{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:29:12Z","timestamp":1761863352984,"version":"3.41.2"},"reference-count":37,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2014,8,12]],"date-time":"2014-08-12T00:00:00Z","timestamp":1407801600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,8,12]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user\u2019s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user\u2019s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 Both users\u2019 movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user\u2019s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/ir-04-2014-0327","type":"journal-article","created":{"date-parts":[[2014,9,22]],"date-time":"2014-09-22T06:09:10Z","timestamp":1411366150000},"page":"465-479","source":"Crossref","is-referenced-by-count":35,"title":["Active interaction control applied to a lower limb rehabilitation robot by using EMG recognition and impedance model"],"prefix":"10.1108","volume":"41","author":[{"given":"Wei","family":"Meng","sequence":"first","affiliation":[]},{"given":"Quan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Zude","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Qingsong","family":"Ai","sequence":"first","affiliation":[]}],"member":"140","reference":[{"key":"key2020123001214405500_b1","doi-asserted-by":"crossref","unstructured":"Ai, Q.S.\n               , \n                  Liu, Q.\n               , \n                  Yuan, T.T.\n                and \n                  Lu, Y.\n                (2013), \u201cGestures recognition based on wavelet and LLE,\u201d Australasian Physical & Engineering Sciences in Medicine, Vol. 36 No. 2, pp. 167-176.","DOI":"10.1007\/s13246-013-0191-3"},{"key":"key2020123001214405500_b2","doi-asserted-by":"crossref","unstructured":"Ai, Q.S.\n               , \n                  Chen, L.\n               , \n                  Liu, Q.\n                and \n                  Zou, L.\n                (2014), \u201cRehabilitation assessment for lower limb disability based on multi-disciplinary approaches\u201d, Australasian Physical & Engineering Sciences in Medicine, Vol. 37 No. 2, pp. 355-365.","DOI":"10.1007\/s13246-014-0268-7"},{"key":"key2020123001214405500_b3","doi-asserted-by":"crossref","unstructured":"Akdogan, E.\n               , \n                  Shima, K.\n               , \n                  Kataoka, H.\n               , \n                  Hasegawa, M.\n               , \n                  Otsuka, A.\n                and \n                  Tsuji, T.\n                (2012), \u201cThe cybernetic rehabilitation aid: preliminary results for wrist and elbow motions in healthy subjects\u201d, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 20 No. 5, pp. 697-707.","DOI":"10.1109\/TNSRE.2012.2198496"},{"key":"key2020123001214405500_b4","unstructured":"Benitez, L.M.V.\n               , \n                  Tabie, M.\n               , \n                  Will, N.\n               , \n                  Schmidt, S.\n               , \n                  Jordan, M.\n                and \n                  Kirchner, E.A.\n                (2013), \u201cExoskeleton technology in rehabilitation: towards an emg-based orthosis system for upper limb neuromotor rehabilitation\u201d, Journal of Robotics, Vol. 2013."},{"key":"key2020123001214405500_b5","doi-asserted-by":"crossref","unstructured":"Cho, H.C.\n                and \n                  Park, J.H.\n                (2005), \u201cImpedance control with variable damping for bilateral teleoperation under time delay\u201d, JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, Vol. 48, No. 4, pp. 695-703.","DOI":"10.1299\/jsmec.48.695"},{"key":"key2020123001214405500_b6","doi-asserted-by":"crossref","unstructured":"DiCicco, M.\n               , \n                  Lucas, L.\n                and \n                  Matsuoka, Y.\n                (2004), \u201cComparison of control strategies for an EMG controlled orthotic exoskeleton for the hand\u201d, in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA \u201804), pp. 1622-1627.","DOI":"10.1109\/ROBOT.2004.1308056"},{"key":"key2020123001214405500_b7","doi-asserted-by":"crossref","unstructured":"Duchaine, V.\n                and \n                  Gosselin, C.M.\n                (2007), \u201cGeneral model of human-robot cooperation using a novel velocity based variable impedance control\u201d, in EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. 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