Traditionally, physicians have relied on subjective observations and specialized equipment to gauge balance in individuals with conditions such as Parkinson’s disease, neurological injuries, and age-related decline. These methods, especially the subjective ones, can lack precision, be difficult to administer remotely, and often prove inconsistent. Addressing these limitations, researchers from Florida Atlantic University have developed a novel approach using wearable sensors and advanced machine learning algorithms that could redefine balance assessment practices.

The researchers utilized wearable Inertial Measurement Unit (IMU) sensors placed on five body locations: ankle, lumbar, sternum, wrist, and arm. Data collection followed the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB) protocol, testing four sensory conditions: eyes open and closed on stable and foam surfaces. Each test lasted roughly 11 seconds, simulating continuous balance scenarios.

The scientists preprocessed and extracted features from the raw sensor data, then applied a trio of machine learning algorithms to estimate m-CTSIB scores: multiple linear regression, support vector regression, and the open-source software library XGBOOST.

The researchers trained and validated the models using wearable sensor data as input and corresponding m-CTSIB scores from Falltrak II as ground truth labels. They evaluated the performance of the models through cross-validation methods, correlation with ground truth scores, and Mean Absolute Error (MAE) measures. The XGBOOST model, using lumbar sensor data, yielded the best results, demonstrating high accuracy and strong correlation with ground truth balance scores. The lumbar and dominant ankle sensors produced the highest performance in balance score estimation.

Published in Frontiers in Digital Health, the researchers concluded that their “findings pave the way for more precise and convenient balance assessments.” They stated that the approach has “immense potential to enhance balance performance assessment and management in various settings, including clinical environments, rehabilitation, and remote monitoring.”

By Impact Lab