Haozhi Chen , Peiran Liu , Guoyang Zhou , Ming-Lun Lu , Denny Yu
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引用次数: 0
Abstract
Work-related injuries from overexertion, particularly lifting, are a major concern in occupational safety. Traditional assessment tools, such as the Revised NIOSH Lifting Equation (RNLE), require significant training and practice for deployment. This study presents an approach that integrates tactile gloves with computer vision (CV) to enhance the assessment of lifting-related injury risks, addressing the limitations of existing single-modality methods. Thirty-one participants performed 2747 lifting tasks across three lifting risk categories (LI < 1, 1 ≤ LI ≤ 2, LI > 2). Features including hand pressure measured by tactile gloves during each lift and 3D body poses estimated using CV algorithms from video recordings were combined and used to develop prediction models. The Convolutional Neural Network (CNN) model achieved an overall accuracy of 89 % in predicting the three lifting risk categories. The results highlight the potential for a real-time, non-intrusive risk assessment tool to assist ergonomic practitioners in mitigating musculoskeletal injury risks in workplace environments.
期刊介绍:
Applied Ergonomics is aimed at ergonomists and all those interested in applying ergonomics/human factors in the design, planning and management of technical and social systems at work or leisure. Readership is truly international with subscribers in over 50 countries. Professionals for whom Applied Ergonomics is of interest include: ergonomists, designers, industrial engineers, health and safety specialists, systems engineers, design engineers, organizational psychologists, occupational health specialists and human-computer interaction specialists.