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Freezing of gait (FoG) is a common and severe gait impairment among patients with advanced Parkinson’s disease. FoG is associated with falls and negatively impacts the patient’s quality of life. Wearable systems that detect FoG in real time have been developed to help patients resume walking by means of rhythmic cueing. However, current methods focus on detection, which require FoG events to happen first. Predicting that such episodes with few seconds before opens the road to preemptive cueing, which might help subjects to avoid freeze altogether. We analyzed wearable collected electrocardiography (ECG) and skin- conductance response (SCR) data from 11 subjects who experience FoG in daily-life, and found statistically significant changes in ECG and SCR data just before the FoG episodes, compared to normal walking. Based on these findings, we developed an anomaly-based algorithm for predicting gait freeze from relevant SCR features. We were able to predict 71.3% from 184 FoG episodes in the CuPiD dataset, with an average of 4.2 seconds before a freeze episode happened. Our findings enable the possibility of wearable systems, which predict with few seconds before an upcoming FoG from skin conductance, and start external cues to help the user avoid the freezing of gait episodes.
The post Prediction of Freezing of Gait in Parkinson’s from Physiological Wearables: An Exploratory Study appeared first on Journal of Biomedical and Health Informatics.
