Freezing of Gait Prediction Using Deep Learning
Conference proceedings article
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Publication Details
Author list: Mo, Wen Tao; Chan, Jonathan H.
Publisher: Association for Computing Machinery
Publication year: 2023
Title of series: IAIT 2023: 13th International Conference on Advances in Information Technology
ISBN: 979-840070849-7
Languages: English-Great Britain (EN-GB)
Abstract
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. ฉ 2023 Owner/Author.
Keywords
Freezing of Gait
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