Predicting Three Types of Freezing of Gait Events Using Deep Learning
  Models
Wen Tao Mo Jonathan H. Chan
Freezing of gait is a Parkinson's Disease symptom that episodically inflicts
a patient with the inability to step or turn while walking. While medical
experts have discovered various triggers and alleviating actions for freezing
of gait, the underlying causes and prediction models are still being explored
today. Current freezing of gait prediction models that utilize machine learning
achieve high sensitivity and specificity in freezing of gait predictions based
on time-series data; however, these models lack specifications on the type of
freezing of gait events. We develop various deep learning models using the
transformer encoder architecture plus Bidirectional LSTM layers and different
feature sets to predict the three different types of freezing of gait events.
The best performing model achieves a score of 0.427 on testing data, which
would rank top 5 in Kaggle's Freezing of Gait prediction competition, hosted by
THE MICHAEL J. FOX FOUNDATION. However, we also recognize overfitting in
training data that could be potentially improved through pseudo labelling on
additional data and model architecture simplification.