Ricerc@Sapienza

Freezing of Gait (FOG) is an unpredictable gait disorder typical of Parkinson's Disease (PD). The main goals of this work are detecting FOG episodes, classifying FOG subtypes and analyzing the leg muscles activity toward a deeper insight into the disorder pathophysiology and in the associated risk of fall. Fusion of inertial and electromyographic signals in our wearable system allows distinguishing correctly 98.4% of FOG episodes and monitoring in free-living conditions the activity type and intensity of leg antagonist muscles involved in FOG.

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson's disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability.