Towards Automated Self-Administered Motor Status Assessment: Validation of a Depth Camera System for Gait Feature Analysis

Abstract

Background: Gait 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 question: Can an AzureKinect-based system measure basic gait parameters with sufficient accuracy for motor status assessments? Methods: The 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. 

Results: The 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. 

Significance: The AzureKinect-based system is able to measure basic gait parameters with a 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.

Note:

Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.00

Declaration of Interests: None.

Ethics Approval Statement: The study was approved by the local ethics committee (Medizinische Ethikkommission der Carl von Ossietzky Universität Oldenburg (medical ethics committee of the CvO University of Oldenburg). All participants gave their consent to participate in the study in written form. The study was registered (DRKS00020921).

Keywords: Gait analysis, GAITRite, RGB-D Camera, Microsoft Azure Kinect, Reliability analysis