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Updated: Aug 11, 2026
Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
Published on: November 7, 2014
Autoregressive modeling to assess stride time pattern stability in individuals with Huntington's disease
Helia Mahzoun Alzakerin1, Yannis Halkiadakis1, Kristin D Morgan2
1Biomedical Engineering, School of Engineering, University of Connecticut, 260 Glenbrook Road, Storrs, CT, 06269-3247, USA.
Insights
Autoregressive (AR) modeling of stride time patterns effectively distinguished individuals with Huntington's disease (HD) from controls. This method identified altered gait stability in HD patients, offering a new way to detect gait impairment.
Area of Science:
- Neurology
- Biomechanical Engineering
- Data Science
Background:
- Huntington's disease (HD) is a progressive neurological disorder causing significant cognitive and physical impairments, notably affecting gait and stability.
- Changes in stride time patterns can differentiate between healthy and pathological gait.
- Autoregressive (AR) modeling analyzes temporal patterns in data to assess gait stride time pattern stability.
Purpose of the Study:
- To assess differences in gait stride time pattern stability between individuals with HD and healthy controls using AR modeling.
- To determine if AR model coefficients and their placement on a stationarity triangle can differentiate between HD and control groups.
Main Methods:
- A five-minute walking protocol was conducted with 16 controls and 20 individuals with HD.
- Time series were created from consecutive stride times, and a second-order AR model was applied.
- A two-sample t-test was used to analyze differences in stride time pattern data between groups.
Main Results:
- Individuals with HD showed significantly altered stride time pattern stability compared to controls, based on AR model coefficients (AR1 p < 0.001; AR2 p < 0.001).
- The AR coefficients successfully differentiated between the control and HD groups.
Conclusions:
- AR modeling and stationarity triangle analysis can quantitatively and visually detect gait impairment in individuals with HD.
- HD patients' stride time patterns clustered in the oscillatory region of the stationarity triangle, potentially reflecting altered neuronal activity.
Background:
Huntington's disease (HD) is a progressive, neurological disorder that results in both cognitive and physical impairments. These impairments affect an individual's gait and, as the disease progresses, it significantly alters one's stability. Previous research found that changes in stride time patterns can help delineate between healthy and pathological gait. Autoregressive (AR) modeling is a statistical technique that models the underlying temporal patterns in data. Here the AR models assessed differences in gait stride time pattern stability between the controls and individuals with HD. Differences in stride time pattern stability were determined based on the AR model coefficients and their placement on a stationarity triangle that provides a visual representation of how the patterns mean, variance and autocorrelation change with time. Thus, individuals who exhibit similar stride time pattern stability will reside in the same region of the stationarity triangle. It was hypothesized that individuals with HD would exhibit a more altered stride time pattern stability than the controls based on the AR model coefficients and their location in the stationarity triangle.
Methods:
Sixteen control and twenty individuals with HD performed a five-minute walking protocol. Time series' were constructed from consecutive stride times extracted during the protocol and a second order AR model was fit to the stride time series data. A two-sample t-test was performed on the stride time pattern data to identify differences between the control and HD groups.
Results:
The individuals with HD exhibited significantly altered stride time pattern stability than the controls based on their AR model coefficients (AR1 p < 0.001; AR2 p < 0.001).
Conclusions:
The AR coefficients successfully delineated between the controls and individuals with HD. Individuals with HD resided closer to and within the oscillatory region of the stationarity triangle, which could be reflective of the oscillatory neuronal activity commonly observed in this population. The ability to quantitatively and visually detect differences in stride time behavior highlights the potential of this approach for identifying gait impairment in individuals with HD.
