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Hilborn, Olle
Publications (5 of 5) Show all publications
Hilborn, O. (2015). A Serious Game for Training in Emotion Regulation: From Design to Evaluation. (Licentiate dissertation). Karlskrona: Blekinge Institute of Technology
Open this publication in new window or tab >>A Serious Game for Training in Emotion Regulation: From Design to Evaluation
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Games are often used as training devices in various tasks, but proper biofeedback is more seldom used. Within an EU project it was explored how biofeedback games can target emotion regulation and be evaluated meaningfully. While many use games and biofeedback separately, here the focus was to combine them. This was explored through how the games were perceived and played while players were punished in-game, based on their physiological activity. By implementing games and study the interaction patterns in experimental settings, primarily correlational data was acquired. The results suggest that targeting cognitive constructs has to be validated for each specific game, since game strategies can influence the activation of the cognitive constructs.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Institute of Technology, 2015. p. 106 p.
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 1
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-00607 (URN)oai:bth.se:forskinfo934EC1DD6F403001C1257DAB0058A8B2 (Local ID)978-91-7295-270-6 (ISBN)oai:bth.se:forskinfo934EC1DD6F403001C1257DAB0058A8B2 (Archive number)oai:bth.se:forskinfo934EC1DD6F403001C1257DAB0058A8B2 (OAI)
Available from: 2015-02-10 Created: 2014-12-11 Last updated: 2018-01-11Bibliographically approved
Hagelbäck, J., Hilborn, O., Jercic, P., Johansson, S., Lindley, C., Svensson, J. & Wen, W. (2014). Psychophysiological Interaction and Empathic Cognition for Human-Robot Cooperative Work (PsyIntEC). In: Rohrbein, F.; Veiga, G.; Natale, C. (Ed.), Gearing Up and Accelerating Cross-Fertilization between Academic and Industrial Robotics Research in Europe: Technology Transfer Experiments from the ECHORD Project (pp. 283-299). Springer
Open this publication in new window or tab >>Psychophysiological Interaction and Empathic Cognition for Human-Robot Cooperative Work (PsyIntEC)
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2014 (English)In: Gearing Up and Accelerating Cross-Fertilization between Academic and Industrial Robotics Research in Europe: Technology Transfer Experiments from the ECHORD Project / [ed] Rohrbein, F.; Veiga, G.; Natale, C., Springer , 2014, p. 283-299Chapter in book (Refereed)
Abstract [en]

The aim of the PsyIntEC project is to explore affective and cognitive modeling of humans in human-robot interaction (HRI) as a basis for behavioral adaptation. To achieve this we have explored human affective perception of relevant modalities in human-human and human-robot interaction on a collaborative problem-solving task using psychophysiological measurements. The experiments conducted have given us valuable insight into the communicational and affective queues interplaying in such interactions from the human perspective. The results indicate that there is an increase in both positive and negative emotions when interacting with robots compared to interacting with another human or solving the task alone, but detailed analysis on shorter time segments is required for the results from all sensors to be conclusive and significant.

Place, publisher, year, edition, pages
Springer, 2014
Keywords
human-robot interaction, psychophysiology, affective modeling, robotics
National Category
Human Aspects of ICT Computer Sciences
Identifiers
urn:nbn:se:bth-6590 (URN)10.1007/978-3-319-02934-4_14 (DOI)000329752400016 ()oai:bth.se:forskinfoB5643A721B059E9AC1257D6D0036DE33 (Local ID)978-3-319-02933-7 (ISBN)oai:bth.se:forskinfoB5643A721B059E9AC1257D6D0036DE33 (Archive number)oai:bth.se:forskinfoB5643A721B059E9AC1257D6D0036DE33 (OAI)
Available from: 2014-10-10 Created: 2014-10-10 Last updated: 2018-01-11Bibliographically approved
Hilborn, O., Cederholm, H., Eriksson, J. & Lindley, C. (2013). A biofeedback game for training arousal regulation during a stressful task: The space investor. In: Lecture Notes in Computer Science: . Paper presented at 15th International Conference on Human-Computer Interaction, HCI International (pp. 403-410). Las Vegas: Springer, 8008(part 5)
Open this publication in new window or tab >>A biofeedback game for training arousal regulation during a stressful task: The space investor
2013 (English)In: Lecture Notes in Computer Science, Las Vegas: Springer , 2013, Vol. 8008, no part 5, p. 403-410Conference paper, Published paper (Refereed)
Abstract [en]

Emotion regulation is a topic that has considerable impact in our everyday lives, among others emotional biases that affect our decision making. A serious game that was built in order to be able to train emotion regulation is presented and evaluated here. The evaluation consisted of a usability testing and then an experiment that targeted the difficulty of the game. The results suggested adequate usability and a difficulty that requires the player to engage in managing their emotion in order to have a winning strategy.

Place, publisher, year, edition, pages
Las Vegas: Springer, 2013
Keywords
Emotion regulations, Serious games, Usability testing, Winning strategy
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-6893 (URN)10.1007/978-3-642-39342-6_44 (DOI)oai:bth.se:forskinfo95F13E373187CDE8C1257BE500493673 (Local ID)9783642393419 (ISBN)oai:bth.se:forskinfo95F13E373187CDE8C1257BE500493673 (Archive number)oai:bth.se:forskinfo95F13E373187CDE8C1257BE500493673 (OAI)
Conference
15th International Conference on Human-Computer Interaction, HCI International
Available from: 2013-09-13 Created: 2013-09-13 Last updated: 2018-01-11Bibliographically approved
Sohaib, A. T., Qureshi, S., Hagelbäck, J., Hilborn, O. & Jerčić, P. (2013). Evaluating classifiers for emotion recognition using EEG. In: : . Paper presented at 7th International Conference on Foundations of Augmented Cognition. Las Vegas: Springer
Open this publication in new window or tab >>Evaluating classifiers for emotion recognition using EEG
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2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

There are several ways of recording psychophysiology data from humans, for example Galvanic Skin Response (GSR), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper we focus on emotion detection using EEG. Various machine learning techniques can be used on the recorded EEG data to classify emotional states. K-Nearest Neighbor (KNN), Bayesian Network (BN), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are some machine learning techniques that previously have been used to classify EEG data in various experiments. Five different machine learning techniques were evaluated in this paper, classifying EEG data associated with specific affective/emotional states. The emotions were elicited in the subjects using pictures from the International Affective Picture System (IAPS) database. The raw EEG data were processed to remove artifacts and a number of features were selected as input to the classifiers. The results showed that it is difficult to train a classifier to be accurate over large datasets (15 subjects) but KNN and SVM with the proposed features were reasonably accurate over smaller datasets (5 subjects) identifying the emotional states with an accuracy up to 77.78%.

Place, publisher, year, edition, pages
Las Vegas: Springer, 2013
Keywords
Emotion detection, Emotion recognition, Emotional state, Galvanic skin response, K nearest neighbor (KNN), Large datasets, Machine learning techniques, Picture system
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-6852 (URN)10.1007/978-3-642-39454-6_53 (DOI)oai:bth.se:forskinfo4E725BBDBF0A9820C1257C2E003674CF (Local ID)9783642394539 (ISBN)oai:bth.se:forskinfo4E725BBDBF0A9820C1257C2E003674CF (Archive number)oai:bth.se:forskinfo4E725BBDBF0A9820C1257C2E003674CF (OAI)
Conference
7th International Conference on Foundations of Augmented Cognition
Note

Held as Part of 15th International Conference on Human-Computer Interaction, HCI International 2013

Available from: 2013-11-25 Created: 2013-11-25 Last updated: 2018-01-11Bibliographically approved
Jerčić, P., Astor, P. J., Adam, M., Hilborn, O., Schaff, K., Lindley, C., . . . Eriksson, J. (2012). A Serious Game using Physiological Interfaces for Emotion Regulation Training in the context of Financial Decision-Making. In: ECIS 2012 - Proceedings of the 20th European Conference on Information Systems: . Paper presented at 20th European Conference on Information Systems (ECIS 2012), Barcelona (pp. 1-14). AIS Electronic Library (AISeL)
Open this publication in new window or tab >>A Serious Game using Physiological Interfaces for Emotion Regulation Training in the context of Financial Decision-Making
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2012 (English)In: ECIS 2012 - Proceedings of the 20th European Conference on Information Systems, AIS Electronic Library (AISeL) , 2012, p. 1-14Conference paper, Published paper (Refereed)
Abstract [en]

Research on financial decision-making shows that traders and investors with high emotion regulation capabilities perform better in trading. But how can the others learn to regulate their emotions? ‘Learning by doing’ sounds like a straightforward approach. But how can one perform ‘learning by doing’ when there is no feedback? This problem particularly applies to learning emotion regulation, because learners can get practically no feedback on their level of emotion regulation. Our research aims at providing a learning environment that can help decision-makers to improve their emotion regulation. The approach is based on a serious game with real-time biofeedback. The game is settled in a financial context and the decision scenario is directly linked to the individual biofeedback of the learner’s heart rate data. More specifically, depending on the learner’s ability to regulate emotions, the decision scenario of the game continuously adjusts and thereby becomes more (or less) difficult. The learner wears an electrocardiogram sensor that transfers the data via Bluetooth to the game. The game itself is evaluated at several levels.

Place, publisher, year, edition, pages
AIS Electronic Library (AISeL), 2012
Keywords
Biofeedback, Emotion Regulation, Serious Games
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-17556 (URN)
Conference
20th European Conference on Information Systems (ECIS 2012), Barcelona
Note

open access

Available from: 2019-01-30 Created: 2019-01-30 Last updated: 2019-02-11Bibliographically approved
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