• Scalable ergonomic model tailored to packaging work. • Multimodal framework integrating four ergonomic dimensions. • Uses wearables, eye tracking, and computer vision to collect real-time operator data directly on the shop floor. • Detects high-risk conditions across operator and contextual work factors. This study introduces a multimodal ergonomic assessment framework that integrates organizational, environmental, physical, and cognitive dimensions into a unified, human-centric system tailored for packaging operations. Addressing the limitations of traditional single-aspect assessments, the framework combines subjective evaluations with objective measurements from wearable sensors and computer vision to enable continuous, real-time monitoring and the identification of critical risk scenarios, providing companies with valuable insights to prioritize and target ergonomic interventions. The framework was validated in the packaging area of a pharmaceutical company in Colombia, involving 43 operators (42 females, 1 male; aged 19–53 years) performing repetitive visual inspection and manual packaging tasks. Data were obtained through questionnaires, smartwatches (recording electrodermal activity, heart rate, and motion data), eye-tracking glasses (to assess visual fatigue), and Microsoft Azure Kinect (for postural motion tracking integrated with the OCRA checklist). These multimodal measurements were complemented by environmental monitoring of lighting, noise, temperature, and humidity at each workstation. Results showed that physical fatigue and biomechanical risks peaked at the end of each shift and during small-lot packaging tasks, while stress levels increased during night shifts and large-batch operations. Both physical and cognitive strain intensified as the workday progressed and among less experienced workers. Visual fatigue was highest under substandard lighting conditions, particularly in large-tray packaging. Aligned with Industry 5.0 principles, this framework integrates human-centered design with data-driven analytics, enabling real-time ergonomic monitoring and informed decision-making in packaging operations, with potential applicability across other manufacturing sectors.
Morillo et al. (Sun,) studied this question.