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The Use of Microsoft Kinect for Human Movement Analysis

Profile image of Chelsey LeesChelsey Lees

2015, International Journal of Sports Science

Abstract

The purpose of this study was to provide evidence of reliability and validity for the use of a Microsoft Kinect system to measure displacement in human movement analysis. Three dimensional (3D) video motion systems are commonly used to analyze human movement kinematics of body joints and segments for many diverse applications related to gait analysis, rehabilitation, sports performance, medical robotics, and biofeedback. These systems, however, have certain drawbacks pertaining to the use of markers, calibration time, number of cameras, and high cost. Microsoft Kinect systems create 3D images and are low cost, portable, not markers required, and easy to set up. They lack, however, evidence of reliability and validity for human movement kinematics analysis. Twenty-six participants were recruited for this study. Peak Motus version 9 and Microsoft Kinect system with customized skeleton software were used to collect data from each subject sitting on a platform moving horizontally at the...

Figures (5)
Figure 1. Peak Motus and Kinect Systems Set up A Microsoft Kinect camera was also used to capture th 3D human movement. The Microsoft Kinect system wa composed of a depth sensor, an accelerometer and RGI cameras (one VGA and one infrared camera) to produce ; real time 3D image. Proprietary skeleton software designe: for the Kinect camera was used to capture kinematic: movement of human joints. Both the Peak Motus an Microsoft Kinect systems were connected to separat computers and synchronized via a LED sensor to start botl systems simultaneously as depicted in Figure 1.
Figure 1. Peak Motus and Kinect Systems Set up A Microsoft Kinect camera was also used to capture th 3D human movement. The Microsoft Kinect system wa composed of a depth sensor, an accelerometer and RGI cameras (one VGA and one infrared camera) to produce ; real time 3D image. Proprietary skeleton software designe: for the Kinect camera was used to capture kinematic: movement of human joints. Both the Peak Motus an Microsoft Kinect systems were connected to separat computers and synchronized via a LED sensor to start botl systems simultaneously as depicted in Figure 1.
Figure 3. Calibration Tree for Peak Motus Prior to any testing sessions, the equipment was set up as depicted in Figure 1. The Peak Motus set up involved assembling the calibration tree on a tripod as shown in Figure 3. The set up also entailed the use of two basler cameras, with two external lights mounted on the tripod of each camera and connected to the computer, as depicted in Figure 1.
Figure 3. Calibration Tree for Peak Motus Prior to any testing sessions, the equipment was set up as depicted in Figure 1. The Peak Motus set up involved assembling the calibration tree on a tripod as shown in Figure 3. The set up also entailed the use of two basler cameras, with two external lights mounted on the tripod of each camera and connected to the computer, as depicted in Figure 1.
Figure 2. Sitting Moveable platform Microsoft Kinect system, the LED light was activated. The light was activated again, when the left mouse button was used to stop the recording. This approach allowed the frames captured on the Peak Motus system to be cropped to the same frames the Microsoft Kinect system captured.
Figure 2. Sitting Moveable platform Microsoft Kinect system, the LED light was activated. The light was activated again, when the left mouse button was used to stop the recording. This approach allowed the frames captured on the Peak Motus system to be cropped to the same frames the Microsoft Kinect system captured.
The ICC values computed to provide concurrent related evidence of validity when comparing the Microsoft Kinect displacement measures to the traditional Peak Motus system were different for trials 1 and 2. As depicted in Figure 5, trial 1 had a higher concurrent validity mean value (M=0.461, SD= 0.264) than trial 2 (M=0.33, SD=0.211). There was also more variability for the correlation measures of trial | when compared to trial 2. However, the left and right knee joints provided the strongest evidence of concurrent validity measures for both trials; all four of these values were above r=0.7, p<0.05, n=26. Figure 5. Concurrent validity of displacement measures between the traditional and Microsoft Kinect system
The ICC values computed to provide concurrent related evidence of validity when comparing the Microsoft Kinect displacement measures to the traditional Peak Motus system were different for trials 1 and 2. As depicted in Figure 5, trial 1 had a higher concurrent validity mean value (M=0.461, SD= 0.264) than trial 2 (M=0.33, SD=0.211). There was also more variability for the correlation measures of trial | when compared to trial 2. However, the left and right knee joints provided the strongest evidence of concurrent validity measures for both trials; all four of these values were above r=0.7, p<0.05, n=26. Figure 5. Concurrent validity of displacement measures between the traditional and Microsoft Kinect system
the fifteen body joints, however, had a reliability value below r=0.4, p<0.05, n=26. Figure 4. Reliability of displacement measures from the traditional and Microsoft Kinect system
the fifteen body joints, however, had a reliability value below r=0.4, p<0.05, n=26. Figure 4. Reliability of displacement measures from the traditional and Microsoft Kinect system

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