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Evaluating the accuracy and feasibility of a commercial AI-powered ergonomic assessment system for automotive assembly work.

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Τίτλος: Evaluating the accuracy and feasibility of a commercial AI-powered ergonomic assessment system for automotive assembly work.
Συγγραφείς: Zahabi SJN; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA., Kim S; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA., Nussbaum MA; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA., Porto R; Advanced Ergonomics, General Motors, Detroit, MI, 28243, USA., Lim S; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA. Electronic address: sol@vt.edu.
Πηγή: Applied ergonomics [Appl Ergon] 2026 Oct; Vol. 136, pp. 104779. Date of Electronic Publication: 2026 Apr 03.
Τύπος έκδοσης: Journal Article; Evaluation Study
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Butterworth-Heinemann Country of Publication: England NLM ID: 0261412 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-9126 (Electronic) Linking ISSN: 00036870 NLM ISO Abbreviation: Appl Ergon Subsets: MEDLINE
Imprint Name(s): Publication: Oxford : Butterworth-Heinemann
Original Publication: London.
Ιατρικοί όροι (MeSH): Ergonomics*/methods , Artificial Intelligence* , Automobiles*, Risk Assessment/methods ; Musculoskeletal Diseases/prevention & control ; Occupational Diseases/prevention & control ; Humans ; Motion Capture ; Feasibility Studies ; Reproducibility of Results ; Intelligent Systems ; Male ; Adult ; Female ; Software ; Task Performance and Analysis
Περίληψη: Accurate ergonomic risk assessment is essential to prevent work-related musculoskeletal disorders. Artificial intelligence (AI) and computer vision offer new opportunities for automatic risk assessment, but accuracy and usability remain uncertain. We evaluated a commercial, AI-powered, ergonomic assessment system through two studies: In an exploratory evaluation (Study 1; n = 10), we compared risk estimates and joint angles using three camera placements vs. a gold-standard 3D motion capture system. In Study 2 (n = 10), we interviewed ergonomics professionals who used the software system for automotive assembly tasks. We found substantial inaccuracies in joint angles (especially left elbow and bilateral wrists) and risk estimates, and important variability across camera setups. Although the software system was reported as easy to use and visually intuitive, concerns were noted about reliability-particularly in cluttered environments and tasks involving fine hand motions. While promising, these systems may require further refinements to improve accuracy and adaptability.
(Copyright © 2026. Published by Elsevier Ltd.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: AI-Based ergonomics; Automated risk evaluation; Computer vision; Software usability
Entry Date(s): Date Created: 20260404 Date Completed: 20260714 Latest Revision: 20260714
Update Code: 20260714
DOI: 10.1016/j.apergo.2026.104779
PMID: 41934762
Βάση Δεδομένων: MEDLINE
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  Data: <searchLink fieldCode="AU" term="%22Zahabi+SJN%22">Zahabi SJN</searchLink>; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.<br /><searchLink fieldCode="AU" term="%22Kim+S%22">Kim S</searchLink>; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.<br /><searchLink fieldCode="AU" term="%22Nussbaum+MA%22">Nussbaum MA</searchLink>; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.<br /><searchLink fieldCode="AU" term="%22Porto+R%22">Porto R</searchLink>; Advanced Ergonomics, General Motors, Detroit, MI, 28243, USA.<br /><searchLink fieldCode="AU" term="%22Lim+S%22">Lim S</searchLink>; Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA. Electronic address: sol@vt.edu.
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  Data: Accurate ergonomic risk assessment is essential to prevent work-related musculoskeletal disorders. Artificial intelligence (AI) and computer vision offer new opportunities for automatic risk assessment, but accuracy and usability remain uncertain. We evaluated a commercial, AI-powered, ergonomic assessment system through two studies: In an exploratory evaluation (Study 1; n = 10), we compared risk estimates and joint angles using three camera placements vs. a gold-standard 3D motion capture system. In Study 2 (n = 10), we interviewed ergonomics professionals who used the software system for automotive assembly tasks. We found substantial inaccuracies in joint angles (especially left elbow and bilateral wrists) and risk estimates, and important variability across camera setups. Although the software system was reported as easy to use and visually intuitive, concerns were noted about reliability-particularly in cluttered environments and tasks involving fine hand motions. While promising, these systems may require further refinements to improve accuracy and adaptability.<br /> (Copyright © 2026. Published by Elsevier Ltd.)
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  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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