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Recognition of emotions by the emotional feedback through behavioral human poses
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
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2015 (English)In: International Journal of Computer Science Issues, ISSN 1694-0784, E-ISSN 1694-0814, Vol. 12, no 1, 7-17 p.Article in journal (Refereed) Published
Abstract [en]

The sensory perceptions from humans are intertwined channels,which assemble diverse data in order to decrypt emotionalinformation. Just by associations, humans can mix emotionalinformation, i.e. emotion detection through facial expressionscriteria, emotional speech, and the challenging field of emotionalbody language over the body poses and motion. In this work, wepresent an approach that can predict six basic universal emotionscollected by responses linked to human body poses, from acomputational perspective. The emotional outputs could be fedas inputs to a synthetic socially skilled agent capable ofinteraction, in the context of socially intelligent systems. Themethodology uses a classification technique of information fromsix images extracted from a video, entirely developed using themotion sensing input device of Xbox 360 by Microsoft. We aretaking into account that the emotional body language containsadvantageous information about the emotional state of humans,especially when bodily reaction brings about consciousemotional experiences. The body parts are windows that showemotions and they would be particularly suitable to decodingaffective states. The group of extracted images is merged in oneimage with all the relevant information. The recovered image willserve as input to the classifiers. The analysis of images fromhuman body poses makes it possible to obtain relevantinformation through the combination of proper data in the sameimage. It is shown by experimental results that the SVM candetect emotion with good accuracy compared to other classifiers.

Place, publisher, year, edition, pages
2015. Vol. 12, no 1, 7-17 p.
Keyword [en]
Detection of Emotional Information, Affective Computing, Body Gesture Analysis, Robotics, Classification, Machine Learning.
National Category
Computer Science
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-122138OAI: oai:DiVA.org:su-122138DiVA: diva2:865110
Available from: 2015-10-26 Created: 2015-10-26 Last updated: 2017-12-01Bibliographically approved
In thesis
1. Decisional-Emotional Support System for a Synthetic Agent: Influence of Emotions in Decision-Making Toward the Participation of Automata in Society
Open this publication in new window or tab >>Decisional-Emotional Support System for a Synthetic Agent: Influence of Emotions in Decision-Making Toward the Participation of Automata in Society
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Emotion influences our actions, and this means that emotion has subjective decision value. Emotions, properly interpreted and understood, of those affected by decisions provide feedback to actions and, as such, serve as a basis for decisions. Accordingly, "affective computing" represents a wide range of technological opportunities toward the implementation of emotions to improve human-computer interaction, which also includes insights across a range of contexts of computational sciences into how we can design computer systems to communicate and recognize the emotional states provided by humans. Today, emotional systems such as software-only agents and embodied robots seem to improve every day at managing large volumes of information, and they remain emotionally incapable to read our feelings and react according to them. From a computational viewpoint, technology has made significant steps in determining how an emotional behavior model could be built; such a model is intended to be used for the purpose of intelligent assistance and support to humans. Human emotions are engines that allow people to generate useful responses to the current situation, taking into account the emotional states of others. Recovering the emotional cues emanating from the natural behavior of humans such as facial expressions and bodily kinetics could help to develop systems that allow recognition, interpretation, processing, simulation, and basing decisions on human emotions. Currently, there is a need to create emotional systems able to develop an emotional bond with users, reacting emotionally to encountered situations with the ability to help, assisting users to make their daily life easier. Handling emotions and their influence on decisions can improve the human-machine communication with a wider vision. The present thesis strives to provide an emotional architecture applicable for an agent, based on a group of decision-making models influenced by external emotional information provided by humans, acquired through a group of classification techniques from machine learning algorithms. The system can form positive bonds with the people it encounters when proceeding according to their emotional behavior. The agent embodied in the emotional architecture will interact with a user, facilitating their adoption in application areas such as caregiving to provide emotional support to the elderly. The agent's architecture uses an adversarial structure based on an Adversarial Risk Analysis framework with a decision analytic flavor that includes models forecasting a human's behavior and their impact on the surrounding environment. The agent perceives its environment and the actions performed by an individual, which constitute the resources needed to execute the agent's decision during the interaction. The agent's decision that is carried out from the adversarial structure is also affected by the information of emotional states provided by a classifiers-ensemble system, giving rise to a "decision with emotional connotation" included in the group of affective decisions. The performance of different well-known classifiers was compared in order to select the best result and build the ensemble system, based on feature selection methods that were introduced to predict the emotion. These methods are based on facial expression, bodily gestures, and speech, with satisfactory accuracy long before the final system.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2015. 146 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 15-019
Keyword
Affective Computing; Machine Learning; Adversarial Risk Analysis; Broaden and Build Theory; Facial Expression Recognition; Speech Emotion Recognition; Detection of Emotional Information; Emotional self-regulation
National Category
Human Computer Interaction
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-122084 (URN)978-91-7649-291-8 (ISBN)
Public defence
2015-12-14, room L70, NOD Building, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 8: Accepted.

Available from: 2015-11-20 Created: 2015-10-23 Last updated: 2015-11-30Bibliographically approved

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