Online Human Intention Detection through Machine-learning based Algorithm for the Control of Lower-limbs Wearable Robot
Online human intention detection is one of the main challenges to ensure smooth human robot interaction for assistive robotics through wearable devices. This paper proposes a framework that combines both machine learning based algorithms and task-oriented control of an actuated-ankle-foot orthosis f...
Gespeichert in:
| Veröffentlicht in: | IEEE-RAS International Conference on Humanoid Robots (Print) S. 809 - 814 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
28.11.2022
|
| Schlagworte: | |
| ISSN: | 2164-0580 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Online human intention detection is one of the main challenges to ensure smooth human robot interaction for assistive robotics through wearable devices. This paper proposes a framework that combines both machine learning based algorithms and task-oriented control of an actuated-ankle-foot orthosis for human locomotion assistance during five gait modes that are level walking, stairs ascent/descent, and ramp ascent/descent. A random-forest based algorithm has been trained to provide an online classification of the five gait modes using kinematic features of a dataset collected with ten healthy subjects. Finally, appropriate assistive torques were applied at the ankle joint level with respect to the detected gait mode. The proposed scheme is verified in terms of gait mode detection success rate and the torque assistance through the actuated-ankle-foot orthosis at the ankle joint level. One healthy subject participated in the experiments with and without applying the torque assistance strategy. The results show the following average success rates of 99.49%, 98.30%, 96.07%, 84.63%, and 85.55% for the different locomotion modes, that are level walking, stair ascent, stair descent, ramp ascent, and ramp descent, respectively. |
|---|---|
| AbstractList | Online human intention detection is one of the main challenges to ensure smooth human robot interaction for assistive robotics through wearable devices. This paper proposes a framework that combines both machine learning based algorithms and task-oriented control of an actuated-ankle-foot orthosis for human locomotion assistance during five gait modes that are level walking, stairs ascent/descent, and ramp ascent/descent. A random-forest based algorithm has been trained to provide an online classification of the five gait modes using kinematic features of a dataset collected with ten healthy subjects. Finally, appropriate assistive torques were applied at the ankle joint level with respect to the detected gait mode. The proposed scheme is verified in terms of gait mode detection success rate and the torque assistance through the actuated-ankle-foot orthosis at the ankle joint level. One healthy subject participated in the experiments with and without applying the torque assistance strategy. The results show the following average success rates of 99.49%, 98.30%, 96.07%, 84.63%, and 85.55% for the different locomotion modes, that are level walking, stair ascent, stair descent, ramp ascent, and ramp descent, respectively. |
| Author | Moon, Huiseok Oukhellou, Latifa Mohammed, Samer Amirat, Yacine Boubezoul, Abderrahmane |
| Author_xml | – sequence: 1 givenname: Huiseok surname: Moon fullname: Moon, Huiseok email: huiseok.moon@u-pec.fr organization: University of Pais-Est Créteil (UPEC),Laboratory of Images, Signals and Intelligent Systems (LISSI),Vitry-sur-Seine,France,94400 – sequence: 2 givenname: Abderrahmane surname: Boubezoul fullname: Boubezoul, Abderrahmane organization: Université Gustave Eiffel,COSYS-GRETTIA,Marne-la-Vallée,France – sequence: 3 givenname: Latifa surname: Oukhellou fullname: Oukhellou, Latifa organization: Université Gustave Eiffel,COSYS-GRETTIA,Marne-la-Vallée,France – sequence: 4 givenname: Yacine surname: Amirat fullname: Amirat, Yacine organization: University of Pais-Est Créteil (UPEC),Laboratory of Images, Signals and Intelligent Systems (LISSI),Vitry-sur-Seine,France,94400 – sequence: 5 givenname: Samer surname: Mohammed fullname: Mohammed, Samer email: samer.mohammed@u-pec.fr organization: University of Pais-Est Créteil (UPEC),Laboratory of Images, Signals and Intelligent Systems (LISSI),Vitry-sur-Seine,France,94400 |
| BookMark | eNo1kMFOwzAQRA0CiVL6Bxx84JqydurEPlaF0kpFlRCIY-Uk68YosZHjquLvMQX2MqPdN3PYa3LhvENC7hhMGQN1vzr02nnbDCJXSkw5cD5lkIYJOCMTVSqZC8ghGXVORpwVswyEhCsyGYaPxOVMSsWLETluXWcd0lMjXbuILlrv6ANGrE8utsEf9i191nWbyKxDHZx1e1rpARs67_Y-2Nj21PiQYKQL72LwHfWGbvwRQ9bZvhroe8rpqkP64isfb8il0d2Akz8dk7fl4-tilW22T-vFfJO1PJcxU2amSzAGDMgCazCzuhDCNI0Uukg3FGWtNJQlK7isK1ULqHjzs1BaC93kY3L722sRcfcZbK_D1-7_V_k3JK5kpQ |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/Humanoids53995.2022.10000150 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9798350309799 |
| EISSN | 2164-0580 |
| EndPage | 814 |
| ExternalDocumentID | 10000150 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IH 6IK 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL |
| ID | FETCH-LOGICAL-h238t-9f4a70ff0f086ec0f4c655fdd85a6f4ae57c9a0771628cb9c50b2da0779aa5ad3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000925894300107&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:14:14 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-h238t-9f4a70ff0f086ec0f4c655fdd85a6f4ae57c9a0771628cb9c50b2da0779aa5ad3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_10000150 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-Nov.-28 |
| PublicationDateYYYYMMDD | 2022-11-28 |
| PublicationDate_xml | – month: 11 year: 2022 text: 2022-Nov.-28 day: 28 |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE-RAS International Conference on Humanoid Robots (Print) |
| PublicationTitleAbbrev | HUMANOIDS |
| PublicationYear | 2022 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0003188926 |
| Score | 1.829463 |
| Snippet | Online human intention detection is one of the main challenges to ensure smooth human robot interaction for assistive robotics through wearable devices. This... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 809 |
| SubjectTerms | Humanoid robots Kinematics Legged locomotion Machine learning algorithms Torque Wearable computers Wearable robots |
| Title | Online Human Intention Detection through Machine-learning based Algorithm for the Control of Lower-limbs Wearable Robot |
| URI | https://ieeexplore.ieee.org/document/10000150 |
| WOSCitedRecordID | wos000925894300107&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELWgQggWvor4loeubkNqO_aICogBqgqB6FY59rmN1CaoTeHvYztpgYGBLbKTyDpHer7LvfcQanW5OyVLQwnvQpdQzmKigAIx1Hj4UwBQmU0k_b4YDuWgJqsHLoybDM1n0PaX4V--KfTSl8o6VS3aZ-ibScIrsta6oOI-TiFjvo1atY5mJ5TBi8wsvPoqc7lgHLdXr_hlphKw5H7vn6vYR81vVh4erPHmAG1Afoh2fwgKHqHPSjkUhzXh0J7uA49voQwtVzmufXnwU2iiBFK7RoyxhzODb6bjYp6Vkxl2h1l3M-Be1cuOC4sfvaMamWazdIHf3HOedYWfi7Qom-j1_u6l90BqawUycRhdEmmpSiJrI-tSGtCRpZozZo0RTHE3ByzRUkWJ15cSOpWaRWls_IBUiinTPUaNvMjhBGEtpEk1B6BS0zSJ02sLoLkWwB3SCX2Kmj6Go_dKPWO0Ct_ZH-PnaMfvlOf7xeICNcr5Ei7Rlv4os8X8Kuz5F4u_sEk |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1dT8IwFG0MGj9e_ML4bR94Lcyu7dZHgxKNQIjByBvZ2jsggc3A0L9v2w3UBx98W9ptaW6XnN67e85BqOYLc0qWmhHhg0-Y4JREwIBopi38RQBQmE0E3W44GMheSVZ3XBgz6ZrPoG4v3b98namlLZU1ilq0zdA3OWPUK-ha65KK-TxDScU2qpVKmg1XCM8memH1V7nJBimtr17yy07FoUlr_5_rOEDVb14e7q0R5xBtQHqE9n5ICh6jz0I7FLs1YdegbkOP7yF3TVcpLp15cMe1UQIpfSNG2AKaxnfTUTaf5OMZNsdZczPgZtHNjrMEt62nGplOZvECv5nnLO8Kv2RxllfRa-uh33wkpbkCGRuUzolMWBR4SeIlJqkB5SVMCc4TrUMeCTMHPFAy8gKrMBWqWCruxVTbARlFPNL-CaqkWQqnCKtQ6lgJACYViwMa3yYASqgQhMG6UJ2hqo3h8L3Qzxiuwnf-x_gN2nnsd9rD9lP3-QLt2l2z7D8aXqJKPl_CFdpSH_lkMb92-_8F30KzkA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=IEEE-RAS+International+Conference+on+Humanoid+Robots+%28Print%29&rft.atitle=Online+Human+Intention+Detection+through+Machine-learning+based+Algorithm+for+the+Control+of+Lower-limbs+Wearable+Robot&rft.au=Moon%2C+Huiseok&rft.au=Boubezoul%2C+Abderrahmane&rft.au=Oukhellou%2C+Latifa&rft.au=Amirat%2C+Yacine&rft.date=2022-11-28&rft.pub=IEEE&rft.eissn=2164-0580&rft.spage=809&rft.epage=814&rft_id=info:doi/10.1109%2FHumanoids53995.2022.10000150&rft.externalDocID=10000150 |