{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T01:55:02Z","timestamp":1778550902470,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T00:00:00Z","timestamp":1572220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China Science and Technical Assistance Project for Developing Countries","award":["KY201501009"],"award-info":[{"award-number":["KY201501009"]}]},{"name":"Key Research and Development Plan of Hebei Province","award":["19211820D"],"award-info":[{"award-number":["19211820D"]}]},{"name":"The forty-third regular meeting exchange programs of China Romania science and technology cooperation committee","award":["43-2"],"award-info":[{"award-number":["43-2"]}]},{"name":"The European Commission SMOOTH project: Smart Robot for Fire-fighting","award":["H2020-MSCA-RISE-2016:734875"],"award-info":[{"award-number":["H2020-MSCA-RISE-2016:734875"]}]},{"name":"Romanian Ministry of Research and Innovation, CCCDI-UEFISCDI, within PNCDI III, &quot;KEYT HROB&quot; project","award":["PN-III-P3-3.1-PM-RO-CN-2018-0144 \/ 2 BM \u2044 2018"],"award-info":[{"award-number":["PN-III-P3-3.1-PM-RO-CN-2018-0144 \/ 2 BM \u2044 2018"]}]},{"name":"Postgraduate Innovation Research Assistant Support Project","award":["CXZS201902"],"award-info":[{"award-number":["CXZS201902"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the patients take part in. Most of the signals used to measure the patients\u2019 participation are EMG signals or oxygen consumption, which increase the cost and the complexity of the robotic device. In this work, we design a multi-sensor system robot with torque and six-dimensional force sensors to gauge the patients\u2019 participation in training. By establishing the static equation of the mechanical leg, the man\u2013machine interaction force of the patient can be accurately extracted. Using the impedance model, the auxiliary force training mode is established, and the difficulty of the target task is changed by adjusting the K value of auxiliary force. Participation models with three intensities were developed offline using support vector machines, for which the C and \u03c3 parameters are optimized by the hybrid quantum particle swarm optimization and support vector machines (Hybrid QPSO-SVM) algorithm. An experimental statistical analysis was conducted on ten volunteers\u2019 motion representation in different training tasks, which are divided into three stages: over-challenge, challenge, less challenge, by choosing characteristic quantities with significant differences among the various difficulty task stages, as a training set for the support vector machines (SVM). Experimental results from 12 volunteers, with tasks conducted on the lower limb rehabilitation robot LLR-II show that the rehabilitation robot can accurately predict patient participation and training task difficulty. The prediction accuracy reflects the superiority of the Hybrid QPSO-SVM algorithm.<\/jats:p>","DOI":"10.3390\/s19214681","type":"journal-article","created":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T11:26:13Z","timestamp":1572261973000},"page":"4681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots"],"prefix":"10.3390","volume":"19","author":[{"given":"Hao","family":"Yan","sequence":"first","affiliation":[{"name":"Parallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbo","family":"Wang","sequence":"additional","affiliation":[{"name":"Parallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University, Qinhuangdao 066004, China"},{"name":"Academy for Engineering &amp; Technology, Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luige","family":"Vladareanu","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Department, Institute of Solid Mechanics of Romanian Academy, 010141 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Musong","family":"Lin","sequence":"additional","affiliation":[{"name":"Parallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2000-4792","authenticated-orcid":false,"given":"Victor","family":"Vladareanu","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Department, Institute of Solid Mechanics of Romanian Academy, 010141 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yungui","family":"Li","sequence":"additional","affiliation":[{"name":"Parallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,28]]},"reference":[{"key":"ref_1","first-page":"519","article-title":"Expert consensus on post-stroke cognitive management","volume":"12","author":"Dong","year":"2017","journal-title":"Chin. 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