A Study of Deep Learning for Predicting Freeze of Gait in Patients with Parkinson’s Disease

Abstract

Freezing of gait (FOG) is a gait impairment, common in patients with advanced Parkinson's disease. Predicting FOG before its onset enables preemptive cueing that can prevent FOG or reduce its intensity and duration. Deep learning models have recently been proposed to predict FOG. Such models have feature learning capabilities and do not require the use of hand-crafted features. However, some intricacies that are specific to this approach have not been carefully studied. In particular, the implication of the lack of accurately labelled pre-FOG data, which can have a significant impact on model development and evaluation, has not been fully understood.In this work, we discuss the challenges in deep learning for predicting FOG, illustrate the impact of the lack of accurate pre-FOG data on model development and evaluation, and present a more reliable evaluation method that is independent of the labelling of pre-FOG data. Using this new evaluation method, we study the deep learning schemes for FOG prediction by performing extensive experiments on a public domain dataset. The main conclusions of the study include the following: 1) even without accurate pre-FOG data, deep learning techniques can achieve very high FOG prediction performance while not introducing significant false alarms; 2) traditional deep learning performance metrics such as accuracy, sensitivity, and specificity may not be indicative of the FOG prediction performance; 3) human gait data have high subject-dependent variability, and it requires different deep learning models to achieve the best performance for different individuals; and finally 4) transfer learning is an effective technique for predicting FOG. To the best of our knowledge, this is the first research effort to derive these conclusions via extensive empirical analysis.

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