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

Pedro Arizpe-Gómez and Kirsten Harms and Kathrin Janitzky and Karsten Witt and Andreas Hein
Biomedical Signal Processing and Control
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 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.
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