Authors - Sara OULED LAGHZAL, Abdelmajid El Ouadi Abstract - Musculoskeletal disorders (MSDs) are a significant occupational health problem in the automotive industry [1].Manual and semiautomated assembly work often exposes workers to repetitive movements and non-neutral wrist positions. Conventional ergonomic assessments are often ad hoc and subjective, limiting their ability to capture positional variations and cumulative strain over time. This article proposes a framework for continuous improvement using artificial intelligence that combines a convolutional neural network-based classification of wrist position (CNN) and a rapid upper limb assessment (RULA)[2] in real time. The convolutional neural network distinguishes between acceptable and unacceptable wrist postures during task execution, and the RULA layer translates the posture data into standardised biomechanical risk indicators. Empirical tests in an industrial context have shown that the CNNRULA hybrid system reliably detects even subtle deviations in wrist position that are difficult to detect by visual observation. This enables comfortable, data-driven proactive interventions in an Industry 4.0 environment.
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