Human Workflow Digital Twin: Fatigue and Motion Analyses

The human worker is simultaneously the most complex and capable component of a production system and presents unique challenges within an Industry 4.0 framework. Worker fatigue is estimated to cost U.S. employers $136 billion annually due to health-related loss of productivity. There is a need to quantitatively and unobtrusively measure operator fatigue. This can be accomplished with a scalable, human- centered framework for a digital twin: a virtual model of a product or process that takes its inputs directly from the environment, so it is highly accurate.

The team will develop and demonstrate a novel and scalable architecture for measuring worker fatigue and ergonomics using combined data from a camera system and flexible, wearable sensors. Based on the combined camera and sensor inputs and backend data analytics, expert suggestions of ergonomics and workflow optimization will be provided to the operators as well as the stakeholders interested in safety and performance such as environmental health and safety professionals, safety managers, and upper management. This will help augment operator capabilities and improve their knowledge and performance through real-time feedback on fatigue conditions.

The digital twin of worker performance will be capable of integration to other manufacturing digital twins. It will help to inform micro- and macro- level continuous process improvement in corresponding production systems and help achieve predictive scheduling and optimal productivity. This new framework will promote sustainable and healthy working environments for improvements in safety, ergonomics, operational efficiency, quality, and yields.