Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Efficient Legendre moment computation for grey level images
Pattern Recognition
International Journal of Human-Computer Studies
Automatic prediction of frustration
International Journal of Human-Computer Studies
Anxiety-based affective communication for implicit human-machine interaction
Advanced Engineering Informatics
Using noninvasive wearable computers to recognize human emotions from physiological signals
EURASIP Journal on Applied Signal Processing
International Journal of Human-Computer Studies
Emotion Recognition Based on Physiological Changes in Music Listening
IEEE Transactions on Pattern Analysis and Machine Intelligence
Short-term emotion assessment in a recall paradigm
International Journal of Human-Computer Studies
Automated stress detection using keystroke and linguistic features: An exploratory study
International Journal of Human-Computer Studies
Non-intrusive physiological monitoring for automated stress detection in human-computer interaction
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Discriminating stress from cognitive load using a wearable EDA device
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
Support Vector Machines to Define and Detect Agitation Transition
IEEE Transactions on Affective Computing
IEEE Transactions on Affective Computing
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment
IEEE Transactions on Fuzzy Systems
Affect prediction from physiological measures via visual stimuli
International Journal of Human-Computer Studies
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
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This paper presents novel subject-dependent biosignal features, with a view towards increasing the effectiveness of automatic psychological stress detection. The features proposed in this work focus on suppressing between-subject variability that typically appears in biosignals like the skin conductance (SC) and the electrocardiogram (ECG), and degrades the performance of relevant emotion recognition (ER) systems. For this purpose, the proposed features employ filtering of input signals, on the basis of ''rest signatures'' calculated from each subject's baseline recordings. These signatures are biosignal transformations capable to express each individual's baseline deviation from signal templates, which would ideally be applied during rest. The proposed subject-dependent features, extracted from SC and ECG modalities, were found capable to significantly increase automatic stress detection accuracy over a multi-subject (N=24) data set, collected through an experiment of natural stress induction. They provided accuracy at the level of 95%, significantly improved to the respective result (86.05%) taken from common SC and ECG features that have been typically used in the past. They appeared also similarly effective in automatic frustration detection over a further dataset. The results of the present work indicate that the proposed subject-dependent features, can lead to significant advances in the performance of future relevant ER systems.