Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury
Issue title: Papers from the International Multi-Conference on Engineering and Technology Innovation 2016 (IMETI2016), 28 Oct.–1 Nov. 2016, Taichung, Taiwan
Affiliations: [a] Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 807, Taiwan | [b] Department of Rehabilitation Medicine, Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan | [c] Department of Rehabilitation Medicine, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung 801, Taiwan | [d] Department of Logistics Management, National Kaohsiung First University of Science and Technology, Kaohsiung 824, Taiwan | [e] Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan | [f] Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan
Correspondence:
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Corresponding author: Wen-Hsien Ho, Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, 100 Shin-Chuan 1st Road, Kaohsiung 807, Taiwan. Tel.: +886 7 3121101; Fax: +886 7 3137487; E-mail: whho@kmu.edu.tw.
Abstract: BACKGROUND: Effective neurological rehabilitation requires long term assessment and treatment. The rapid progress of virtual reality-based assistive technologies and tele-rehabilitation has increased the potential for self-rehabilitation of various neurological injuries under clinical supervision. OBJECTIVE: The objective of this study was to develop a fuzzy inference mechanism for a smart mobile computing system designed to support in-home rehabilitation of patients with neurological injury in the hand by providing an objective means of self-assessment. METHODS: A commercially available tablet computer equipped with a Bluetooth motion sensor was integrated in a splint to obtain a smart assistive device for collecting hand motion data, including writing performance and the corresponding grasp force. A virtual reality game was also embedded in the smart splint to support hand rehabilitation. Quantitative data obtained during the rehabilitation process were modeled by fuzzy logic. Finally, the improvement in hand function was quantified with a fuzzy rule database of expert opinion and experience. RESULTS: Experiments in chronic stroke patients showed that the proposed system is applicable for supporting in-home hand rehabilitation. CONCLUSIONS: The proposed virtual reality system can be customized for specific therapeutic purposes. Commercial development of the system could immediately provide stroke patients with an effective in-home rehabilitation therapy for improving hand problems.
Keywords: Hand rehabilitation, neurological patient, fuzzy logic, intelligent system
