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

Publication date: Jul 07, 2023

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.


Concepts Keywords
Belgium Competition
Harvard Data
June Detection
Kickstarted Events
Parkinsonism Fog


Type Source Name
disease MESH Gait
drug DRUGBANK Spinosad
drug DRUGBANK Coenzyme M
disease MESH parkinsonism
disease MESH residual block
drug DRUGBANK Resiniferatoxin
drug DRUGBANK Saquinavir
disease MESH etiology
disease MESH Movement Disorders
disease MESH Parkinson Disease
pathway KEGG Parkinson disease

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