Due to high ergonomic risks, forestry considered a high-priority issue in forest workers' health. Timber production workers usually work in open environments, where harsh conditions such as rough terrain and extreme weather. Lack of experience, improper equipment and other related factors can negatively impact occupational health and safety (OHS). In this study, work posture analysis was performed by using computer vision technique. Real-time joint angles were calculated using a MediaPipe-supported system based on machine learning (ML) technique. Real-time joint angle data extracted from video frames were used to compute Rapid Entire Body Assessment (REBA) scores instantly. Besides the entire body analysis, each limb could be assessed separately. The initial user impressions regarding the system were assessed. This method enables the collection of detailed data, which can regularly accumulate into a largescale dataset appropriate for big data applications. CV-based work posture analysis is promising technique as a method open to development as an adaptive system. The risk of biased evaluations in expert observations during body posture analysis can be minimized.
