Time series modeling characterizes stride time variability to identify individuals with neurodegenerative disorders

Abstract

The progressive death and dysfunction of neurons causes altered stride-to-stride variability in individuals with Amyotrophic Lateral Sclerosis (ALS) and Huntington's Disease (HD). Yet these altered gait dynamics can manifest differently in these populations based on how and where these neurodegenerative disorders attack the central nervous system. Time series analyses can quantify differences in stride time variability which can help contribute to the detection and identification of these disorders. Here, autoregressive modeling time series analysis was utilized to quantify differences in stride time variability amongst the Controls, the individuals with ALS, and the individuals with HD. For this study, fifteen Controls, 12 individuals with ALS and 15 individuals with HD walked up and down a hallway continuously for 5-min. Participants wore force sensitive resistors in their shoes to collect stride time data. A second order autoregressive (AR) model was fit to the time series created from the stride time data. The mean stride time and two AR model coefficients served as metrics to identify differences in stride time variability amongst the three groups. The individuals with HD walked with significantly greater stride time variability indicating a more chaotic gait while the individuals with ALS adopted more ordered, less variable stride time dynamics (p < 0.001). A plot of the stride time metrics illustrated how each group exhibited significantly different stride time dynamics. The stride time metrics successfully quantified differences in stride time variability amongst individuals with neurodegenerative disorders. This work provided valuable insight about how these neuromuscular disorders disrupt motor coordination leading to the adoption of new gait dynamics.