User-Independent Freezing of Gait Detection in Parkinson's Patients Using Machine Learning on Edge Devices

Freezing of Gait (FoG) is a debilitating symptom affecting over 50% of Parkinson's disease (PD) patients, significantly increasing the risk of falls and impacting their quality of life. This paper presents a novel, scalable approach to FoG detection using machine learning models and wearable sensors, designed for user-independent operation on resource-constrained embedded platforms. We leverage the DAPHNet dataset, which includes accelerometer data from PD patients, to extract comprehensive time and frequency domain features. Various machine learning classifiers, including resource-efficient models like ProtoNN, are trained and evaluated for their performance in detecting FoG episodes. Our results demonstrate that ProtoNN achieves robust and consistent performance, particularly with larger window sizes and diverse patient data. The findings underscore the importance of model selection, feature extraction and training strategies in developing practical FoG detection systems. This research contributes to the advancement of real-time, portable monitoring solutions, paving the way for improved management of FoG in PD patients.