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
Jan Brederecke
Freezing of Gait (FOG) is a common motor symptom in patients with Parkinson's
disease (PD). During episodes of FOG, patients suddenly lose their ability to
stride as intended. Patient-worn accelerometers can capture information on the
patient's movement during these episodes and machine learning algorithms can
potentially classify this data. The combination therefore holds the potential
to detect FOG in real-time. In this work I present a simple 1-D convolutional
neural network that was trained to detect FOG events in accelerometer data.
Model performance was assessed by measuring the success of the model to
discriminate normal movement from FOG episodes and resulted in a mean average
precision of 0.356 on the private leaderboard on Kaggle. Ultimately, the model
ranked 8th out of 1379 teams in the Parkinson's Freezing of Gait Prediction
competition. The results underscore the potential of Deep Learning-based
solutions in advancing the field of FOG detection, contributing to improved
interventions and management strategies for PD patients.