{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T11:56:22Z","timestamp":1776426982620,"version":"3.51.2"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001348","name":"\u201cIntelligent Human\u2013robot interface for upper limb wearable robots\u201d","doi-asserted-by":"publisher","award":["SERC1922500046"],"award-info":[{"award-number":["SERC1922500046"]}],"id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The lack of intuitive and active human\u2013robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study\u2019s detailed analysis can improve the usability of the assistive\/rehabilitation robots.<\/jats:p>","DOI":"10.3390\/s23062998","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T02:05:54Z","timestamp":1678413954000},"page":"2998","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3024-0630","authenticated-orcid":false,"given":"Sibo","family":"Yang","sequence":"first","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7374-659X","authenticated-orcid":false,"given":"Neha P.","family":"Garg","sequence":"additional","affiliation":[{"name":"Rehabilitation Research Institute of Singapore (RRIS), Nanyang Technological University, Singapore 308232, Singapore"}]},{"given":"Ruobin","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0875-0127","authenticated-orcid":false,"given":"Meng","family":"Yuan","sequence":"additional","affiliation":[{"name":"Rehabilitation Research Institute of Singapore (RRIS), Nanyang Technological University, Singapore 308232, Singapore"}]},{"given":"Bernardo","family":"Noronha","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"given":"Wei Tech","family":"Ang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"},{"name":"Rehabilitation Research Institute of Singapore (RRIS), Nanyang Technological University, Singapore 308232, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9039-5488","authenticated-orcid":false,"given":"Dino","family":"Accoto","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Robotics, Automation and Mechatronics Division, KU Leuven, 3590 Diepenbeek, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1053\/apmr.2002.33984","article-title":"Activity, participation, and quality of life 6 months poststroke","volume":"83","author":"Mayo","year":"2002","journal-title":"Arch. 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