Motor Ingredients Derived from a Wearable Sensor-Based Virtual Reality System for Frozen Shoulder Rehabilitation

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Title:	Motor Ingredients Derived from a Wearable Sensor-Based Virtual Reality System for Frozen Shoulder Rehabilitation
Authors:	葉士青;Yang, Geng;Chen, Shih-Yin;Chan, Rai-Chi;Yeh, Shih-Ching;Lee, Si-Huei;Zheng, Li-Rong
Contributors:	資訊電機學院資訊工程學系
Keywords:	Analysis;Arm - physiopathology;Bursitis - physiopathology;Bursitis - rehabilitation;Cats;Correlation analysis;Data mining;Data processing;Female;Hospitals;Humans;Internet of Things;Male;Medical research;Medicine, Experimental;Middle Aged;Motor Activity;Muscle Strength;Physical therapy;Range of Motion, Articular;Rehabilitation;Rehabilitation - instrumentation;Sensors;Shoulder;Studies;Task Performance and Analysis;User-Computer Interface;Virtual reality
Date:	2016-01-01
Issue Date:	2026-04-23 13:54:45 (UTC+8)
Publisher:	Hindawi Publishing Corporation;Cairo, Egypt: Hindawi Publishing Corporation
Abstract:	摘要： Objective. This study aims to extract motor ingredients through data mining from wearable sensors in a virtual reality goal-directed shoulder rehabilitation (GDSR) system and to examine their effects toward clinical assessment. Design. A single-group before/after comparison. Setting. Outpatient research hospital. Subjects. 16 patients with frozen shoulder. Interventions. The rehabilitation treatment involved GDSR exercises, hot pack, and interferential therapy. All patients first received hot pack and interferential therapy on the shoulder joints before engaging in the exercises. The GDSR exercise sessions were 40 minutes twice a week for 4 weeks. Main Measures. Clinical assessments included Constant and Murley score, range of motion of the shoulder, and muscle strength of upper arm as main measures. Motor indices from sensor data and task performance were measured as secondary measures. Results. The pre- and posttest results for task performance, motor indices, and the clinical assessments indicated significant improvement for the majority of the assessed items. Correlation analysis between the task performance and clinical assessments revealed significant correlations among a number of items. Stepwise regression analysis showed that task performance effectively predicted the results of several clinical assessment items. Conclusions. The motor ingredients derived from the wearable sensor and task performance are applicable and adequate to examine and predict clinical improvement after GDSR training. 其他題名： Biomed Res Int 出版者： Cairo, Egypt: Hindawi Publishing Corporation 出版日期： 2016-01-01 出處： BioMed research international, 2016-01, Vol.2016 (2016), p.1-10 資源來源： Publicly Available Content Database 版權： Copyright © 2016 Si-Huei Lee et al. 版權： COPYRIGHT 2016 John Wiley & Sons, Inc. 版權： Copyright © 2016 Si-Huei Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 版權： Copyright © 2016 Si-Huei Lee et al. 2016 識別號： ISSN: 2314-6133 識別號： ISSN: 2314-6141 識別號： EISSN: 2314-6141 識別號： DOI: 10.1155/2016/7075464 識別號： PMID: 27642600
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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