PMID- 34288917
OWN - NLM
STAT- MEDLINE
DCOM- 20211109
LR  - 20211109
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 16
IP  - 7
DP  - 2021
TI  - Simple benchmarking method for determining the accuracy of depth cameras in body 
      landmark location estimation: Static upright posture as a measurement example.
PG  - e0254814
LID - 10.1371/journal.pone.0254814 [doi]
LID - e0254814
AB  - 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.
FAU - Liu, Pin-Ling
AU  - Liu PL
AUID- ORCID: 0000-0001-8147-3309
AD  - Department of Industrial Engineering and Engineering Management, National Tsing 
      Hua University, Hsinchu, Taiwan.
FAU - Chang, Chien-Chi
AU  - Chang CC
AUID- ORCID: 0000-0002-6476-4232
AD  - Department of Industrial Engineering and Engineering Management, National Tsing 
      Hua University, Hsinchu, Taiwan.
FAU - Lin, Jia-Hua
AU  - Lin JH
AUID- ORCID: 0000-0001-8908-9328
AD  - Washington State Department of Labor and Industries, Olympia, Washington, United 
      States of America.
FAU - Kobayashi, Yoshiyuki
AU  - Kobayashi Y
AD  - Human Augmentation Research Center, National Institute of Advanced Industrial 
      Science and Technology, Tokyo, Japan.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210721
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - *Gait
MH  - Humans
MH  - *Imaging, Three-Dimensional
MH  - *Motion
MH  - *Posture
MH  - *Software
PMC - PMC8294549
COIS- The authors have declared that no competing interests exist.
EDAT- 2021/07/22 06:00
MHDA- 2021/11/10 06:00
PMCR- 2021/07/21
CRDT- 2021/07/21 17:24
PHST- 2021/02/19 00:00 [received]
PHST- 2021/07/04 00:00 [accepted]
PHST- 2021/07/21 17:24 [entrez]
PHST- 2021/07/22 06:00 [pubmed]
PHST- 2021/11/10 06:00 [medline]
PHST- 2021/07/21 00:00 [pmc-release]
AID - PONE-D-21-04220 [pii]
AID - 10.1371/journal.pone.0254814 [doi]
PST - epublish
SO  - PLoS One. 2021 Jul 21;16(7):e0254814. doi: 10.1371/journal.pone.0254814. 
      eCollection 2021.