Freezing of gait detection: The effect of sensor type, position, activities, datasets, and machine learning model

Freezing of gait (FoG) is a complex, frequent, and disabling motor symptom of Parkinson’s disease (PD). Wearable technology has the potential to improve FoG assessment by providing objective, quantitative, and continuous monitoring. This study aims to develop a robust FoG detection algorithm that can be embedded in a simple and unobtrusive wearable sensor system and can lead to a reliable unsupervised home assessment. Twenty-two subjects with PD and FoG were enrolled, equipped with four inertial modules on the ankles, back, and wrist, and asked to perform different tasks. Feature-driven and data-driven machine learning approaches were implemented, optimized, and evaluated. Further testing was conducted on two external datasets including a total of 545 FoG episodes. Sixteen subjects experienced FoG, providing a total number of 101 FoG events. Results demonstrated that a single sensor on the ankle, with an adequate algorithm of data analysis based on machine learning, can provide a non-invasive approach for accurate FoG detection. The model proved robust on the independent datasets, with 88–95% FoG episodes correctly detected. Interestingly, while FoG can be easily discriminated from walking, static positions, and postural transitions, turning represents a significant challenge. The high number of false alarms still represents the main limitation of the FoG recognition algorithms. The collected dataset includes data from different sensors at different body positions. This, together with detailed labeling of tasks, activities, FoG episodes and their severity, can be a significant contribution to research on automatic FoG detection and characterization.