Characterizing freezing of gait episodes for Parkinson's disease using a wearable device quantifying gait and posture

Abstract

Purpose: With disease progression, individuals with Parkinson’s disease (PD) experience freezing of gait (FoG). Certain motor, cognitive and environmental factors can trigger freezing. Investigators have been focusing on studying different gait-related indices, e.g., Step Time, Double Limb Support Time and postural indices, e.g., Knee Flexion of these individuals while walking under different task conditions and turning on a pathway. However, none of the existing studies have focused on identifying specific gait-related and postural indices that can be used to characterize one's FoG (under free living conditions) and distinguish freezing episodes from those in which no freezing occurs using wearable and non-wearable systems. This is important since once the freezing episodes are characterized and identified, it will enable systems to autonomously adopt measures for addressing occurrence of such episodes. Methods: Motivated by this need, we have come up with a wearable device (SmartGait) that comprises Sensored insoles integrated with Knee Flexion module to identify gait-related and postural indices.Results: Results of our study with fourteen healthy and fourteen age and gender-matched individuals with PD showed that among the gait-related and postural indices being studied here, the variability in Step Time emerged as a powerful index to characterize and identify the FoG episodes and distinguish such episodes from those with no freezing with accuracy ≥ 80%. Conclusion: The variability in Step Time strongly corroborated with clinical measure of disease progression, thereby offering pre-clinical input to clinicians working with individuals with PD.