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Updated: Apr 27, 2026
Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
Published on: November 7, 2014
The use of regression and normalisation for the comparison of spatio-temporal gait data in children
P C Dixon1, M V Bowtell2, J Stebbins2
1Department of Engineering Science, University of Oxford, Oxford, UK.
Insights
Actual spatio-temporal parameters (STPs) in children
Area of Science:
- Biomechanics
- Pediatric Gait Analysis
- Motor Control
Background:
- Spatio-temporal parameters (STPs) are crucial for assessing gait in children, but normalization methods may not fully account for inter-subject variability.
- Comparing gait in children with and without disabilities often relies on matched databases or regression models, which have limitations.
- Previous regression models for gait prediction used insufficient parameters, limiting their success.
Purpose of the Study:
- To evaluate the predictive accuracy of actual and non-dimensionally normalized (ND) spatio-temporal parameters (STPs) using anthropometrics and walking speed in typically developing children.
- To determine if self-selected walking speed can be predicted from anthropometric data using multivariate regression.
- To validate predictive equations on an independent dataset.
Main Methods:
- Multivariate regression analysis was employed to predict STPs and self-selected speed from anthropometric measurements and gait speed in 89 typically developing children.
- Actual and leg-length normalized STPs were compared for their predictive power.
- Predictive models were validated using an external dataset.
Main Results:
- Predictive equations for actual step length, stride length, and cadence explained over 84% of the variance, outperforming their normalized counterparts.
- Only leg-length normalized versions of these parameters showed a linear relationship with walking speed.
- Prediction of single and double limb support times was moderate (R²=0.69 and 0.72), and self-selected speed was poorly predicted (R²<0.16).
Conclusions:
- Actual STPs are more reliably predicted by anthropometrics and speed than normalized STPs in children.
- Anthropometric data alone is insufficient for predicting self-selected walking speed.
- Regression models offer a potential clinical tool for quantifying gait deviations and monitoring intervention progress, with validation showing generalizability but limitations for atypical ranges and gait speed.
Abstract:
Spatio-temporal parameters (STPs) are fundamental gait measures often used to compare children of different ages or gait ability. In the first case, non-dimensional normalisation (ND) of STPs using either leg-length or height is frequently conducted even though the process may not remove known inter-subject variability. STPs of children with and without disability can be compared through matched databases or using regression driven prediction. Unfortunately, database assignment is largely arbitrary and previous regressions have employed too few parameters to be successful. Therefore, the aims of this study were to test how well actual and ND STPs could be predicted from anthropometrics and speed and to assess if self-selected speed could be predicted from anthropometrics using multivariate regression in a cohort of eighty-nine typically developing children. Equations were validated on an extraneous dataset. We found that equations for actual step length, stride length, and cadence explained more than 84% of the variance compared to their ND counterparts. Moreover, only leg-length ND versions of these parameters were linearly proportional to speed. Prediction of single and double limb support times was weaker (R(2)=0.69 and 0.72, respectively) and we were unable to predict self-selected speed (R(2)<0.16) suggesting the use of anthropometrics is inappropriate for this purpose. Validation was successful for most STPs except in children lying near or outside the normal ranges and for gait speed. Clinically, regression could be used to quantify the difference between a patient's actual and theoretical STPs, allowing for monitoring of progress pre- and post intervention.
