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Menezes, Maria Luiza Recena
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Publications (7 of 7) Show all publications
Menezes, M. L., Pinheiro Sant'Anna, A., Pavel, M., Jimison, H. & Alonso-Fernandez, F. (2018). Affective Ambient Intelligence: from Domotics to Ambient Intelligence. In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract. Paper presented at Artificial Intelligence International Conference, A2IC 2018, November 21-23, 2018, Barcelona, Spain (pp. 25-25).
Open this publication in new window or tab >>Affective Ambient Intelligence: from Domotics to Ambient Intelligence
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2018 (English)In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 25-25Conference paper, Oral presentation with published abstract (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-38503 (URN)
Conference
Artificial Intelligence International Conference, A2IC 2018, November 21-23, 2018, Barcelona, Spain
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2018-12-06Bibliographically approved
Cooney, M. & Menezes, M. L. (2018). Design for an Art Therapy Robot: An Explorative Review of the Theoretical Foundations for Engaging in Emotional and Creative Painting with a Robot. Multimodal Technologies Interact. Special Issue Emotions in Robots: Embodied Interaction in Social and Non-Social Environments, 2(3), Article ID 52.
Open this publication in new window or tab >>Design for an Art Therapy Robot: An Explorative Review of the Theoretical Foundations for Engaging in Emotional and Creative Painting with a Robot
2018 (English)In: Multimodal Technologies Interact. Special Issue Emotions in Robots: Embodied Interaction in Social and Non-Social Environments, ISSN 2414-4088, Vol. 2, no 3, article id 52Article in journal (Refereed) Published
Abstract [en]

Social robots are being designed to help support people’s well-being in domestic and public environments. To address increasing incidences of psychological and emotional difficulties such as loneliness, and a shortage of human healthcare workers, we believe that robots will also play a useful role in engaging with people in therapy, on an emotional and creative level, e.g., in music, drama, playing, and art therapy. Here, we focus on the latter case, on an autonomous robot capable of painting with a person. A challenge is that the theoretical foundations are highly complex; we are only just beginning ourselves to understand emotions and creativity in human science, which have been described as highly important challenges in artificial intelligence. To gain insight, we review some of the literature on robots used for therapy and art, potential strategies for interacting, and mechanisms for expressing emotions and creativity. In doing so, we also suggest the usefulness of the responsive art approach as a starting point for art therapy robots, describe a perceived gap between our understanding of emotions in human science and what is currently typically being addressed in engineering studies, and identify some potential ethical pitfalls and solutions for avoiding them. Based on our arguments, we propose a design for an art therapy robot, also discussing a simplified prototype implementation, toward informing future work in the area.

Place, publisher, year, edition, pages
Basel: MDPI, 2018
Keywords
social robots, art therapy, emotions, creativity, art robots, therapy robots
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-37884 (URN)10.3390/mti2030052 (DOI)
Funder
Knowledge Foundation, SIDUS AIR 20140220
Available from: 2018-09-03 Created: 2018-09-03 Last updated: 2019-05-23Bibliographically approved
Menezes, M. L., Pinheiro Sant'Anna, A. & Alonso-Fernandez, F. (2018). Methodology for Subject Authentification and Identification through EEG signal: equipment's and positioning artifacts. In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract. Paper presented at Artificial Intelligence International Conference, A2IC 2018, November 21-23, 2018, Barcelona, Spain (pp. 37-37).
Open this publication in new window or tab >>Methodology for Subject Authentification and Identification through EEG signal: equipment's and positioning artifacts
2018 (English)In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 37-37Conference paper, Oral presentation with published abstract (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-38502 (URN)
Conference
Artificial Intelligence International Conference, A2IC 2018, November 21-23, 2018, Barcelona, Spain
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2018-12-06Bibliographically approved
Menezes, M. L., Samara, A., Galway, L., Pinheiro Sant'Anna, A., Verikas, A., Alonso-Fernandez, F., . . . Bond, R. (2017). Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset. Personal and Ubiquitous Computing, 21(6), 1003-1013
Open this publication in new window or tab >>Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset
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2017 (English)In: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 21, no 6, p. 1003-1013Article in journal (Refereed) Published
Abstract [en]

One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user’s emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell’s Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the and waves and High Order Crossing of the EEG signal. © 2017, The Author(s).

Place, publisher, year, edition, pages
London: Springer London, 2017
Keywords
Classification (of information), Decision trees, Electroencephalography, Feature extraction, Speech recognition, Virtual reality, Affective Computing, Affective state, Benchmark datasets, Circumplex models, Classification accuracy, Electroencephalogram signals, Emotion recognition, Interaction experiences, Behavioral research
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-36499 (URN)10.1007/s00779-017-1072-7 (DOI)000416170900005 ()2-s2.0-85027845103 (Scopus ID)
Note

cited By 1

Available from: 2018-06-14 Created: 2018-06-14 Last updated: 2018-06-14Bibliographically approved
Camelo, G. A., Recena Menezes, M. L., Pinheiro Sant'Anna, A., Vicari, R. M. & Pereira, C. E. (2016). Control of Smart Environments Using Brain Computer Interface Based on Genetic Algorithm. Paper presented at 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016), Da Nang, Vietnam, 14-16 March, 2016. Lecture Notes in Computer Science, 9622, 773-781
Open this publication in new window or tab >>Control of Smart Environments Using Brain Computer Interface Based on Genetic Algorithm
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2016 (English)In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 9622, p. 773-781Article in journal (Refereed) Published
Abstract [en]

This work deals with the development of an interface to control a smart conference room using passive BCI (Brain Computer Interface). It compares a genetic algorithm developed in a previous project to control the smart conference room with a random control algorithm. The system controls features of the conference room such as air conditioner, lightning systems, electric shutters, entertainment devices, etc. The parameters of the algorithm are extracted from users biosignal using Emotiv Epoc Headset while the user performs an attention test. The tests indicate that the decisions made by the genetic algorithm lead to better results, but in a single execution cannot be considered an effective optimization algorithm. © Springer-Verlag Berlin Heidelberg 2016.

Place, publisher, year, edition, pages
Berlin/Heidelberg: Springer Berlin/Heidelberg, 2016
Keywords
Genetic algorithm, Smart environment, Brain computer interface, Emotive, EEG
National Category
Control Engineering
Identifiers
urn:nbn:se:hh:diva-32133 (URN)10.1007/978-3-662-49390-8_75 (DOI)000389381200075 ()2-s2.0-84961164411 (Scopus ID)
Conference
8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016), Da Nang, Vietnam, 14-16 March, 2016
Note

Funding: Home Systems Company, and the Brazilian research agencies Capes (project PROCAD), FINEP (project CRIAI) & CNPq

Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2018-03-23Bibliographically approved
Samara, A., Menezes, M. L. & Galway, L. (2016). Feature Extraction for Emotion Recognition and Modelling using Neurophysiological Data. In: GarciaBlas, J Carretero, J Ray, I Jin, Q Georgalas, N (Ed.), Proceedings - 2016 15th International Conference on Ubiquitous Computing and Communications and 2016 8th International Symposium on Cyberspace and Security, IUCC-CSS 2016: . Paper presented at 15th International Conference on Ubiquitous Computing and Communications (IUCC) / 8th International Symposium on Cyberspace and Security (CSS), DEC 14-16, 2016, Granada, Spain (pp. 138-144). New York: IEEE, Article ID 7828594.
Open this publication in new window or tab >>Feature Extraction for Emotion Recognition and Modelling using Neurophysiological Data
2016 (English)In: Proceedings - 2016 15th International Conference on Ubiquitous Computing and Communications and 2016 8th International Symposium on Cyberspace and Security, IUCC-CSS 2016 / [ed] GarciaBlas, J Carretero, J Ray, I Jin, Q Georgalas, N, New York: IEEE, 2016, p. 138-144, article id 7828594Conference paper, Published paper (Refereed)
Abstract [en]

The ubiquitous computing paradigm is becoming a reality; we are reaching a level of automation and computing in which people and devices interact seamlessly. However, one of the main challenges is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users' emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram (EEG) as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the users emotions. In this context, this paper investigates feature vector generation from EEG signals for the purpose of affective state modelling based on Russells Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect and interaction experiences through exploitation of different input modalities. The DEAP dataset was used within this work, along with a Support Vector Machine, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements, band power from the α, β, δ and θ waves, and High Order Crossing of the EEG signal. © 2016 IEEE.

Place, publisher, year, edition, pages
New York: IEEE, 2016
Keywords
EEG, bio-signal sensor, affective state modelling, feature extraction
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:hh:diva-35646 (URN)10.1109/IUCC-CSS.2016.027 (DOI)000408893100019 ()2-s2.0-85015251205 (Scopus ID)978-1-5090-5566-1 (ISBN)978-1-5090-5567-8 (ISBN)
Conference
15th International Conference on Ubiquitous Computing and Communications (IUCC) / 8th International Symposium on Cyberspace and Security (CSS), DEC 14-16, 2016, Granada, Spain
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2018-01-13Bibliographically approved
Lundström, J., Ourique de Morais, W., Menezes, M. L., Gabrielli, C., Bentes, J., Pinheiro Sant'Anna, A., . . . Nugent, C. (2016). Halmstad intelligent home - Capabilities and opportunities. In: Mobyen Uddin AhmedShahina BegumWasim Raad (Ed.), Internet of Things Technologies for HealthCare: Third International Conference, HealthyIoT 2016, Västerås, Sweden, October 18-19, 2016, Revised Selected Papers. Paper presented at 3rd International Conference on Internet of Things Technologies for HealthCare, HealthyIoT 2016, Västerås, Sweden, 18 October 2016 through 19 October, 2016 (pp. 9-15). Berlin: Springer Berlin/Heidelberg, 187
Open this publication in new window or tab >>Halmstad intelligent home - Capabilities and opportunities
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2016 (English)In: Internet of Things Technologies for HealthCare: Third International Conference, HealthyIoT 2016, Västerås, Sweden, October 18-19, 2016, Revised Selected Papers / [ed] Mobyen Uddin AhmedShahina BegumWasim Raad, Berlin: Springer Berlin/Heidelberg, 2016, Vol. 187, p. 9-15Conference paper, Published paper (Refereed)
Abstract [en]

Research on intelligent environments, such as smart homes, concerns the mechanisms that intelligently orchestrate the pervasive technical infrastructure in the environment. However, significant challenges are to build, configure, use and maintain these systems. Providing personalized services while preserving the privacy of the occupants is also difficult. As an approach to facilitate research in this area, this paper presents the Halmstad Intelligent Home and a novel approach for multioccupancy detection utilizing the presented environment. This paper also presents initial results and ongoing work. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.

Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2016
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211 ; 187
Keywords
Intelligent environments, Multi-occupancy detection, Automation, Health care, Intelligent agents, Internet of things, Intelligent environment, Intelligent home, Occupancy detections, Personalized service, Smart homes, Technical infrastructure, Intelligent buildings
National Category
Computer and Information Sciences Computer Systems Human Aspects of ICT
Identifiers
urn:nbn:se:hh:diva-37786 (URN)10.1007/978-3-319-51234-1_2 (DOI)2-s2.0-85011263177 (Scopus ID)978-3-319-51233-4 (ISBN)
Conference
3rd International Conference on Internet of Things Technologies for HealthCare, HealthyIoT 2016, Västerås, Sweden, 18 October 2016 through 19 October, 2016
Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2018-08-27Bibliographically approved
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