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Perceptions of a machine learning-based lower-limb exercise training system among older adults with knee pain
Tianrong Chen1, Calvin Kalun Or1
1Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China.
Digital Health
|July 10, 2023
Summary
Older adults with knee pain showed positive perceptions of a machine learning exercise system prototype. Factors like age and pain duration influenced their views, suggesting potential for digital health solutions.
Area of Science:
- Gerontology
- Biomedical Engineering
- Rehabilitation Science
Background:
- Knee pain is prevalent in older adults, limiting exercise and impacting quality of life.
- Machine learning (ML) offers potential for personalized exercise interventions.
- A system combining video demonstrations, real-time feedback, and progress tracking could aid knee health management.
Purpose of the Study:
- To evaluate older adults' perceptions of a paper-based prototype for an ML-based lower-limb exercise system.
- To identify factors influencing user perceptions of the proposed system.
Main Methods:
- A cross-sectional survey (N=94) assessed perceptions of system effects, ease of use, attitude, and intention to use.
- Ordinal logistic regression analyzed associations between perceptions and demographic/clinical characteristics, physical activity, and exercise experience.
Main Results:
- Participants showed strong agreement (≥75%) with perception statements.
- Age, gender, knee pain duration/intensity, exercise therapy experience, and technology-supported exercise experience significantly influenced perceptions.
Conclusions:
- The ML-based exercise system shows promise for older adults managing knee pain.
- Further development into a computer-based system is warranted, with investigation into usability, acceptance, and clinical effectiveness.
