Call Center Stress Recognition with Person-Specific Models

Hernández, Javier; Morris, Rob R.; Picard, Rosalind W.

doi:10.1007/978-3-642-24600-5_16

Cited by 166 publications

(132 citation statements)

References 12 publications

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“…Moreover, long-term recordings enabled an exciting new medical discovery-a significant correlation between the amount of EDA and the duration of postseizure brain wave suppression (Poh, Loddenkemper, Reinsberger, Swenson, Goyal, Madsen, et al, 2012). The wrist location has been used successfully for measurement of autonomic stress in call centers (Hernandez, Morris, & Picard, 2011), for communication of emotional arousal in autism (Picard, 2009), for identifying emotional arousal in children eating in the absence of hunger (Pieper & Laugero, 2013), and for measurement of arousal during sleep (Sano & Picard, 2011).…”

Section: Long-term Measurement Of Arousal Using Edamentioning

confidence: 99%

Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry

2015

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Using "big data" from sensors worn continuously outside the lab, researchers have observed patterns of objective physiology that challenge some of the long-standing theoretical concepts of emotion and its measurement. One challenge is that emotional arousal, when measured as sympathetic nervous system activation through electrodermal activity, can sometimes differ significantly across the two halves of the upper body. We show that traditional measures on only one side may lead to misjudgment of arousal. This article presents daily life and controlled study data, as well as existing evidence from neuroscience, supporting the influence of multiple emotional substrates in the brain causing innervation on different sides of the body. We describe how a theory of multiple arousals explains the asymmetric EDA findings. Keywordsaffective computing, ambulatory measurement, electrodermal activity (EDA), emotional arousal, idiographic research, sympathetic nervous system (SNS)

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Section: Long-term Measurement Of Arousal Using Edamentioning

confidence: 99%

Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry

2015

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“…level and response) and neural activity (EEG) have been employed for physiological measures of stress (e.g. [1]- [5]). Most of these channels require sensor contact.…”

Section: Introductionmentioning

confidence: 99%

Automated mental stress recognition through mobile thermal imaging

Cho

2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)

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Abstract-Mental stress is a critical problem in our modern society. This form of stress strongly affects our well being, and technology is needed to help us to manage health problems. The ability to automatically recognize a person's mental stress can be fundamental in supporting stress and health management. This research focuses on the use of mobile thermal imaging, a new and less explored sensor, to merge the measurement of multiple physiological signatures into one sensor and to build a reliable mental stress automatic recognition model. Mobile thermal imaging has greater potentials for real-world applications given that it is small and light weight, and requires low computation cost. To make mobile thermal imaging a robust multimodal stress sensor, we have so far contributed: i) a new robust respiration tracking method; and ii) a novel respiration-based automatic stress recognition model that works in ubiquitous settings. We are currently investigating new thermal signatures from underexplored body regions (i.e. trapezius muscle) and formulating a research framework to fuse multiple thermal signatures for more reliable stress recognition outcomes.

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“…Several technologies have been developed to recognize stress level; some methods are based on physiological signals: blood pressure [24], heart rate [24], heart rate variability (HRV) [4,24], and skin conductance [8,20]. Activity-aware mental stress detection is also available using Bayesian network and SVM.…”

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confidence: 99%

Multi-level assessment model for wellness service based on human mental stress level

Jung

Yoon

2016

Multimed Tools Appl

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In this paper, we measure human physiological changes from different body parts to quantify human mental stress level by using multimodal bio-sensors. By integrating these physiological responses, we generate bio-index and rule for the prediction of mental status, such as tension, normal, and relax. We also develop an inspection service middleware for analyzing health parameters such as electroencephalography (EEG), electrocardiography (ECG), oxygen saturation (SpO2), blood pressure (BP), and respiration rate (RR). In this service middleware, we use the multi-level assessment model for mental stress level that consists of three steps as follows; classification, reasoning, and decision making. The classification of datasets from bio-sensors is enabled by fuzzy logic and SVM algorithm. The reasoning uses the decision-tree model and random forest algorithm to classify the mental stress level from the health parameters. Finally, we propose a prediction model to make a decision for the wellness contents by using Expectation Maximization (EM).

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Call Center Stress Recognition with Person-Specific Models

Cited by 166 publications

References 12 publications

Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry

Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry

Automated mental stress recognition through mobile thermal imaging

Multi-level assessment model for wellness service based on human mental stress level

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