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In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate continuous multijoint movements from sEMG and MMG signals accurately. The model combines forward dynamics with a Hill-based muscle model that estimates joint torque only in a nonfeedback form, making the extended model capable of predicting the multijoint motion directly. The sEMG and MMG features, including the Wilson amplitude and permutation entropy, are then extracted to construct a measurement equation to reduce system error and external disturbances. Using the proposed model, a closed-loop prediction-correction approach, unscented particle filtering, is used to estimate the joint angle from sEMG and MMG signals. Comprehensive experiments are conducted on the human elbow and shoulder joint, and remarkable improvements are demonstrated compared with conventional methods.<\/jats:p>","DOI":"10.1155\/2020\/4503271","type":"journal-article","created":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T18:31:13Z","timestamp":1581445873000},"page":"1-12","source":"Crossref","is-referenced-by-count":7,"title":["sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9213-6313","authenticated-orcid":true,"given":"Xugang","family":"Xi","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China"}]},{"given":"Chen","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, 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