乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:49:00Z","timestamp":1740149340772,"version":"3.37.3"},"reference-count":49,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T00:00:00Z","timestamp":1554681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 105-2119-M-039-003, 106-2119-M-039-002, 106-2221-E-155-020, 107-2119-M-039-002"],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012544","name":"China Medical University, Taiwan","doi-asserted-by":"publisher","award":["CMU106-S-29"],"id":[{"id":"10.13039\/501100012544","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"The physical therapeutic application needs personalized rehabilitation recognition (PRR) for ubiquitous healthcare measurements (UHMs). This study employed the adaptive neuro-fuzzy inference system (ANFIS) to generate a PRR model for a self-development system of UHM. The subjects wore a sensor-enabled wristband during physiotherapy exercises to measure the scheduled motions of their limbs. In the model, the sampling data collected from the scheduled motions are labeled by an arbitrary number within a defined range. The sample datasets are referred as the design of an initial fuzzy inference system (FIS) with data preprocessing, feature visualizing, fuzzification, and fuzzy logic rules. The ANFIS then processes data training to adjust the FIS for optimization. The trained FIS then can infer the motion labels via defuzzification to recognize the features in the test data. The average recognition rate was higher than 90% for the testing motions if the subject followed the sampling schedule. With model implementation, the middle section of motion datasets in each second is recommended for recognition in the UHM system which also includes a mobile App to retrieve the personalized FIS in order to trace the exercise. This approach contributes a PRR model with trackable diagrams for the physicians to explore the rehabilitation motions in details.<\/jats:p>","DOI":"10.3390\/s19071679","type":"journal-article","created":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T15:54:52Z","timestamp":1554738892000},"page":"1679","source":"Crossref","is-referenced-by-count":4,"title":["Personalized Rehabilitation Recognition for Ubiquitous Healthcare Measurements"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3611-5067","authenticated-orcid":false,"given":"Yao-Chiang","family":"Kan","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Yuan Ze University, Chung-Li, Taoyuan City 32003, Taiwan"}]},{"given":"Yu-Chieh","family":"Kuo","sequence":"additional","affiliation":[{"name":"Department and Institute of Health Service Administrations, China Medical University, Taichung 40402, Taiwan"}]},{"given":"Hsueh-Chun","family":"Lin","sequence":"additional","affiliation":[{"name":"Department and Institute of Health Service Administrations, China Medical University, Taichung 40402, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/0363-5023(90)90102-W","article-title":"Functional range of motion of the joints of the hand","volume":"15","author":"Hume","year":"1990","journal-title":"J. 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