Arm motion analysis using genetic algorithm for rehabilitation and healthcare

被引：10

作者：

Obo, Takenori ^{[1
]}

Loo, Chu Kiong ^{[1
]}

Seera, Manjeevan ^{[2
]}

Takeda, Takahiro ^{[3
]}

Kubota, Naoyuki ^{[3
]}

机构：

[1] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur 50603, Malaysia

[2] Swinburne Univ Technol Sarawak Campus, Fac Engn, Comp & Sci, Sarawak 93350, Malaysia

[3] Tokyo Metropolitan Univ, Grad Sch Syst Design, 6 6 Asahigaoka, Hino, Tokyo 1910065, Japan

来源：

APPLIED SOFT COMPUTING | 2017年 / 52卷

关键词：

Arm motion analysis; Image sensor; Motion analysis; Steady-state genetic algorithma; COGNITIVE REHABILITATION; KINECT; SYSTEM; VISION; VALIDITY;

D O I：

10.1016/j.asoc.2016.12.025

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2016 Elsevier B. V. All rights reserved.

引用

页码：81 / 92

页数：12

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