Computing nonlinear features of skin conductance to build the affective detection model

Cheng, Jing; Liu, Guangyuan

doi:10.1109/icccas.2013.6765349

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…One of the main challenges to deal with when combining physiology and machine learning in automatic human emotion and behavior understanding is the feature extraction from physiological signals. As a result, multiple sets of features from physiological signals are employed in the literature: some of the works extracted features based on signal statistics [3,32,36,43,51,56,59,79], while others worked with nonlinear features [22].…”

Section: Related Workmentioning

confidence: 99%

A Multimodal Approach for Predicting Changes in PTSD Symptom Severity

Mallol-Ragolta

Dhamija

Boult

2018

Proceedings of the 20th ACM International Conference on Multimodal Interaction

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The rising prevalence of mental illnesses is increasing the demand for new digital tools to support mental wellbeing. Numerous collaborations spanning the fields of psychology, machine learning and health are building such tools. Machine-learning models that estimate effects of mental health interventions currently rely on either user self-reports or measurements of user physiology. In this paper, we present a multimodal approach that combines selfreports from questionnaires and skin conductance physiology in a web-based trauma-recovery regime. We evaluate our models on the EASE multimodal dataset and create PTSD symptom severity change estimators at both total and cluster-level. We demonstrate that modeling the PTSD symptom severity change at the total-level with self-reports can be statistically significantly improved by the combination of physiology and self-reports or just skin conductance measurements. Our experiments show that PTSD symptom cluster severity changes using our novel multimodal approach are significantly better modeled than using self-reports and skin conductance alone when extracting skin conductance features from triggers modules for avoidance, negative alterations in cognition & mood and alterations in arousal & reactivity symptoms, while it performs statistically similar for intrusion symptom.

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Section: Related Workmentioning

confidence: 99%

A Multimodal Approach for Predicting Changes in PTSD Symptom Severity

Mallol-Ragolta

Dhamija

Boult

2018

Proceedings of the 20th ACM International Conference on Multimodal Interaction

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“…The results showed that the recognition rate increased when considering the nonlinear features versus linear ones. The study of Cheng and Liu concerned the extraction of nonlinear EDA‐based features in order to recognize the affective state of subjects, while watching different visual scenes.…”

Section: Introductionmentioning

confidence: 99%

Self‐similarity analysis of vehicle driver's electrodermal activity

Haouij

Ghozi

Poggi

et al. 2019

Quality & Reliability Eng

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This paper characterizes stress levels via a self-similarity analysis of the electrodermal activity (EDA) collected in a real-world driving context. To characterize the EDA richness over scales, the fractional Brownian motion (FBM) process and its corresponding exponent H, estimated via a wavelet-based approach, are used. Specifically, an automatic scale range selection is proposed in order to detect the linearity in a log scale diagram. The procedure is applied to the EDA signals, from the open database drivedb, originally captured on the foot and the hand of the drivers during a real-world driving experiment, designed to evoke different levels of arousal and stress. The estimated Hurst exponent H offers a distinction in stress levels when driving in highway versus city, with a reference to restful state of minimal stress level. Specifically, the estimated H values tend to decrease when the driving environmental complexity increases. In addition, the estimated H values on the foot EDA signals allow a better characterization of the driving task than that of hand EDA. The self-similarity analysis was applied to various physiological signals in literature but not to the EDA so far, a signal which was found to correlate most with human affect. The proposed analysis could be useful in real-time monitoring of stress levels in urban driving spaces, among other applications. KEYWORDSelectrodermal activity, Hurst exponent estimation, real-world driving, self-similarity, stress tracking, wavelet-based method 1502

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Computing nonlinear features of skin conductance to build the affective detection model

Cited by 2 publications

References 15 publications

A Multimodal Approach for Predicting Changes in PTSD Symptom Severity

A Multimodal Approach for Predicting Changes in PTSD Symptom Severity

Self‐similarity analysis of vehicle driver's electrodermal activity

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