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.
Continue reading… “Revolutionizing Balance Assessment: AI and Wearable Sensors Lead the Way”