FoG-Track: Semi-supervised Real-Time Response to Freezing of Gait

Freezing of Gait (FoG) is a prevalent motor dysfunction experienced by patients with Parkinson's disease, and while extensive research has focused on detecting FoG episodes in both clinical and home environments over the past decade, predictive approaches that enable preemptive prompting are still scarce. Most existing studies rely on fully supervised learning conditions, which pose significant challenges. This study introduces a semi-supervised learning framework utilizing a prototype network designed to leverage a FoG prediction model based on impaired gait patterns indicative of pre-FoG, simultaneously harnessing information from unlabeled sensor data for real-time FoG prediction. To validate our framework, we establish pre-FoG state labeling across four datasets: Daphnet, CPGDD, PDTURN, and BXHC. Our developed real-time detection model demonstrates strong performance, achieving up to \(96\%\) sensitivity, \(99\%\) specificity, and \(91\%\) average accuracy on the cross-validation dataset. Furthermore, we compare the accuracy between semi-supervised and fully supervised modes, revealing that the semi-supervised approach yields improvements in average accuracy ranging from 0.01 to 0.02 across three datasets. Thus, our proposed method represents an effective strategy for the accurate prediction of FoG in real-time algorithmic settings.