Multimodal Sensing for Cognitive Workload and Affective State Monitoring in Human–Machine Interaction

Cognitive workload and affective states can substantially influence how people interact with automated and intelligent systems, affecting performance, decision-making, trust and safety. This project will investigate these responses during controlled human–machine interaction tasks using multimodal sensing technologies to capture physiological, behavioural and task-performance data.

Artificial intelligence and statistical methods will be used to identify patterns associated with different levels of cognitive workload and affective states. The project aims to develop and validate an objective monitoring framework that could inform the design of adaptive, human-centred systems capable of responding to users’ cognitive and emotional needs.