The task:
The rapid advancement of technology related to Industry 4.0 has brought about a paradigm shift in the way we interact with assets across various domains. This progress has led to the emergence of the concept of a Human Digital Twin (HDT) – a virtual representation of an individual’s cognitive, physiological, psychological, and behavioural characteristics. The HDT has demonstrated potential as a strategic tool for enhancing productivity, safety, and collaboration within the framework of Industry 5.0.
Meanwhile, the growth in the number of automated processes has correspondingly increased the cognitive load on human operators. In response to this challenge, this paper outlines a process for tracking human cognitive load using the galvanic skin response as a physiological marker and proposes a novel method of building a model based on the Human-Asset Administration Shell (HAAS).
The proposed HAAS framework integrates real-time data streams from wearable sensors, user interactions, contextual information, task specifics and environmental condition to deliver a comprehensive understanding of an individual’s cognitive state, physical wellness, and skills set. To validate the effectiveness of the approach, the student will apply it at the Operator 4.0 laboratory.
The thesis must contain:
- Review of literature (Asset Administration Shell, Human Digital Twin)
- Ontology development
- Development of a Python-based framework
- A detailed description of the algorithms and framework and a demonstration of its application