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Thirty\u2010five subjects were divided into five stages (healthy, patient: Brunnstrom stages 3, 4, 5, 6). NIRS was used to record the muscular hemodynamic responses from bilateral femoris (biceps brachii) muscles during passive and active upper (lower) limbs circular exercise. We used the D\u2010S evidence theory to conduct feature information fusion and established a Gradient Boosting DD\u2010MLP Net model, combining the dendrite network and multilayer perceptron, to realize automatic dyskinesias degree evaluation. Our model classified the upper limb dyskinesias with high accuracy: 98.91% under the passive mode and 98.69% under the active mode, and classified the lower limb dyskinesias with high accuracy: 99.45% and 99.63% under the passive and active modes, respectively. Our model combined with NIRS has great potential in monitoring the after\u2010stroke dyskinesias degree and guiding rehabilitation training.<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/jbio202300029-gra-0001.png\" xlink:title=\"image\"\/><\/jats:p>","DOI":"10.1002\/jbio.202300029","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T14:28:03Z","timestamp":1686148083000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Gradient boosting <scp>DD\u2010MLP<\/scp> Net: An ensemble learning model using near\u2010infrared spectroscopy to classify after\u2010stroke dyskinesia degree during exercise"],"prefix":"10.1002","volume":"16","author":[{"given":"Jianbin","family":"Liang","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering and Automation Foshan University  Foshan 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