Title

Cognitive State Estimation For Adaptive Learning Systems Using Wearable Physiological Sensors

Keywords

Augmented cognition; Intelligent tutoring; Psychophysiological metrics; Signal processing; Wearable sensors

Abstract

This paper presents a historical overview of intelligent tutoring systems and describes an adaptive instructional architecture based upon current instructional and adaptive design theories. The goal of such an endeavor is to create a training system that can dynamically change training content and presentation based upon an individual's real-time measure of cognitive state changes. An array of physiological sensors is used to estimate the cognitive state of the learner. This estimate then drives the adaptive mitigation strategy, which is used as a feed-back and changes how the learning information is presented. The underlying assumptions are that real-time monitoring of the learners cognitive state and the subsequent adaptation of the system will maintain the learner in an overall state of optimal learning. The main issues concerning this approach are constructing cognitive state estimators from a multimodal array of physiological sensors and assessing initial baseline values, as well as changes in baseline. We discuss these issues in a data processing block wise structure, where the blocks include synchronization of different data streams, feature extraction, and forming a cognitive state metric by classification/clustering of the features. Initial results show our current capabilities of combining several data streams and determining baseline values. Given that this work is in its initial staged the work points to our ongoing research and future directions.

Publication Date

11-13-2008

Publication Title

BIOSIGNALS 2008 - Proceedings of the 1st International Conference on Bio-inspired Systems and Signal Processing

Volume

2

Number of Pages

147-152

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

55649088222 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/55649088222

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