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Smartphone-Based Hand Function Assessment: Systematic Review
Yan Fu1, Yuxin Zhang1, Bing Ye2,3
1School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
Journal of Medical Internet Research
|September 16, 2024
Summary
Smartphones offer a promising, accessible tool for assessing hand function, utilizing built-in sensors like accelerometers and cameras. Machine learning methods effectively analyze this data for disease detection and severity evaluation.
Area of Science:
- Biomedical Engineering
- Digital Health
- Rehabilitation Technology
Background:
- Traditional hand function assessments face challenges in validity, reliability, and data management.
- Smartphones present a cost-effective and accessible solution for objective hand function evaluation.
- Utilizing built-in smartphone sensors can overcome limitations of conventional assessment methods.
Purpose of the Study:
- To systematically review and evaluate existing research on smartphone-based hand function assessment.
- To identify common hand dysfunctions and assessment methods utilizing smartphone technology.
Main Methods:
- A comprehensive literature search was conducted across 8 databases.
- Studies were screened and appraised using the Mixed Methods Appraisal Tool.
- Data extraction focused on study characteristics, sensors, and analytical methods (statistical and machine learning).
Main Results:
- 46 studies were included, identifying 11 types of hand dysfunctions and 6 specific functional impairments.
- Accelerometers and cameras were the most frequently used smartphone sensors.
- Machine learning algorithms showed promise for disease detection, severity evaluation, and prediction.
Conclusions:
- Smartphone-based assessment is a viable and promising approach for evaluating hand function.
- Machine learning is effective for classifying hand dysfunction levels using smartphone data.
- Future research should focus on establishing a gold standard, leveraging multiple sensors, and developing real-time ML applications for rehabilitation.
