Arm motion analysis using genetic algorithm for rehabilitation and healthcare |
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| Affiliation: | 1. Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Faculty of Engineering, Computing and Science, Swinburne University of Technology (Sarawak Campus), 93350 Sarawak, Malaysia;3. Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan;1. Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;2. Department of Computer Engineering, Hashtgerd Branch, Islamic Azad University, Alborz, Iran;1. Department of Electrical & Computer Engineering, Semnan University, Semnan, Iran;2. Department of Electrical, Biomedical, and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran;1. Ind. Eng., Alzahra University, Tehran, Iran;2. Ind. Eng., Shahed University, Tehran, Iran;1. Department of Mathematics, Faculty of Arts and Sciences, Eastern Mediterranean University, Famagusta, North Cyprus, Mersin-10, Turkey;2. Department of Computer Science and Information Technology, Faculty of Electrical Engineering and Information Technology, University of Oradea, Oradea, Romania;1. Department of Computational Intelligence, Faculty of Computer Science and Management Wroclaw, University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland;2. Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland |
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| Abstract: | The worlds population is quickly aging. With an aging society, an increase in patients with brain damage is predicted. In rehabilitation, the analysis of arm motion is vital as various day to day activities relate to arm movements. The therapeutic approach and evaluation method are generally selected by therapists based on his/her experience, which can be an issue for quantitative evaluation in any specific movement task. In this paper, we develop a measurement system for arm motion analysis using a 3D image sensor. The method of upper body posture estimation based on a steady-state genetic algorithm (SSGA) is proposed. A continuous model of generation for an adaptive search in dynamical environment using an adaptive penalty function and island model is applied. Experimental results indicate promising results as compared with the literature. |
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| Keywords: | Arm motion analysis Image sensor Motion analysis Steady-state genetic algorithm |
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