Automated postural ergonomic risk assessment using vision-based posture classification

• Published in: Automation in construction Vol. 128; p. 103725
• Main Authors: Seo, JoonOh, Lee, SangHyun
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2021; Elsevier BV
• Subjects: Algorithms; Automation; Classification; Construction industry; Construction sites; Ergonomic risk assessment; Identification methods; Image classification; Risk analysis; Risk assessment; Training; Vision-based posture classification; Work-related musculoskeletal disorders; Work-related musculoskeletal disorders; Ergonomic risk assessment; Vision-based posture classification
• ISSN: 0926-5805; 1872-7891
• DOI: 10.1016/j.autcon.2021.103725

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. •A computer vision-based method for automated postural ergonomic risk assessment is proposed.•The method identifies risky postures on images by learning image patterns according to different body postures.•The method shows robust posture classification accuracy comparable with the accuracy attained by human observers.