Deep Learning-Based Worker Posture Classification for Ergonomic Risk Evaluation in Manufacturing - Rahmadwati Rahmadwati; Farrel Rafif Ferdian; Yeni Sumantri; Dhaffin Rayhzan Ferdian (Malikussaleh University, Aceh, Indonesia, 2026)
Continuous ergonomic monitoring in manufacturing remains challenging because conventional posture assessment methods rely on manual observation, making evaluations time-consuming, subjective, and unsuitable for continuous industrial applications. This study proposes an automated ergonomic risk assessment framework that integrates Media Pipe Pose with a Convolutional Neural Network (CNN) to classify worker postures into low-, medium-, and high-risk ergonomic categories. The framework extracts 33 anatomical body landmarks from RGB images and video frames to generate marker less posture representations for deep learning-based classification. A dataset consisting of 4,500 posture samples collected from assembly, packaging, and welding workstations was expanded to 12,000 samples through data augmentation techniques, including rotation, scaling, horizontal flipping, and brightness adjustment, to improve model robustness and generalization. The CNN model was trained and evaluated using an independent test dataset, achieving an overall classification accuracy of 94.2%, with precision, recall, and F1-score consistently exceeding 94% across all ergonomic risk categories. Comparative evaluation against a conventional REBA/RULA-based rule-driven assessment demonstrated that the proposed framework improved classification accuracy by 7.5 percentage points while eliminating the need for manual posture scoring and reducing observer subjectivity. Furthermore, computational performance analysis showed that the complete inference pipeline operated at an average of 14 ms per frame (approximately 28.5 FPS) on a standard Intel Core i7 CPU with 16 GB RAM, without requiring GPU acceleration, indicating its suitability for real-time deployment in manufacturing environments. The proposed Media Pipe–CNN framework provides an efficient, accurate, and marker less solution for automated ergonomic risk assessment, supporting intelligent occupational safety management, continuous workplace monitoring, and the implementation of smart manufacturing systems aligned with Industry 4.0 initiatives