Evaluating the work posture of motor-manual debarking worker in an open environment: a preliminary use of a real-time computer vision technique for timber debarking

OpenAlex İngilizce

Workers involved in timber production are in a high-risk job-safety category due to the variety of ergonomic risk factors they face in forestlands. Research on implementing ergonomics and encouraging a suitable work posture, which ensure long-term musculoskeletal health and safety for workers, is important for reducing risk. In this study, the timber debarking stages and possible muscle strains from the work posture of the worker were analyzed and assessed using the Rapid Entire Body Assessment (REBA). Real-time analyses of worker postures were conducted using computer vision (CV) technology. The angles of worker body joints and severity scores were calculated in real-time using MediaPipe, in alignment with the REBA procedures. Field studies were conducted within the border of the Kahramanmaras Forest Regional Directorate in Turkey. Within the scope of the study, the MediaPipe architecture, which is based on machine learning technology, was implemented to monitor the work pose of the worker debarking in open environment conditions. As a result of this study, we identified repetitive poor posture of the worker in trunk, knee, and arms. If the necessary precautions are not taken, the worker can experience possible musculoskeletal disorders due to working with incorrect work posture in the low back and knees. The method can provide researchers with detailed data for generating big data. Urgent measures are necessary due to the high risk associated with an operator’s posture during the debarking stage. Precautions should also be taken during the medium-risk log, turning stage.