Literature DB >> 34288917

Simple benchmarking method for determining the accuracy of depth cameras in body landmark location estimation: Static upright posture as a measurement example.

Pin-Ling Liu1, Chien-Chi Chang1, Jia-Hua Lin2, Yoshiyuki Kobayashi3.   

Abstract

To evaluate the postures in ergonomics applications, studies have proposed the use of low-cost, marker-less, and portable depth camera-based motion tracking systems (DCMTSs) as a potential alternative to conventional marker-based motion tracking systems (MMTSs). However, a simple but systematic method for examining the estimation errors of various DCMTSs is lacking. This paper proposes a benchmarking method for assessing the estimation accuracy of depth cameras for full-body landmark location estimation. A novel alignment board was fabricated to align the coordinate systems of the DCMTSs and MMTSs. The data from an MMTS were used as a reference to quantify the error of using a DCMTS to identify target locations in a 3-D space. To demonstrate the proposed method, the full-body landmark location tracking errors were evaluated for a static upright posture using two different DCMTSs. For each landmark, we compared each DCMTS (Kinect system and RealSense system) with an MMTS by calculating the Euclidean distances between symmetrical landmarks. The evaluation trials were performed twice. The agreement between the tracking errors of the two evaluation trials was assessed using intraclass correlation coefficient (ICC). The results indicate that the proposed method can effectively assess the tracking performance of DCMTSs. The average errors (standard deviation) for the Kinect system and RealSense system were 2.80 (1.03) cm and 5.14 (1.49) cm, respectively. The highest average error values were observed in the depth orientation for both DCMTSs. The proposed method achieved high reliability with ICCs of 0.97 and 0.92 for the Kinect system and RealSense system, respectively.

Entities:  

Year:  2021        PMID: 34288917     DOI: 10.1371/journal.pone.0254814

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  28 in total

1.  Evaluation of the Kinect™ sensor for 3-D kinematic measurement in the workplace.

Authors:  Tilak Dutta
Journal:  Appl Ergon       Date:  2011-10-20       Impact factor: 3.661

2.  Rear-foot, mid-foot and fore-foot motion during the stance phase of gait.

Authors:  A Leardini; M G Benedetti; L Berti; D Bettinelli; R Nativo; S Giannini
Journal:  Gait Posture       Date:  2006-09-11       Impact factor: 2.840

3.  Accurate estimation of human body orientation from RGB-D sensors.

Authors:  Wu Liu; Yongdong Zhang; Sheng Tang; Jinhui Tang; Richang Hong; Jintao Li
Journal:  IEEE Trans Cybern       Date:  2013-07-23       Impact factor: 11.448

4.  Accuracy of the Microsoft Kinect for measuring gait parameters during treadmill walking.

Authors:  Xu Xu; Raymond W McGorry; Li-Shan Chou; Jia-Hua Lin; Chien-Chi Chang
Journal:  Gait Posture       Date:  2015-05-14       Impact factor: 2.840

5.  RGB-D ergonomic assessment system of adopted working postures.

Authors:  Ahmed Abobakr; Darius Nahavandi; Mohammed Hossny; Julie Iskander; Mohammed Attia; Saeid Nahavandi; Marty Smets
Journal:  Appl Ergon       Date:  2019-05-25       Impact factor: 3.661

6.  Comparison of depth cameras for three-dimensional reconstruction in medicine.

Authors:  Chuang-Yuan Chiu; Michael Thelwell; Terry Senior; Simon Choppin; John Hart; Jon Wheat
Journal:  Proc Inst Mech Eng H       Date:  2019-06-28       Impact factor: 1.617

7.  Improved kinect-based spatiotemporal and kinematic treadmill gait assessment.

Authors:  Moataz Eltoukhy; Jeonghoon Oh; Christopher Kuenze; Joseph Signorile
Journal:  Gait Posture       Date:  2016-10-04       Impact factor: 2.840

8.  Validity of the Microsoft Kinect in assessing spatiotemporal and lower extremity kinematics during stair ascent and descent in healthy young individuals.

Authors:  Jeonghoon Oh; Christopher Kuenze; Marco Jacopetti; Joseph F Signorile; Moataz Eltoukhy
Journal:  Med Eng Phys       Date:  2018-08-08       Impact factor: 2.242

9.  Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis.

Authors:  Alexandra Pfister; Alexandre M West; Shaw Bronner; Jack Adam Noah
Journal:  J Med Eng Technol       Date:  2014-05-30

10.  Accuracy and repeatability of joint angles measured using a single camera markerless motion capture system.

Authors:  Anne Schmitz; Mao Ye; Robert Shapiro; Ruigang Yang; Brian Noehren
Journal:  J Biomech       Date:  2013-11-25       Impact factor: 2.712

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