TY - JOUR
T1 - Automated postural ergonomic risk assessment using vision-based posture classification
AU - Seo, Joon Oh
AU - Lee, Sang Hyun
N1 - Funding Information:
This study was supported by an Early Career Scheme ( PolyU 25210917 ) from Research Grants Council, Hong Kong , a grant ( 21CTAP-C151784-03 ) from Technology Advancement Research Program funded by Ministry of Land, Infrastructure and Transport of the South Korea Government , and National Science Foundation Award (No. CMMI-1161123 ), the United States.
Publisher Copyright:
© 2021
PY - 2021/8
Y1 - 2021/8
N2 - Construction workers are at high risk of work-related musculoskeletal disorders (WMSDs) due to physically demanding manual-handling tasks in awkward postures. Although existing observational methods to identify ergonomic risks are inexpensive and easy to use, they are seldom used in construction sites because they are time-consuming, subject to observer bias, and require well-trained analysts. To address these drawbacks, this paper proposes a vision-based method to automatically classify workers' postures for ergonomic assessment. Specifically, it proposes a vision-based method that eliminates the need to collect extensive training-image datasets by employing classification algorithms to learn diverse postures from virtual images, and then identifies those postures in real-world images. The experimental tests showed about 89% classification accuracy in automatically classifying diverse postures on images, confirming the usefulness of virtual training images for posture classification. The proposed method has potential for automated ergonomic risk analysis, and could help to prevent WMSDs during diverse occupational tasks.
AB - Construction workers are at high risk of work-related musculoskeletal disorders (WMSDs) due to physically demanding manual-handling tasks in awkward postures. Although existing observational methods to identify ergonomic risks are inexpensive and easy to use, they are seldom used in construction sites because they are time-consuming, subject to observer bias, and require well-trained analysts. To address these drawbacks, this paper proposes a vision-based method to automatically classify workers' postures for ergonomic assessment. Specifically, it proposes a vision-based method that eliminates the need to collect extensive training-image datasets by employing classification algorithms to learn diverse postures from virtual images, and then identifies those postures in real-world images. The experimental tests showed about 89% classification accuracy in automatically classifying diverse postures on images, confirming the usefulness of virtual training images for posture classification. The proposed method has potential for automated ergonomic risk analysis, and could help to prevent WMSDs during diverse occupational tasks.
KW - Ergonomic risk assessment
KW - Vision-based posture classification
KW - Work-related musculoskeletal disorders
UR - http://www.scopus.com/inward/record.url?scp=85107141726&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2021.103725
DO - 10.1016/j.autcon.2021.103725
M3 - Journal article
AN - SCOPUS:85107141726
VL - 128
JO - Automation in Construction
JF - Automation in Construction
SN - 0926-5805
M1 - 103725
ER -