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All Journal Journal of Mechanical Engineering and Applied Technology
Farika Tono Putri
S.T., M.T., SCOPUS ID: 57193126339, Politeknik Negeri Semarang
Automotive Engineering Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Markerless Computer-Vision Joint-Angle Analysis for Ergonomic Risk Assessment of Engineering Students During Bench-Work Practicum Rafael Girvan; Hasna Muthia Maghfira; Ferry Anugerah; Muhammad Rizal Cahyo Prayogo; Hartanto Prawibowo; Elta Diah Pasmanasari; Farika Tono Putri; Novie Susanto; Wiwik Purwati; Supriyo; Amrisal Kamal Fajri; Rifky Ismail
Journal of Mechanical Engineering and Applied Technology Vol. 4 No. 2 (2026): VOLUME 4 ISSUE 2 YEAR 2026 (JULY 2026)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jmeat.v4i2.7821

Abstract

Bench work (kerja bangku) is a foundational manual-skills practicum in mechanical and manufacturing engineering education. It requires sustained non-neutral postures—forward trunk flexion, downward neck flexion toward the vice, and repetitive upper-limb exertion—that expose students to work-related musculoskeletal disorder (WMSD) risk early in their careers. Conventional ergonomic evaluation relies on manual observation, which is subjective, labour-intensive, and difficult to scale across large student cohorts. This study presents a markerless computer-vision pipeline that estimates body joint angles from ordinary RGB video and automatically derives Rapid Upper Limb Assessment (RULA) scores for students performing bench-work tasks. Two-dimensional pose estimation localized anatomical landmarks; sagittal joint angles for the neck, trunk, upper arm, lower arm, and wrist were computed from landmark coordinates and mapped to RULA segment scores. Thirty engineering students were recorded performing five representative tasks (filing, hacksawing, marking/scribing, chiselling, and hand-tapping). The mean RULA grand score across tasks was 6.0, with 92% of observations falling in action levels 3–4 (“investigate and change”). Vision-derived joint angles agreed with manual goniometry to within a mean absolute error of 4.8°, and RULA grand scores matched an expert assessor within ±1 point in 96.7% of cases (weighted Cohen’s κ = 0.82). The results show that markerless computer vision offers a low-cost, objective, and scalable instrument for ergonomics education, posture feedback, and bench-station redesign.
Co-Authors Amrisal Kamal Fajri Elta Diah Pasmanasari Ferry Anugerah Hartanto Prawibowo Hasna Muthia Maghfira Muhammad Rizal Cahyo Prayogo Novie Susanto Rafael Girvan Rifky Ismail Supriyo Wiwik Purwati