{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T09:21:26Z","timestamp":1770283286389,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSFC","award":["U1913601"],"award-info":[{"award-number":["U1913601"]}]},{"name":"NSFC","award":["52175033"],"award-info":[{"award-number":["52175033"]}]},{"name":"NSFC","award":["U21A20120"],"award-info":[{"award-number":["U21A20120"]}]},{"name":"NSFC","award":["LZ20E050002"],"award-info":[{"award-number":["LZ20E050002"]}]},{"name":"NSFC","award":["2022C03103"],"award-info":[{"award-number":["2022C03103"]}]},{"name":"NSFC","award":["2021C03051"],"award-info":[{"award-number":["2021C03051"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["U1913601"],"award-info":[{"award-number":["U1913601"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["52175033"],"award-info":[{"award-number":["52175033"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["U21A20120"],"award-info":[{"award-number":["U21A20120"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LZ20E050002"],"award-info":[{"award-number":["LZ20E050002"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["2022C03103"],"award-info":[{"award-number":["2022C03103"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["2021C03051"],"award-info":[{"award-number":["2021C03051"]}]},{"name":"Key Research and Development Program of Zhejiang","award":["U1913601"],"award-info":[{"award-number":["U1913601"]}]},{"name":"Key Research and Development Program of Zhejiang","award":["52175033"],"award-info":[{"award-number":["52175033"]}]},{"name":"Key Research and Development Program of Zhejiang","award":["U21A20120"],"award-info":[{"award-number":["U21A20120"]}]},{"name":"Key Research and Development Program of Zhejiang","award":["LZ20E050002"],"award-info":[{"award-number":["LZ20E050002"]}]},{"name":"Key Research and Development Program of Zhejiang","award":["2022C03103"],"award-info":[{"award-number":["2022C03103"]}]},{"name":"Key Research and Development Program of Zhejiang","award":["2021C03051"],"award-info":[{"award-number":["2021C03051"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rehabilitation evaluation of Parkinson\u2019s disease has always been the research focus of human assistive systems. It is a research hotspot to objectively and accurately evaluate the gait condition of Parkinson\u2019s disease patients, thereby adjusting the actuators of the human\u2013machine system and making rehabilitation robots better adapt to the recovery process of patients. The rehabilitation evaluation of Parkinson\u2019s disease has always been the research focus of rehabilitation robots. It is a research hotspot to be able to objectively and accurately evaluate the recovery of Parkinson\u2019s disease patients, thereby adjusting the driving module of the human\u2013machine collaboration system in real time, so that rehabilitation robots can better adapt to the recovery process of Parkinson\u2019s disease. The gait task in the Unified Parkinson\u2019s Disease Rating Scale (UPDRS) is a widely accepted standard for assessing the gait impairments of patients with Parkinson\u2019s disease (PD). However, the assessments conducted by neurologists are always subjective and inaccurate, and the results are determined by the neurologists\u2019 observation and clinical experience. Thus, in this study, we proposed a novel machine learning-based method of automatically assessing the gait task in UPDRS with wearable sensors as a more convenient and objective alternative means for PD gait assessment. In the design, twelve gait features, including three spatial\u2013temporal features and nine kinematic features, were extracted and calculated from two shank-mounted IMUs. A novel nonlinear model is developed for calculating the score of gait task from the gait features. Twenty-five PD patients and twenty-eight healthy subjects were recruited for validating the proposed method. For comparison purpose, three traditional models, which have been used in previous studies, were also tested by the same dataset. In terms of percentages of participants, 84.9%, 73.6%, 73.6%, and 66.0% of the participants were accurately assigned into the true level with the proposed nonlinear model, the support vector machine model, the naive Bayes model, and the linear regression model, respectively, which indicates that the proposed method has a good performance on calculating the score of the UPDRS gait task and conformance with the rating done by neurologists.<\/jats:p>","DOI":"10.3390\/s23042104","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T01:41:06Z","timestamp":1676338866000},"page":"2104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Automatic Assessments of Parkinsonian Gait with Wearable Sensors for Human Assistive Systems"],"prefix":"10.3390","volume":"23","author":[{"given":"Yi","family":"Han","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology, Kochi 782-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangzhi","family":"Liu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"The National Research Center for Rehabilitation Technical Aids, Beijing 102676, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiufeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"The National Research Center for Rehabilitation Technical Aids, Beijing 102676, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"The College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology, Kochi 782-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2797-0264","authenticated-orcid":false,"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingang","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ 08854, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1097\/MRR.0000000000000312","article-title":"Effectiveness of robot-assisted gait training on motor impairments in people with Parkinson\u2019s disease: A systematic review and meta-analysis","volume":"41","author":"Alwardat","year":"2018","journal-title":"Int. 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