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Framework for early detection and classification of balance pathologies using posturography and anthropometric
Arnab Sarmah1, Raghav Aggarwal2, Sarth Sameer Vitekar2
1Department of Mechanical Engineering, IIT Guwahati, Assam, India; Graduate School of Engineering, Gifu University, Gifu, Japan.
Clinical Biomechanics (Bristol, Avon)
|February 23, 2024
Summary
Early detection of adult balance disorders is possible using posturography and personal data. Combining these factors improves accuracy, aiding timely intervention and better quality of life.
Area of Science:
- Biomedical Engineering
- Gerontology
- Clinical Biomechanics
Background:
- Early detection of balance disorders in adults is currently limited.
- Accessible data for identifying early-stage balance issues is needed.
Purpose of the Study:
- To identify adults in early stages of balance disorders.
- To utilize easily accessible and measurable data for early detection.
Main Methods:
- Utilized open-source data from 163 subjects (47 males, 116 females).
- Included posturography (center of pressure displacement), anthropometric/personal variables, and Trail Making Test scores.
- Trained and tested classification algorithms on 75% and 25% of the data, respectively, with validation on a healthy dataset.
Main Results:
- Including anthropometric and personal variables alongside center of pressure features improved classification accuracy and sensitivity.
- The standard deviation of center of pressure displacement was more effective than the mean value.
- Validation showed similar performance improvements, except when using neural networks.
Conclusions:
- Posturography, anthropometric measurements, personal data, and self-assessment scales can effectively identify balance issues in adults.
- This approach is suitable for resource-limited community health centers.
- Early detection facilitates timely medical care, improving disorder management and quality of life through rehabilitation.
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