Towards a Digital Human Representation in an Industrial Digital Twin

Abstract

Digital twins (DTs) have demonstrated their abilities to integrate sensor data, current state information, and the information about the environment in virtual models. While previous approaches have focused on creating DTs for mainly machines and workstations, a small number of studies have considered human performance when designing the DT system, which leads to a deficiency in overall system performance. The absence of the human integrated-DT framework may decelerate human integration in industrial DT, and thus, disregards the crucial role of the human in the industry of the future. This paper presents a framework for digital human representation in an industrial DT to continuously monitor and to analyse the human operational state and behaviour. Thereby, the DT enables decision-makers to allocate tasks on the shop floor taking into account the human physical and mental status. A sample case showed how a human muscle activity monitoring system could be integrated with the DT based on the developed framework to account for the operator’s muscular fatigue or physical exhaustion for decision-making. This included the use of Artificial Intelligence (AI) to interpret the human activity related data using wearable sensors, such as electromyography (EMG). Future research is proposed to harness human data from a richer variety of sensors as control parameters for production operation and improved decision-making.