A PATIENT INVARIANT MODEL TOWARDS THE PREDICTION OF FREEZING OF GAIT

Freezing of Gait (FoG) is one of the incapacitating motor symptoms that appear in patients with Parkinson's Disease (PD). FoG manifests gait impairments and imposes unforeseen difficulties in commencing the locomotion. Frequent episodes of FoG often lead to fall-related injuries and impart dreadful health repercussions. Prediction of FoG before the occurrence could potentially increase the opportunities for pre-emptive cueing to mitigate or abate the episode of FoG. This paper presents a novel algorithm to predict the onset of FoG using a single ankle accelerometer sensor. Principally, we have focused on designing a lightweight algorithm to facilitate the real-time prediction of FoG in resource-limited hardware. A novel Genetic Algorithm (GA) is introduced as a feature selection method to enhance the algorithm's performance. We have adapted the patient-invariant model which is a more viable approach in practical deployment. The algorithm is evaluated using the Daphnet dataset and achieved 88% FoG prediction accuracy with 1 second prediction time.