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A Study on the Effectiveness of Wel-Tech-based Motion Analyzer

  • Young-Hee RO (Future Welfare Convergence Research Institute, Kangnam University) ;
  • Hye-Min KIL (Future Welfare Convergence Research Institute, Kangnam University) ;
  • Hyun-Soo LEE (removing company corp.) ;
  • Hyun-Jin LEE (removing company corp.) ;
  • Yeon-Ah KIM (Kangnam Social Welfare Center) ;
  • Chung-Man GUK (Kangnam Social Welfare Center)
  • 투고 : 2024.11.30
  • 심사 : 2024.12.25
  • 발행 : 2024.12.30

초록

Purpose: TIn an aging society, maintaining the physical and mental health of middle-aged and older adults and enhancing their quality of life have become significant social challenges. This study aimed to explore the effectiveness, limitations, and areas for improvement of AI-based motion analyzers by providing a personalized exercise program to 21 individuals aged 50 and above who utilized the OO Comprehensive Social Welfare Center. Research design, data and methodology: Over six months, participants engaged in personalized exercise programs designed by the motion analyzer, and their results were analyzed. Results: The analysis revealed that AI-based motion analyzers hold promising potential as effective tools for improving the health of middle-aged and older adults. Conclusions: By collecting and quantitatively analyzing participants' exercise performance data in real time, motion analyzers were confirmed to possess practical potential for active application in health management and rehabilitation therapy processes.

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