An Approach For Analysis Of Human Interaction With Worker Assistance Systems Based On Eye Tracking And Motion Capturing

Human behavior in production systems influences productivity, product quality, work safety and overall process performance. To guide human behavior, digital worker assistance systems can be used to support cognitive decision tasks and sensory perception tasks. In doing so, the design of the assistance systems affects user experience and work results. To optimize and develop human-centric productions systems, data on human behavior and interaction with manufacturing equipment must be collected and analyzed. This analysis is expected to yield benefits regarding process monitoring, quality assurance, user experience and ergonomics. In addition, the results could be used for training purposes to monitor skill improvements. This paper presents a framework for data acquisition and analysis of human interaction with digital worker assistance systems. In addition to the overall system architecture, the individual development steps are discussed. An eye tracking device and a motion capturing camera are used for data collection and provide live information about human behavior in conjunction with a digital worker assistance system. The data is stored in a database and analyzed by custom analysis algorithms. The results are displayed in a dashboard application and show that the presented framework with eye tracking and motion capturing is suitable for the analysis of human interaction with worker assistance systems