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All Journal BANGUN REKAPRIMA JPM : Jurnal Pendidikan Matematika International Journal of Progressive Mathematics Education Jurnal Lintas Keperawatan Al-'Adad: Jurnal Tadris Matematika Jurnal Riset Rumpun Ilmu Pendidikan (JURRIPEN) Jurnal Media Akademik (JMA) Journal of Mechanical Engineering and Applied Technology
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Journal : Journal of Mechanical Engineering and Applied Technology

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ERGONOMIC POSTURE CLASSIFICATION OF BENCH WORK UTILIZING MUSCLE DATA: A CASE STUDY IN EDUCATIONAL WORKSHOP Putri, Farika Tono; Purwati, Wiwik; Margana; Supriyo; Prawibowo, Hartanto; Pasmanasari, Elta Diah; Ismail, Rifky; Kadavi, Fadhil Muhammad; Muryanto
Journal of Mechanical Engineering and Applied Technology Vol. 3 No. 2 (2025): VOLUME 3 ISSUE 2 YEAR 2025 (JULY 2025)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jmeat.v3i2.6674

Abstract

Occupational musculoskeletal disorders (MSDs) often result from prolonged non-ergonomic postures, especially in educational and industrial bench work activities. This study presents an approach to classify ergonomic and non-ergonomic working postures using surface electromyography (sEMG) signals and machine learning. sEMG data were recorded from four upper limb muscles during simulated bench work conditions. Time-domain and frequency-domain features were extracted from segmented EMG signals using sliding windows. Dimensionality reduction was performed using Principal Component Analysis (PCA), and classification was carried out using logistic regression. The proposed system achieved an overall classification accuracy of 75% in distinguishing ergonomic and non-ergonomic postures. Visualization using PCA and Linear Discriminant Analysis (LDA) showed clear class separation, validating the discriminatory power of the extracted features. While the small sample size and class imbalance were identified as limitations, the study demonstrates that a simple and interpretable model like Logistic Regression, when combined with proper feature engineering, can yield promising results.This work contributes to the development of low-cost, efficient, and interpretable ergonomic assessment tools. It is particularly relevant for vocational and educational environments where real-time posture monitoring and early prevention of MSDs are essential. Future research should focus on expanding the dataset, exploring deep learning methods, and implementing real-time wearable systems.
Co-Authors Aidatul Istifaiyah Amaliyah Mukmilah Andika Setyo Budi Lestari Ani Afifah Ani Afifah, Ani Anisa Putri Ariyanto, Arief Subakti Asa Mardliyatim Ni’mah Ashfiak, Roudotul Aufin, Muhammad Budi Lestari, Andika Setyo Dianita Ratna Kusumastuti Fuat Goro, Garup Lambang HARTATI Intan Meilina Aisyah Ismi Zainah Nabilah Kadavi, Fadhil Muhammad Lestari, Andika Setyo Budi Lestari M. Aufin Mardi Hartono Margana Margana MARSUDI Maya Rayungsari Miftahul Khoiri Muryanto Nur Setiaji Pamungkas, Nur Setiaji Pasmanasari, Elta Diah Penyami, Yuniske Praharseno, Fikri Prawibowo, Hartanto Purwati, Wiwik Putri, Farika Tono Rabinah, Aiun Hayatu Rifky Ismail Rifqi Aulia Abdillah, Rifqi Aulia S.T., M.T., DEDI BUDI SETIAWAN Siti Aisah Stefanus Santosa Sulaiman, Sriwahyuningsih Umi Chabibah Yuli Indrianis Zidni Ilma