Efficiently Identifying Non-FoG, Pre-FoG, Pre-FoG Transition, and FoG in Parkinson’s Disease Patients Using Window Acceleration and Spline Function Features

Abstract

Freezing of gait (FoG) is an episodic symptom that disrupts walking initiation in patients with Parkinson’s disease (PD). Identifying pre-FoG stages is crucial for patients with PD. However, in contrast to typical disease labels, defining the FoG and pre-FoG states during PD gait data collection is challenging. The sliding window method can be used to increase data volume; however, the labeling of windows according to fixed FoG or pre-FoG data point thresholds is insensitive to PD severity. Therefore, this study proposes a novel algorithm that dynamically defines labels on the basis of the collected gait data. Overlapping windows are used to augment these data, and sensitivity analysis is conducted to assess the effect of the overlap rate on classification. This study used accelerometer data collected from UCI, which included 10 high-risk of FOG participants and worn on their ankles, thighs, and trunk. Based on this experiment, our approach achieved a sensitivity of 89% and a specificity of 92% for identifying all FoG stages. Moreover, it exhibited a sensitivity of 96% and specificity of 88% for detecting the pre-FoG state (2 s prior to FoG). The aforementioned results were obtained with only 25 key features, thus reducing the computational demand. Furthermore, the risk of overfitting was low for an overlap rate below 25%. This study highlights the importance of dynamic label assignment for the accurate classification of FoG stages and provides the important features for FoG detection.