Arm motion analysis using genetic algorithm for rehabilitation and healthcare

Takenori Obo, Chu Kiong Loo, Manjeevan Seera, Takahiro Takeda, Naoyuki Kubota

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)81-92
Number of pages12
JournalApplied Soft Computing
Volume52
DOIs
Publication statusPublished - Mar 2017
Externally publishedYes

Keywords

  • Arm motion analysis
  • Image sensor
  • Motion analysis
  • Steady-state genetic algorithm

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