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Robust Multimodal Cognitive Load Measurement

Part of the Human–Computer Interaction Series Book Series

DOI: 10.1007/978-3-319-31700-7

ISBN: 978-3-319-31698-7

Publisher: Springer International Publishing - Springer Journals

Jun 15, 2016

1 - 10 of 17 Chapters

Available

Introduction

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

Pages: 3 –12

[Mental workload is an increasingly important determinant of the performance of human-machine systems. As the power, complexity and ubiquity of both everyday and specialised computing solutions increase, the limiting factor of such systems performance is shifting from the ma-chine to the human....
Available

The State-of-The-Art

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

Pages: 13 –32

[Chapter 2 provides an overview of the latest developments in this body of work. Firstly, different approaches to the problem of measuring cognitive load are identified. The related work of each measurement approach is then discussed in more detail. These approaches include subjective...
Available

Theoretical Aspects of Multimodal Cognitive Load Measures

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

Pages: 33 –71

[In Chap. 3, the theoretical aspects that result in the framework for Multimodal Cognitive Load Measures (MCLM) are discussed. This chapter brings together the critical elements of mental effort and performance studies from the domain of human factors and ergonomics. These elements are then...
Available

Eye-Based Measures

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

Pages: 75 –85

[This chapter investigates the measurement of cognitive load through eye tracking under the influence of varying luminance conditions. We demonstrate how reliable measurements can be achieved via this non-intrusive approach. We also discuss the characteristics of pupillary response and their...
Available

Galvanic Skin Response-Based Measures

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

Pages: 87 –99

[This chapter focuses on the use of Galvanic Skin Response (GSR) for cognitive load measurement. GSR is a measure of conductivity of human skin, and provides an indication of changes within the human sympathetic nervous system.]
Available

Linguistic Feature-Based Measures

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

Pages: 103 –114

[This chapter discusses methods for cognitive load examination via language and shows that many features, some of which were originally designed to examine language complexity for learning analytics or comprehension, can be successfully applied to the cognitive load research. The linguistic...
Available

Speech Signal Based Measures

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

Pages: 115 –131

[This chapter reviews cognitive load measurement methods via speech with typical experiments to induce different cognitive load levels via speech being introduced. Various speech features are then investigated, and a comparison of cognitive load level classification methods is conducted.]
Available

Pen Input Based Measures

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

Pages: 133 –145

[This chapter introduces methods to examine cognitive load via writing and pen-based features, including writing velocity, pen pressure, writing gestures and additional features derived from these signals. Based on the examination of different types of script, including text, digits and sketches,...
Available

Mouse Based Measures

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

Pages: 147 –157

[This chapter demonstrates the applicability of mouse interactivity based features as indicators of a user’s cognitive load. The chapter begins by introducing some basics of user mouse interaction. Then the temporal and spatial mouse features that are found to be viable indicators of cognitive...
Available

Multimodal Measures and Data Fusion

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

Pages: 161 –171

[This chapter presents a model for multimodal cognitive load. The features extracted from speech, pen input and GSR in a user study are fused using the AdaBoost boosting algorithm to demonstrate the methods advantages.]

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