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Predicting the performance of assistive device for elderly people using weighted KNN machine learning algorithm.

S Vaisali1, C Maheswari1, S Shankar2,3

  • 1Department of Mechatronics Engineering, Kongu Engineering College, Erode, Tamil Nadu, India.

Journal of Back and Musculoskeletal Rehabilitation
|March 19, 2025
PubMed
Summary

This study shows an upper limb exoskeleton reduces muscle fatigue in the elderly during weight lifting. A machine learning algorithm predicts device suitability for individuals, aiding assistive technology development.

Keywords:
Exo-skeletonUpper limb Exo-skeletonergonomicsweighted K-Nearest Neighbors algorithm

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Artificial Intelligence in Healthcare

Background:

  • Aging often leads to decreased muscle strength and endurance in the elderly, impacting daily activities and independence.
  • Limited mobility and strength hinder the ability to perform routine tasks, affecting overall quality of life.

Purpose of the Study:

  • To evaluate the effectiveness of a developed upper limb exoskeleton for weight lifting in elderly individuals.
  • To predict the suitability of the exoskeleton using ergonomic analysis and a weighted K-Nearest Neighbors (KNN) machine learning algorithm.

Main Methods:

  • Experimental measurements of Maximum Voluntary Isometric Contraction (MVIC) and Mean Power Frequency (MPF) were taken before and after device use.
  • Ergonomic analysis and a weighted KNN algorithm were employed to assess muscle strength and predict device effectiveness.
  • Elderly subjects performed weight-lifting tasks with and without the exoskeleton to measure muscle response.

Main Results:

  • Exoskeleton use reduced %MVIC values significantly during 5kg and 15kg weight lifting.
  • Muscle fatigue in Biceps Brachii and flexor carpi radialis increased without the exoskeleton but decreased with its use.
  • %MVIC values ranged from 2-6% (no load), 25-40% (5kg), and 30-71% (15kg) with the device.

Conclusions:

  • The developed upper limb exoskeleton effectively reduces muscle fatigue and compensates for strength loss in the elderly during weight lifting.
  • A weighted KNN algorithm can predict exoskeleton suitability based on Body Mass Index and muscle fatigue levels.
  • Findings support the development of user-friendly assistive devices, highlighting the role of ergonomics and AI in enhancing rehabilitation technology.