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Investigating the Impact of a Motion Capture System on Microsoft Kinect V2 Recordings: A Caution for Using the Technologies Together

Overview
Journal PLoS One
Date 2018 Sep 15
PMID 30216382
Citations 8
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Abstract

Microsoft Kinect sensors are considered to be low-cost popular RGB-D sensors and are widely employed in various applications. Consequently, several studies have been conducted to evaluate the reliability and validity of Microsoft Kinect sensors, and noise models have been proposed for the sensors. Several studies utilized motion capture systems as a golden standard to assess the Microsoft Kinect sensors, and none of them reported interference between Kinect sensors and motion capture systems. This study aimed to investigate possible interference between a golden standard (i.e., Qualisys) and Microsoft Kinect v2. The depth recordings of Microsoft Kinect sensors were processed to estimate the intensity of interference. A flat non-reflective surface was utilized, and smoothness of the surface was measured using Microsoft Kinect v2 in absence and presence of an active motion capture system. The recording was repeated in five different distances. The results indicated that Microsoft Kinect v2 is distorted by the motion capture system and the distortion is increasing by increasing distance between Kinect and region of interest. Regarding the results, it can be concluded that the golden standard motion capture system is robust against interference from the Microsoft Kinect sensors.

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References
1.
Auvinet E, Multon F, Manning V, Meunier J, Cobb J . Validity and sensitivity of the longitudinal asymmetry index to detect gait asymmetry using Microsoft Kinect data. Gait Posture. 2016; 51:162-168. DOI: 10.1016/j.gaitpost.2016.08.022. View

2.
Geerse D, Coolen B, Roerdink M . Kinematic Validation of a Multi-Kinect v2 Instrumented 10-Meter Walkway for Quantitative Gait Assessments. PLoS One. 2015; 10(10):e0139913. PMC: 4603795. DOI: 10.1371/journal.pone.0139913. View

3.
Azzari G, Goulden M, Rusu R . Rapid characterization of vegetation structure with a Microsoft Kinect sensor. Sensors (Basel). 2013; 13(2):2384-98. PMC: 3649362. DOI: 10.3390/s130202384. View

4.
Xu X, McGorry R, Chou L, Lin J, Chang C . Accuracy of the Microsoft Kinect for measuring gait parameters during treadmill walking. Gait Posture. 2015; 42(2):145-51. DOI: 10.1016/j.gaitpost.2015.05.002. View

5.
Eltoukhy M, Oh J, Kuenze C, Signorile J . Improved kinect-based spatiotemporal and kinematic treadmill gait assessment. Gait Posture. 2016; 51:77-83. DOI: 10.1016/j.gaitpost.2016.10.001. View