Towards automated self-administered motor status assessment: Validation of a depth camera system for gait feature analysis

Abstract

BackgroundGait feature analysis plays an important role in diagnosing and monitoring diseases that compromise motor function. This article presents the results of a study, which was aimed at assessing the accuracy and precision of computer-aided gait feature analysis performed with a system based on Microsoft® Azure™ Kinect™ Cameras (AzureKinect). Research questionCan an AzureKinect-based system measure basic gait parameters with sufficient accuracy for motor status assessments? MethodsThe presented AzureKinect-based system was evaluated by measuring the step length (SL), cadence, and velocity, which are important gait features, of both healthy participants and participants with a neurological motor impairment (total number of participants: N = 24). The GAITRite® system, which is an established gold standard for gait analysis, was used as the ground truth. ResultsThe results show that the AzureKinect-based system can provide measurements of average SL, cadence, and velocity. A comparison with the ground truth revealed a mean absolute error (MAE) of 1.74 cm in SL, 4.6 cm/s in gait velocity and 6.3 steps/min for cadence. Pearson’s correlation coefficients range from r = 0.8 to r = 0.99, demonstrating a very high correlation between the measurements of the AzureKinect system and the ground truth. SignificanceThe AzureKinect-based system is able to measure basic gait parameters with sufficient accuracy. This is a first step towards a comprehensive self-measuring marker-less camera-based kinematic analysis that could be performed at home or in general medical practices.