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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Jurnal Pendidikan dan Pemberdayaan Masyarakat JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal METIK JURNAL JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer
Hamdani Hamdani
Universitas Mulawarman
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Automated ergonomic sitting postures detection for office workstation using XGBoost method Theresia Amelia Pawitra; Farida Djumiati Sitania; Anindita Septiarini; Hamdani Hamdani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp506-514

Abstract

Sedentary office work increases musculoskeletal risk, underscoring the need for non-intrusive, real-time posture monitoring. This study presents a computer vision approach that classifies ergonomic versus non-ergonomic sitting postures using upper body key points extracted by MoveNet thunder. Images from 30 participants were captured from frontal and side views, and labeled according to SNI 9011:2021 criteria. Seventeen key points were detected, with head-to-hip landmarks retained, then normalized and centered. Three classifiers—adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and a multi-layer perceptron (MLP)—were trained and evaluated with 10-fold stratified cross-validation. XGBoost achieved the best performance, with accuracy 93.0%±1.9%, precision 94.6%, recall 91.4%, F1-score 92.9%, and area under the receiver operating characteristic curve (ROC-AUC) 0.974±0.010, outperforming MLP and AdaBoost. The method supports privacy-preserving, on-device inference and is suitable for integration into smart office systems to reduce exposure to high-risk postures. Limitations include controlled capture conditions and an upper body focus; future work will expand posture taxonomy and real-world deployment.
Co-Authors A. Ismail Lukman Agus Qomaruddin Munir Albert Richart Singal Amin Padmo Azam Masa Anam, M Khairul Andri Syafrianto Andrianus Layuk Anindita Septiariani Anindita Septiarini, Anindita Anindita Septirini Awang Harsa Kridalaksana Budi Rahmani Budi Rahmani Citra Tri Angga Dana Pranata Decky Aspandi Latif Didit Suprihanto, Didit Dyna Marisa Khairina Edy Winarno Farida Djumiati Sitania Faza Alameka Hatta, Heliza Rahmania Haviluddin Haviluddin Heliza Hatta Henderi . Hepy Tri Winarti Heru Ismanto Hugo Aprilianto Irwanda Syahputra Junadhi Junadhi Khabib Mustofa Lucky Lhaura Van FC, Lucky Lhaura Muhammad Alisalman Muhammad Bambang Firdaus Muhammad Olly Muhammad Sofian Sauri Muhammad Syarif Abdillah Muhammad Zaini Mustangin Mustangin Ngurah Satria Darmawangsa Nur Madia Puspitasari, Novianti Rahmaddeni Rahmaddeni Retantyo Wardoyo Roni Stepanus Ginting Sayekti Harits Suryawan Sumaini Sumaini Syaffira Rizky Amalia Theresia Amelia Pawitra Wati, Masna Widians, Joan Angelina Wiwien Hadikurniawati Yuda Irawan Yulianto Yulianto Zainal Arifin