Deep Domain Adaptation to Predict Freezing of Gait in Patients with Parkinson's Disease

Freezing of gait (FoG) is a common gait impairment in patients with advanced Parkinson's disease (PD), which manifests as sudden difficulties in starting or continuing locomotion. FoG often results in falls and negatively affect a patient's quality of life. Real-time detection algorithms have been developed, which detect FoG events using signals derived from wearable sensors. However, predicting FoG before it actually occurs opens the possibility of preemptive cueing, which can potentially avoid (or reduce the intensity and duration of) the episodes. Moreover, human gait involves significant subject-based variability and a machine learning model trained on a particular patient's data may not generalize well to other patients. In this paper, we study the performance of advanced deep learning algorithms to predict FoG events in short time durations before their occurrence. We further study the performance of domain adaptation (or transfer learning) algorithms to address the domain disparity between data from different subjects, in order to develop a better prediction model for a particular subject. To the best of our knowledge, this is the first research effort to study domain adaptation algorithms to predict FoG episodes in patients with PD. Our extensive empirical studies on a publicly available dataset (collected from 10 PD patients) demonstrate the potential of our algorithms to accurately identify FoG events before their onset. We believe this research will serve as a stepping stone toward the development of more advanced FoG prediction algorithms for patients with PD.