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.

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.

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.

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).

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.