A cognitive architecture for modeling emotion dynamics: Intensity estimation from physiological signals

Jenke, Robert; Peer, Angelika

doi:10.1016/j.cogsys.2018.01.004

Cited by 9 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various approaches in affective sciences have been used to investigate the relationship between physiological responses and emotional experience. For example, dimensional models of emotion based on arousal and valence have been used widely to describe the experienced emotion and relate emotional intensity to physiological responses ( Bradley et al, 2001 ; Bradley & Lang, 2000 ; Lang et al, 1993 ; Jenke & Peer, 2018 ). These studies have demonstrated that arousal and valence ratings are related significantly to physiological responses to emotional stimuli.…”

Section: Introductionmentioning

confidence: 99%

“…However, they measured intensity as the level of arousal and valence instead of the level of the experienced discrete emotion, that is, fear specifically. Similarly, Jenke and Peer ( 2018 ) examined the link between emotional intensity and physiological signals, but it was difficult to define the specific physiological responses to fear since they used an appraisal model that conceptualizes emotion based on the dimensions of relevance, implication, coping potential, and normative significances.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Individuals’ Experienced Fear From Multimodal Physiological Responses to a Fear-Inducing Stimulus

Jang¹,

Byun²,

Park³

et al. 2020

ACP

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Individuals’ Experienced Fear From Multimodal Physiological Responses to a Fear-Inducing Stimulus

Jang¹,

Byun²,

Park³

et al. 2020

ACP

View full text Add to dashboard Cite

“…Krone et al [25] proposed a vector autoregressive Bayesian sentiment dynamics model to predict the basic emotions with long-term dependence based on short time sequences. Jenke and Peer [26] used dynamic field theory, combined with theoretical knowledge and data-driven experimental methods, to model the dynamic characteristics of emotions, deepening the understanding of emotional mechanisms. The above research fully exploited the time-dependent attributes of emotions and modeled the emotional system as a shock-recovery model with some steady-state characteristics.…”

Section: Related Workmentioning

confidence: 99%

“…After adding the Wong-Zakai correction, Eq. ( 25) can be transformed into the following It ô stochastic differential equation dy = α (y, t)dt + β (y, t)dW (t), (26) in which…”

Section: So We Obtain Dymentioning

confidence: 99%

A Novel Emotion Control System for Embedded Human–Computer Interaction in Green Iot

Gong

Lin

2019

IEEE Access

View full text Add to dashboard Cite

The green Internet of Things (IoT) technology has increased the need for a more emotion-connected human-computer interaction. The lower network latency and longer battery life ensure the continuous and real-time work of human-computer interaction systems in embedded environments. However, the lack of emotional dynamics limits the empathy capability of human-computer interactions. In this paper, we propose a novel emotion control system for embedded human-computer interaction in green IoT. The Van Der Pol emotional dynamics with nonlinear resilience is proposed for the modeling of human emotional mechanisms. Moreover, respectively for the autonomous and stochastic-excitation situation, we analyze the stability of the solution, based on which four kinds of the emotional health status are defined. Finally, we obtain the steady-state solution and design its feedback controller. Results of the numerical simulation and prototype demonstrate the validity and practical operability of the theory.

show abstract

“…It enables continues prediction of valance and arousal over time. In [19] a dynamic model is proposed based on dynamic field theory (DFT) enabling prediction of emotion intensity. DFT can be seen as a generalization of recurrent neural networks to continuous dimensions, adding a functional interpretation to each layer.…”

Section: Literature Reviewmentioning

confidence: 99%

On-body Sensing and Signal Analysis for User Experience Recognition in Human-Machine Interaction

Haratian

Timotijevic

2018

2018 4th International Conference on Frontiers of Signal Processing (ICFSP)

View full text Add to dashboard Cite

In this paper, a new algorithm is proposed for recognition of user experience through emotion detection using physiological signals, for application in human-machine interaction. The algorithm recognizes user's emotion quality and intensity in a two dimensional emotion space continuously. The continuous recognition of the user's emotion during human-machine interaction will enable the machine to adapt its activity based on the user's emotion in a real-time manner, thus improving user experience. The emotion model underlying the proposed algorithm is one of the most recent emotion models, which models emotion's intensity and quality in a continuous two-dimensional space of valance and arousal axes. Using only two physiological signals, which are correlated to the valance and arousal axes of the emotion space, is among the contributions of this paper. Prediction of emotion through physiological signals has the advantage of elimination of social masking and making the prediction more reliable. The key advantage of the proposed algorithm over other algorithms presented to date is the use of the least number of modalities (only two physiological signals) to predict the quality and intensity of emotion continuously in time, and using the most recent widely accepted emotion model.

show abstract

A cognitive architecture for modeling emotion dynamics: Intensity estimation from physiological signals

Cited by 9 publications

References 28 publications

Predicting Individuals’ Experienced Fear From Multimodal Physiological Responses to a Fear-Inducing Stimulus

Predicting Individuals’ Experienced Fear From Multimodal Physiological Responses to a Fear-Inducing Stimulus

A Novel Emotion Control System for Embedded Human–Computer Interaction in Green Iot

On-body Sensing and Signal Analysis for User Experience Recognition in Human-Machine Interaction

Contact Info

Product

Resources

About