Integrating Wearable Sensor Technology and Machine Learning for Objective m-CTSIB Balance Score Estimation.

This study marks the first endeavor to utilize wearable technology combined with machine learning to objectively assess the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB). We focus on developing an affordable, easily accessible method for balance assessment, critical for adults at risk of falls and cognitive decline. Our novel approach uses a single inertial measurement unit sensor (APDM, INC.) to gather lumbar accelerometer and gyroscope data. This data is accompanied by ground truth scores obtained from m-CTSIB tests on a force plate (Falltrak II, MedTrak VNG, Inc.) from 34 participants aged 21 to 88. Using XGBOOST, we achieve a remarkable 0.94 correlation using accelerometer data and 0.90 with gyroscope data in the test dataset, demonstrating a strong correlation with actual scores in a subject-wise leave-one-out cross-validation. Offering objectivity, affordability, and potential for remote monitoring, our innovative approach holds promise for enhancing the diagnosis and management of balance disorders in adults, thereby improving their quality of life and independence.