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Background: Instrumented measurement of spatiotemporal parameters during walking can provide valuable information on an individual's overall function and health. Efficient, inexpensive, and accurate measurement of overground walking spatiotemporal parameters would be a critical component of providing point-of-care assessments of gait function, concussion recovery, fall-risk, and cognitive decline. Depth cameras combined with skeleton pose tracking algorithms, such as the Microsoft Kinect with body tracking software, have been used to measure walking spatiotemporal parameters. However, the ability of the latest generation Microsoft Kinect sensor, the Azure Kinect, to accurately measure overground walking spatiotemporal parameters has not been evaluated in the literature.Research Question: The purpose of this work was to compare overground walking spatiotemporal parameters measurements from a 12 camera Vicon optical motion capture system to measurements of a single Azure Kinect with body tracking SDK (software development kit).Methods: Spatiotemporal parameters of overground walking were simultaneously collected on twenty young healthy participants. Stride length, stride time, step length and step width were derived from ankle joint center locations and measurements from the two instruments were compared using descriptive statistics, scatter plots, Pearson correlation analyses, and Bland-Altman analyses.Results: Pearson correlation coefficients were greater than 0.87 for all spatiotemporal parameters with most parameters demonstrating very strong (> 0.9) agreement. The mean of the differences for stride length between measurements was 35.6 mm for the left limb and 39.1 mm for the right limb, both of which are less than 3% of average stride length. Mean of the differences for step width and stride time were less than 2% and 1% of their averages respectively.Significance: A single Microsoft Azure Kinect with body tracking SDK can provide clinically relevant measurement of walking spatiotemporal parameters, providing accessible and objective measurements that can improve clinical decision making across a variety of patient populations. [ABSTRACT FROM AUTHOR]

Background: The sit-to-stand test (STS) is commonly used to evaluate functional capabilities within a variety of clinical populations. Traditionally STS is a timed test, limiting the depth of information which can be gained from its evaluation. The Azure Kinect has the potential to add in-depth analysis to STS. Despite these potential benefits, the recently released (2019) Azure Kinect has yet to be evaluated for its ability to accurately assess STS.Research Questions: Purposes of this work were to compare data captured during STS using both a 12 camera Vicon motion capture system and the Azure Kinect; and to calculate kinematic and spatiotemporal variables related to the four phases of the STS cycle.Methods: Spatiotemporal and kinematic measures for STS were simultaneously collected by both devices for 15 participants. Cycle waveforms were compared for right and left hip and knee flexion/extension angular displacement, right and left hip and knee flexion/extension angular velocity, and knee-to-ankle separation ratio. Evaluated discrete outcome variables included: phase time points, maximum knee extension velocity from phases 3 to 4, medial-lateral pelvic sway range, and total time to completion. Waveform summary data were compared using R, R2, and RMSE. Discrete variables were analyzed using Spearman's Rank correlation coefficient.Results: R and R2 values between the two systems indicated high levels of correlation (all R values > 0.711, all R2 values > 0.660). Although there was an overall high level of agreement between waveform shapes, high RMSE values indicated some minor tracking errors for Kinect within the STS cycle. Spearman's Rank correlation coefficient indicated high levels of correlation between the systems for discrete variables (all R values > 0.89), with the exception of medial-lateral pelvic sway range.Significance: The Azure Kinect provides valuable insight into STS movement strategies allowing for improved precision in clinical decision making across multiple clinical populations. [ABSTRACT FROM AUTHOR]