Cognitive load (CL) refers to the amount of mental demand imposed on a user by a particular task, and has been associated with the limited capacity of working memory and the ability to process novel information.

Multimodal Cognitive Load Measurement project aims at investigating approaches for measuring an individual’s CL levels in real-time unobtrusively and automatically. Multiple measures, including physiological (e.g. skin conductance, pupil dilation and EEG) and behavioural patterns (e.g. speech/language, eye-movement, mouse movement, and pen-gesture/handwriting) have been being investigated in different task contexts and scenarios, to dynamically assess the level of load a user experiences while performing a task. Factors which affect cognitive load measurement (CLM) such as stress, trust, and environmental factors such as illumination are also extensively studied in this project. Furthermore, dynamic workload adjustment and real-time CLM with data streaming are evaluated to make CLM accessible by more widespread applications and users.

Selected Publications

  1. Fang Chen, Jianlong Zhou, Yang Wang, Kun Yu, Syed Z. Arshad, Ahmad Khawaji, and Dan Conway. Robust Multimodal Cognitive Load Measurement. Springer, 2016, 254pp. ISBN: 978-3-319-31698-7.
  2. Jianlong Zhou, Ju Young Jung, and Fang Chen. Dynamic Workload Adjustments in Human-Machine Systems Based on GSR Features. J. Abascal et al. Eds., Human-Computer Interaction - INTERACT 2015, Part I, LNCS 9296, pp. 550–558, 2015. (PDF)