Stress Classification for Gender Bias in Reading

Sharma, Nandita; Gedeon, Tamás D.

doi:10.1007/978-3-642-24965-5_39

Cited by 7 publications

(7 citation statements)

References 10 publications

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“…To date, only a few studies have proposed a stress detection based only on EDA, showing methodological limitations that reduce the application to real-life scenarios. In fact, despite satisfactory accuracies, they have often shown limitations in the EDA decomposition method, which affect the reliability of the estimated sympathetic response [47], [48], [50], [51]. More specifically, they have applied trough-to-peak algorithms (i.e., without decompose into tonic and phasic components) [48], [50] or filtering approaches [47], [51], achieving an average accuracy between 74.19% and 85.5% in binary classification.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, despite satisfactory accuracies, they have often shown limitations in the EDA decomposition method, which affect the reliability of the estimated sympathetic response [47], [48], [50], [51]. More specifically, they have applied trough-to-peak algorithms (i.e., without decompose into tonic and phasic components) [48], [50] or filtering approaches [47], [51], achieving an average accuracy between 74.19% and 85.5% in binary classification. These two methods have been extensively demonstrated to underestimate the sympathetic response in the frequent case of overlapped skin conductance phasic responses compared to a model-based approach (e.g., cvxEDA) [52], [54], [55], [66] (and others).…”

Section: Discussionmentioning

confidence: 99%

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Acute Stress State Classification Based on Electrodermal Activity Modeling

Greco

Valenza

Lázaro

et al. 2023

IEEE Trans. Affective Comput.

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Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using electrodermal activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a singlesubject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62%. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00%. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Acute Stress State Classification Based on Electrodermal Activity Modeling

Greco

Valenza

Lázaro

et al. 2023

IEEE Trans. Affective Comput.

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“…Various computational techniques have been used to objectively recognize stress using models based on techniques such as Bayesian networks [11], decision trees [12], support vector machines [13], and artificial neural networks [14]. These techniques have used a range of physiological (e.g., heart activity [15,16], brain activity [17,18], galvanic skin response [19], and skin temperature [12,20]) and physical (e.g., eye gaze [11], facial information [21]) measures for stress as inputs.…”

Section: Introductionmentioning

confidence: 99%

Thermal spatio-temporal data for stress recognition

et al. 2014

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Stress is a serious concern facing our world today, motivating the development of a better objective understanding through the use of non-intrusive means for stress recognition by reducing restrictions to natural human behavior. As an initial step in computer vision-based stress detection, this paper proposes a temporal thermal spectrum (TS) and visible spectrum (VS) video database ANUStressDB -a major contribution to stress research. The database contains videos of 35 subjects watching stressed and not-stressed film clips validated by the subjects. We present the experiment and the process conducted to acquire videos of subjects' faces while they watched the films for the ANUStressDB. Further, a baseline model based on computing local binary patterns on three orthogonal planes (LBP-TOP) descriptor on VS and TS videos for stress detection is presented. A LBP-TOP-inspired descriptor was used to capture dynamic thermal patterns in histograms (HDTP) which exploited spatio-temporal characteristics in TS videos. Support vector machines were used for our stress detection model. A genetic algorithm was used to select salient facial block divisions for stress classification and to determine whether certain regions of the face of subjects showed better stress patterns. Results showed that a fusion of facial patterns from VS and TS videos produced statistically significantly better stress recognition rates than patterns from VS or TS videos used in isolation. Moreover, the genetic algorithm selection method led to statistically significantly better stress detection rates than classifiers that used all the facial block divisions. In addition, the best stress recognition rate was obtained from HDTP features fused with LBP-TOP features for TS and VS videos using a hybrid of a genetic algorithm and a support vector machine stress detection model. The model produced an accuracy of 86%.

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“…We can recognize differences in behaviour in reading by gender [62], and differentiate English as a first language readers from second or later language readers [61]. We can differentiate responses to face replacement in videos [63].…”

Section: Gendermentioning

confidence: 99%

Bio-inspired computing tools and applications: position paper

Gedeon

2017

Int. j. inf. tecnol.

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The confluence of significant computational power and inexpensive sensors provides the opportunity to reliably collect large volumes of information from the world and extract humanly useful information resources. This paper reviews a coherent body of work over the last 20? years focused on development of advanced bio-inspired computing techniques, and their applications primarily for human related data in behaviour and human centered computing. We close with a synthesis proposing an experiment analysis methodology combining these tools.

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Stress Classification for Gender Bias in Reading

Cited by 7 publications

References 10 publications

Acute Stress State Classification Based on Electrodermal Activity Modeling

Acute Stress State Classification Based on Electrodermal Activity Modeling

Thermal spatio-temporal data for stress recognition

Bio-inspired computing tools and applications: position paper

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