Using data from the Microsoft Kinect 2 to determine postural stability in healthy subjects: A feasibility trial

Abstract

The objective of this study was to determine whether kinematic data collected by the Microsoft Kinect 2 (MK2) could be used to quantify postural stability in healthy subjects. Twelve subjects were recruited for the project, and were instructed to perform a sequence of simple postural stability tasks. The movement sequence was performed as subjects were seated on top of a force platform, and the MK2 was positioned in front of them. This sequence of tasks was performed by each subject under three different postural conditions: “both feet on the ground” (1), “One foot off the ground” (2), and “both feet off the ground” (3). We compared force platform and MK2 data to quantify the degree to which the MK2 was returning reliable data across subjects. We then applied a novel machine-learning paradigm to the MK2 data in order to determine the extent to which data from the MK2 could be used to reliably classify different postural conditions. Our initial comparison of force plate and MK2 data showed a strong agreement between the two devices, with strong Pearson correlations between the trunk centroids “Spine_Mid” (0.85 ± 0.06), “Neck” (0.86 ± 0.07) and “Head” (0.87 ± 0.07), and the center of pressure centroid inferred by the force platform. Mean accuracy for the machine learning classifier from MK2 was 97.0%, with a specific classification accuracy breakdown of 90.9%, 100%, and 100% for conditions 1 through 3, respectively. Mean accuracy for the machine learning classifier derived from the force platform data was lower at 84.4%. We conclude that data from the MK2 has sufficient information content to allow us to classify sequences of tasks being performed under different levels of postural stability. Future studies will focus on validating this protocol on large populations of individuals with actual balance impairments in order to create a toolkit that is clinically validated and available to the medical community.

Usability Test of Exercise Games Designed for Rehabilitation of Elderly Patients After Hip Replacement Surgery: Pilot Study

“…This allows the virtual character on the screen to mirror the movements of the user in real time. Earlier studies concluded that Kinect v2 has the potential to be used as a reliable and valid clinical measurement tool [ 17 - 19 ]. Hence, the physiotherapists can set the range of knee angle and hip angle as they usually do in the traditional rehabilitation training when they use the exercise games.…”

An Egg Volume Measurement System Based on the Microsoft Kinect

“…Considering the improved hardware of Kinect 2.0, and higher accuracy, typically less than 2 mm within 2 m object-to-sensor distance [ 18 ], the Kinect 2.0 was used in this work. There are many application examples of the Kinect 2.0: biomedical engineering [ 19 , 20 , 21 ]; indoor mapping [ 22 ] and structural deformation measurement [ 23 ].…”

The Validity and Reliability of the Microsoft Kinect for Measuring Trunk Compensation during Reaching

“…The measurement abilities of the first-generation Kinect (K1) have been established for UE movements, spatiotemporal gait variables, standing balance, postural control, and even static foot posture [ 9 , 10 , 13 , 14 , 15 ]. The abilities of the second-generation Kinect (K2) are not as robustly established, but have been investigated for some UE, gait, and postural movements [ 11 , 16 , 17 ]. A recent study within our laboratory found both sensors to be valid relative to the gold standard of a VMC system when measuring reaching (forward and side) and angular shoulder movements (frontal, transverse, sagittal) [ 12 ].…”