{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T04:05:11Z","timestamp":1771473911907,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T00:00:00Z","timestamp":1585526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802338"],"award-info":[{"award-number":["61802338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ19A020001"],"award-info":[{"award-number":["LQ19A020001"]}]},{"DOI":"10.13039\/100007834","name":"Ningbo Natural Science Foundation","doi-asserted-by":"publisher","award":["2018A610193"],"award-info":[{"award-number":["2018A610193"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We develop a deep learning refined kinematic model for accurately assessing upper limb joint angles using a single Kinect v2 sensor. We train a long short-term memory recurrent neural network using a supervised machine learning architecture to compensate for the systematic error of the Kinect kinematic model, taking a marker-based three-dimensional motion capture system (3DMC) as the golden standard. A series of upper limb functional task experiments were conducted, namely hand to the contralateral shoulder, hand to mouth or drinking, combing hair, and hand to back pocket. Our deep learning-based model significantly improves the performance of a single Kinect v2 sensor for all investigated upper limb joint angles across all functional tasks. Using a single Kinect v2 sensor, our deep learning-based model could measure shoulder and elbow flexion\/extension waveforms with mean CMCs &gt;0.93 for all tasks, shoulder adduction\/abduction, and internal\/external rotation waveforms with mean CMCs &gt;0.8 for most of the tasks. The mean deviations of angles at the point of target achieved and range of motion are under 5\u00b0 for all investigated joint angles during all functional tasks. Compared with the 3DMC, our presented system is easier to operate and needs less laboratory space.<\/jats:p>","DOI":"10.3390\/s20071903","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1903","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Deep Learning-Based Upper Limb Functional Assessment Using a Single Kinect v2 Sensor"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1964-045X","authenticated-orcid":false,"given":"Ye","family":"Ma","sequence":"first","affiliation":[{"name":"Research Academy of Grand Health, Faculty of Sports Science, Ningbo University, Ningbo 315000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0697-0548","authenticated-orcid":false,"given":"Dongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, China"}]},{"given":"Laisi","family":"Cai","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315000, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s00221-005-2251-8","article-title":"Disruptions in joint control during drawing arm movements in Parkinson\u2019s disease","volume":"164","author":"Dounskaia","year":"2005","journal-title":"Exp. 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