Steady state gait and beyond: Complex mobility tasks in the laboratory reveal gait changes in Parkinson's Disease.

BACKGROUND: Parkinson's Disease (PD) is a progressive neurodegenerative disorder that impairs gait and balance. However, most existing objective gait assessments focus on parameters from steady state gait (SSG) and coarse measures of turns. As a result, these analytical approaches overlook the challenges people with PD encounter during more complex mobility tasks such as gait initiation (GI), turning, and gait termination (GT). Identifying relevant parameters of complex mobility tasks would improve mobility assessments. PURPOSE: The primary focus of this study was to evaluate gait parameters across steady-state walking and complex mobility tasks (gait initiation, gait termination, and turning) and determine whether complex tasks reveal differences that are not apparent during steady-state gait. METHODS: IMU data from 91 PD and 78 control participants were used from the open-access WearGait-PD dataset. Signals from one lower back and two foot-mounted IMUs were processed using a temporal convolutional network for stride segmentation, followed by trajectory reconstruction and spatiotemporal parameter extraction. Stride length, gait velocity, swing time, max sensor lift, and initial/final contact (IC/TC) angles were calculated across SSG, turn, GI, and GT strides. Linear mixed-effects models assessed associations between gait parameters and disease status (PD vs. control) and severity (H&Y stage). RESULTS: SSG stride length and velocity decreased significantly with disease severity at higher H&Y stages. Complex gait tasks revealed additional sensitive parameters: max sensor lift, swing time, and IC angle showed significant associations with disease severity during turning, GI, and GT strides. SIGNIFICANCE: Stride-level analysis of complex mobility tasks identified statistically significant gait parameters beyond what SSG measures capture.