Real Time Cognitive Load Measurement: Data Streaming Approach
In this chapter, we discuss how the efficacy of intelligent user interfaces would be greatly enhanced if a user's cognitive load could be sensed in real time and adjustments made accordingly. Monitoring different data streams derived from the user can individually (or collectively) be processed to detect sudden shifts or gradual drifts in behavior. We present a reformulation of the problem of cognitive load change detection as the problem of concept shift/drift detection in data streams. The chapter extends the multimodal behavioral model to include mouse interactivity streams and discusses a modified sliding window implementation. In detailing an experiment utilising several variations of this model, the technical feasibility of learning from streams sheds light on the challenges presented by real time cognitive load measurement.