Trent M Guess1, Rebecca Bliss2, Jamie B Hall2, Andrew M Kiselica3. 1. Department of Physical Therapy, University of Missouri, Columbia, MO, USA; Department of Orthopaedic Surgery, University of Missouri, Columbia, MO, USA. Electronic address: guesstr@health.missouri.edu. 2. Department of Physical Therapy, University of Missouri, Columbia, MO, USA. 3. Department of Health Psychology, University of Missouri, Columbia, MO, USA.
Abstract
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.
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.