Abstract
Clinical gait analysis attempts to provide, in a pathological context, an objective record that quantifies the magnitude of deviations from normal gait. However, the identification of deviations is highly dependent with the characteristics of the normative database used. In particular, a mismatch between patient characteristics and an asymptomatic population database in terms of walking speed, demographic and anthropometric parameters may lead to misinterpretation during the clinical process. Rather than developing a new normative data repository that may require considerable of resources and time, this study aims to assess a method for predicting lower limb sagittal kinematics using multiple regression models based on walking speed, gender, age and BMI as predictors. With this approach, we were able to predict kinematics with an error within 1 standard deviation of the mean of the original waveforms recorded on fifty-four participants. Furthermore, the proposed approach allowed us to estimate the relative contribution to angular variations of each predictor, independently from the others. It appeared that a mismatch in walking speed, but also age, sex and BMI may lead to errors higher than 5° on lower limb sagittal kinematics and should thus be taken into account before any clinical interpretation.
Highlights
Clinical gait analysis (CGA) is nowadays fully integrated in the clinical decision-making for patients with complex gait disorders[1]
The second aim was to determine parameters that may have an influence on CGA interpretation in case of parameters mismatched with the normative database
The methodology applied in this study was illustrated on lower limb sagittal kinematics during gait for the sake of simplicity, but can be extended to all the parameters used in CGA
Summary
Clinical gait analysis (CGA) is nowadays fully integrated in the clinical decision-making for patients with complex gait disorders[1]. Because walking speed is known to affect kinematics, kinetics, spatiotemporal parameters and muscular activity[7], the identification of gait deviations can become challenging since both pathology and walking speed difference may contribute to them[8]. This can be illustrated with the knee flexion amplitude during gait. The second aim was to determine parameters that may have an influence on CGA interpretation (i.e. identification of gait deviations) in case of parameters mismatched with the normative database This was achieved by applying the previously defined regressors for different values of one isolated predictor. The methodology applied in this study was illustrated on lower limb sagittal kinematics during gait for the sake of simplicity, but can be extended to all the parameters used in CGA (e.g. kinetics, EMG, spatiotemporal parameters)
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