Human emotion model based on discourse sentence for expression generation of conversation agent

Hakamata, Ai; Ren, Fuji; Tsuchiya, Seiji

doi:10.1109/nlpke.2008.4906812

Cited by 8 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the increasing use of human-computer interfaces, accurate automatic emotiondetection algorithms based on EEG data have become commonplace. EEG data have been used in studies of emotion recognition because emotions and their associated responses are processed primarily in the brain [96][97][98][99]. Using machine learning techniques to identify individual emotional states enhances understanding of human emotions [43].…”

Section: Nonlinear Dynamical Studies On Emotionmentioning

confidence: 99%

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Kargarnovin,

Hernandez,

Farahani

et al. 2023

Brain Sciences

View full text Add to dashboard Cite

(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.

show abstract

Section: Nonlinear Dynamical Studies On Emotionmentioning

confidence: 99%

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Kargarnovin,

Hernandez,

Farahani

et al. 2023

Brain Sciences

View full text Add to dashboard Cite

show abstract

“…The mapping method can be used in a higher-dimensional space to solve this problem. For this purpose, the proper strategy is to use the kernel trick (Hakamata, A., Ren, F., & Tsuchiya, S., 2008, October).…”

Section: Fig 4: Feature Extraction Block Diagrammentioning

confidence: 99%

A Modified Fuzzy Support Vector Machine Classification-Based Approach for Emotional Recognition Using Physiological Signals

Menhaj¹

2022

AzJHPC

View full text Add to dashboard Cite

Emotional state recognition has become an essential topic for human–robot interaction researches that diverted and covers a wide range of topics. By specifying emotional expressions, robots can identify the significant variables of human behavior and apply them to communicate in a very human-like fashion and develop interaction possibilities. The multimodality and spontaneity nature of human emotions make them hard to be recognized by robots. Each modality has its advantages and limitations, which, along with the unstructured behavior of spontaneous facial expressions, make several challenges for the proposed approaches in the literature. The most important of these approaches is based on a combination of explicit feature extraction methods and manual modality. This paper proposes a modified fuzzy support vector machine (FSVM) classification-based approach for emotional recognition using physiological signals. The main contribution of this study includes applying various data extraction indices and proper kernels for the FSVM classification method and evaluating the signal's richness in experimental tests. The developed emotional recognition method is also compared with conventional SVM and other existing state-of-the-art emotional recognition algorithms. The comparison results show an improved accuracy of the developed method over other approaches.

show abstract

“…Park et al [18] suggested that the robot uses a set of multi-model motions described as a combination of sentence types and emotions to express its behavior. Hakamata et al suggested a model to create emotional expressions in interactive sentences [19]. This study utilized the emotional weighting vector of the word in conversation and tagged each sentence of the conversation with it as mental status.…”

Section: Related Workmentioning

confidence: 99%

Contextual Emotion Appraisal Based on a Sentential Cognitive System for Robots

Ahn

Park

2021

Applied Sciences

View full text Add to dashboard Cite

Emotion plays a powerful role in human interaction with robots. In order to express more human-friendly emotions, robots need the capability of contextual appraisal that expresses the emotional relevance of various targets in the spatiotemporal situation. In this paper, an emotional appraisal methodology is proposed to cope with such contexts. Specifically, the Ortony, Clore, and Collins model is abstracted and simplified to approximate an emotional appraisal model in the form of a sentence-based cognitive system. The contextual emotion appraisal is modeled by formulating the emotional relationships among multiple targets and the emotional transition with events and time passing. To verify the proposed robotic system’s feasibility, simulations were conducted for scenarios where it interacts with humans manipulating liked or disliked objects on a table. This experiment demonstrated that the robot’s emotion could change over time like humans by using a proposed formula for emotional valence, which is moderated by emotion appraisal of occurring events.

show abstract

Human emotion model based on discourse sentence for expression generation of conversation agent

Cited by 8 publications

References 3 publications

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

A Modified Fuzzy Support Vector Machine Classification-Based Approach for Emotional Recognition Using Physiological Signals

Contextual Emotion Appraisal Based on a Sentential Cognitive System for Robots

Contact Info

Product

Resources

About