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Freezing of Gait (FOG) is a common motor symptom in patients with Parkinson'sdisease (PD). During episodes of FOG, patients suddenly lose their ability tostride as intended. Patient-worn accelerometers can capture information on thepatient's movement during these episodes and machine learning algorithms canpotentially classify this data. The combination therefore holds the potentialto detect FOG in real-time. In this work I present a simple 1-D convolutionalneural network that was trained to detect FOG events in accelerometer data.Model performance was assessed by measuring the success of the model todiscriminate normal movement from FOG episodes and resulted in a mean averageprecision of 0.356 on the private leaderboard on Kaggle. Ultimately, the modelranked 8th out of 1379 teams in the Parkinson's Freezing of Gait Predictioncompetition. The results underscore the potential of Deep Learning-basedsolutions in advancing the field of FOG detection, contributing to improvedinterventions and management strategies for PD patients.

Freezing of Gait Prediction From Accelerometer Data Using a Simple  1D-Convolutional Neural Network -- 8th Place Solution for Kaggle's  Parkinson's Freezing of Gait Prediction Competition