Smartphone-Based Balance Assessment Using Machine Learning.
Journal:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
PMID:
40039846
Abstract
This study explores the potential of smartphones to objectively assess balance, which is crucial for the elderly and individuals recovering from various medical conditions. We propose an innovative methodology to estimate the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB) scores using the accelerometer sensor of a smartphone coupled with machine learning techniques. Our dataset consists of 28 participants, aged 21 to 88 years. Notably, the XGBOOST algorithm demonstrates a strong correlation (0.92) with the ground truth balance scores. These ground truth scores are obtained using a force plate system collected simultaneously with the smartphone data, ensuring precise and reliable comparisons. This methodology offers an objective, accessible, and convenient means for balance assessment, greatly facilitating at-home monitoring and enhancing the potential for remote health monitoring. Our findings underscore the method's reliability and potential impact on telemedicine and patient care, offering notable improvements in the quality of life.