;MAGNI Dynamics\u0026rdquo;, that utilized a vision-based\u003cbr\u003e\nkinematic/dynamic module and an adaptive haptic feedback\u003cbr\u003e\ncontroller. The system is expected to provide personalized\u003cbr\u003e\nrehabilitation by adjusting its resistive and supportive behavior\u003cbr\u003e\naccording to a fuzzy intelligence controller that acts as an inference\u003cbr\u003e\nsystem, which correlates the user\u0026rsquo;s performance to different stiffness\u003cbr\u003e\nfactors. The vision module uses the Kinect\u0026rsquo;s skeletal tracking to\u003cbr\u003e\nmonitor the user\u0026rsquo;s effort in an unobtrusive and safe way, by estimating\u003cbr\u003e\nthe torque that affects the user\u0026rsquo;s arm. The system\u0026rsquo;s torque estimations\u003cbr\u003e\nare justified by capturing electromyographic data from primitive\u003cbr\u003e\nhand motions (Shoulder Abduction and Shoulder Forward Flexion).\u003cbr\u003e\nMoreover, we present and analyze how the Barrett WAM generates\u003cbr\u003e\na force-field with a haptic controller to support or challenge the\u003cbr\u003e\nusers. Experiments show that by shifting the proportional value,\u003cbr\u003e\nthat corresponds to different stiffness factors of the haptic path, can\u003cbr\u003e\npotentially help the user to improve his/her motor skills. Finally,\u003cbr\u003e\npotential areas for future research are discussed, that address how\u003cbr\u003e\na rehabilitation robotic framework may include multisensing data, to\u003cbr\u003e\nimprove the user\u0026rsquo;s recovery process.", "languages": [{"id": "eng", "title": {"en": "English"}}], "publication_date": "2017-03-03", "publisher": "Zenodo", "resource_type": {"id": "publication-article", "title": {"de": "Zeitschriftenartikel", "en": "Journal article"}}, "rights": [{"description": {"en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited."}, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": {"scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode"}, "title": {"en": "Creative Commons Attribution 4.0 International"}}], "title": "MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation", "version": "10006973"}, "parent": {"access": {"owned_by": {"user": "32148"}, "settings": {"accept_conditions_text": null, "allow_guest_requests": false, "allow_user_requests": false, "secret_link_expiration": 0}}, "communities": {}, "id": "1340175", "pids": {"doi": {"client": "datacite", "identifier": "10.5281/zenodo.1340175", "provider": "datacite"}}}, "pids": {"doi": {"client": "datacite", "identifier": "10.5281/zenodo.1340176", "provider": "datacite"}, "oai": {"identifier": "oai:zenodo.org:1340176", "provider": "oai"}}, "revision_id": 7, "stats": {"all_versions": {"data_volume": 23402167.0, "downloads": 37, "unique_downloads": 34, "unique_views": 258, "views": 261}, "this_version": {"data_volume": 23402167.0, "downloads": 37, "unique_downloads": 34, "unique_views": 258, "views": 261}}, "status": "published", "ui": {"access_status": {"description_l10n": "The record and files are publicly accessible.", "embargo_date_l10n": null, "icon": "unlock", "id": "open", "message_class": "", "title_l10n": "Open"}, "created_date_l10n_long": "August 7, 2018", "creators": {"affiliations": [], "creators": [{"person_or_org": {"family_name": "Alexandros Lioulemes", "name": "Alexandros Lioulemes", "type": "personal"}}, {"person_or_org": {"family_name": "Michail Theofanidis", "name": "Michail Theofanidis", "type": "personal"}}, {"person_or_org": {"family_name": "Varun Kanal", "name": "Varun Kanal", "type": "personal"}}, {"person_or_org": {"family_name": "Konstantinos Tsiakas", "name": "Konstantinos Tsiakas", "type": "personal"}}, {"person_or_org": {"family_name": "Maher Abujelala", "name": "Maher Abujelala", "type": "personal"}}, {"person_or_org": {"family_name": "Chris Collander", "name": "Chris Collander", "type": "personal"}}, {"person_or_org": {"family_name": "William B. Townsend", "name": "William B. Townsend", "type": "personal"}}, {"person_or_org": {"family_name": "Angie Boisselle", "name": "Angie Boisselle", "type": "personal"}}, {"person_or_org": {"family_name": "Fillia Makedon", "name": "Fillia Makedon", "type": "personal"}}]}, "custom_fields": {"journal:journal": {"issue": "4", "title": "International Journal of Medical, Medicine and Health Sciences", "volume": "10.0"}}, "description_stripped": "This paper presents a home-based robot-rehabilitation\ninstrument, called \u201dMAGNI Dynamics\u201d, that utilized a vision-based\nkinematic/dynamic module and an adaptive haptic feedback\ncontroller. The system is expected to provide personalized\nrehabilitation by adjusting its resistive and supportive behavior\naccording to a fuzzy intelligence controller that acts as an inference\nsystem, which correlates the user\u2019s performance to different stiffness\nfactors. The vision module uses the Kinect\u2019s skeletal tracking to\nmonitor the user\u2019s effort in an unobtrusive and safe way, by estimating\nthe torque that affects the user\u2019s arm. The system\u2019s torque estimations\nare justified by capturing electromyographic data from primitive\nhand motions (Shoulder Abduction and Shoulder Forward Flexion).\nMoreover, we present and analyze how the Barrett WAM generates\na force-field with a haptic controller to support or challenge the\nusers. Experiments show that by shifting the proportional value,\nthat corresponds to different stiffness factors of the haptic path, can\npotentially help the user to improve his/her motor skills. Finally,\npotential areas for future research are discussed, that address how\na rehabilitation robotic framework may include multisensing data, to\nimprove the user\u2019s recovery process.", "is_draft": false, "languages": [{"id": "eng", "title_l10n": "English"}], "publication_date_l10n_long": "March 3, 2017", "publication_date_l10n_medium": "Mar 3, 2017", "publishing_information": {"journal": "International Journal of Medical, Medicine and Health Sciences, 10.0(4), 2017."}, "resource_type": {"id": "publication-article", "title_l10n": "Journal article"}, "rights": [{"description_l10n": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited.", "icon": "cc-by-icon", "id": "cc-by-4.0", "props": {"scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode"}, "title_l10n": "Creative Commons Attribution 4.0 International"}], "updated_date_l10n_long": "August 2, 2024", "version": "10006973"}, "updated": "2024-08-02T04:38:45.742573+00:00", "versions": {"index": 1, "is_latest": true}}' data-styles='[["apa", "APA"], ["harvard-cite-them-right", "Harvard"], ["modern-language-association", "MLA"], ["vancouver", "Vancouver"], ["chicago-fullnote-bibliography", "Chicago"], ["ieee", "IEEE"]]' data-defaultstyle='"apa"' data-include-deleted='false'> </div> </div> </div> <div class="sidebar-container"> <h2 class="ui medium top attached header mt-0">Export</h2> <div id="export-record" class="ui segment bottom attached exports rdm-sidebar"> <div id="recordExportDownload" data-formats='[{"export_url": "/records/1340176/export/json", "name": "JSON"}, {"export_url": "/records/1340176/export/json-ld", "name": "JSON-LD"}, {"export_url": "/records/1340176/export/csl", "name": "CSL"}, {"export_url": "/records/1340176/export/datacite-json", "name": "DataCite JSON"}, {"export_url": "/records/1340176/export/datacite-xml", "name": "DataCite XML"}, {"export_url": "/records/1340176/export/dublincore", "name": "Dublin Core XML"}, {"export_url": "/records/1340176/export/marcxml", "name": "MARCXML"}, {"export_url": "/records/1340176/export/bibtex", "name": "BibTeX"}, {"export_url": "/records/1340176/export/geojson", "name": "GeoJSON"}, {"export_url": "/records/1340176/export/dcat-ap", "name": "DCAT"}, {"export_url": "/records/1340176/export/codemeta", "name": "Codemeta"}, {"export_url": "/records/1340176/export/cff", "name": "Citation File Format"}]'></div> </div> </div> <section id="upload-info" role="note" aria-label="Upload information" class="sidebar-container ui segment rdm-sidebar text-muted" > <h2 class="ui small header text-muted p-0 mb-5"><small>Technical metadata</small></h2> <dl class="m-0"> <dt class="inline"><small>Created</small></dt> <dd class="inline"> <small>August 7, 2018</small> </dd> <div> <dt class="rel-mt-1 inline"><small>Modified</small></dt> <dd class="inline"> <small>August 2, 2024</small> </dd> </div> </dl> </section> 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Townsend"}, {"@type": "Person", "familyName": "Angie Boisselle", "name": "Angie Boisselle"}, {"@type": "Person", "familyName": "Fillia Makedon", "name": "Fillia Makedon"}], "dateCreated": "2018-08-07T09:26:29.218611+00:00", "dateModified": "2024-08-02T04:38:45.742573+00:00", "datePublished": "2017-03-03", "description": "This paper presents a home-based robot-rehabilitation\u003cbr\u003e\ninstrument, called \u0026rdquo;MAGNI Dynamics\u0026rdquo;, that utilized a vision-based\u003cbr\u003e\nkinematic/dynamic module and an adaptive haptic feedback\u003cbr\u003e\ncontroller. The system is expected to provide personalized\u003cbr\u003e\nrehabilitation by adjusting its resistive and supportive behavior\u003cbr\u003e\naccording to a fuzzy intelligence controller that acts as an inference\u003cbr\u003e\nsystem, which correlates the user\u0026rsquo;s performance to different stiffness\u003cbr\u003e\nfactors. 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MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation