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Driven by the maturing of Internet and the development of the Internet of Things, people are becoming connected not only via the smart personal devices, but also via the increasingly responsive and connected environments, which brings up new opportunities to involve potential user group like elderly people into this network through tangible interaction and embedded systems. This paper presents the design and implementation of a participatory view-sharing system which consists of a group of specially designed camera kits and a gallery-like interactive installation, aiming to increase the social connection between elderly people living in a care home and the local communities around. The camera kits are designed to provide openness for local people to share sceneries via public participation, while the interactive installation presents the continuous real-time changes of the shared outside views, and triggers further communication between sharers and receivers through a “postcard-sending” metaphor. In this study, we mainly explore the possibilities of connecting people and facilitating social interaction through the combination of online and offline sharing behaviour, as well as the application of IoT technology.

Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions. This paper proposes a hierarchical Bayesian model to learn in unsupervised manner the behavior of individuals and of the crowd as a single entity, and explore the relation between behavior and emotions to infer emotional states. Information about the motion patterns of individuals are described using a self-organizing map, and a hierarchical Bayesian network builds probabilistic models to identify behaviors and infer the emotional state of individuals and the crowd. This model is trained and tested using data produced from simulated scenarios that resemble real-life environments. The conducted experiments tested the efficiency of our method to learn, detect and associate behaviors with emotional states yielding accuracy levels of 74% for individuals and 81% for the crowd, similar in performance with existing methods for pedestrian behavior detection but with novel concepts regarding the analysis of crowds.

Driven by the maturing of Internet and the development of the Internet of Things, people are becoming connected not only via the smart personal devices, but also via the increasingly responsive and connected environments, which brings up new opportunities to involve potential user group like elderly people into this network through tangible interaction and embedded systems. This paper presents the design and implementation of a participatory view-sharing system which consists of a group of specially designed camera kits and a gallery-like interactive installation, aiming to increase the social connection between elderly people living in a care home and the local communities around. The camera kits are designed to provide openness for local people to share sceneries via public participation, while the interactive installation presents the continuous real-time changes of the shared outside views, and triggers further communication between sharers and receivers through a “postcard-sending” metaphor. In this study, we mainly explore the possibilities of connecting people and facilitating social interaction through the combination of online and offline sharing behaviour, as well as the application of IoT technology.

Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions. This paper proposes a hierarchical Bayesian model to learn in unsupervised manner the behavior of individuals and of the crowd as a single entity, and explore the relation between behavior and emotions to infer emotional states. Information about the motion patterns of individuals are described using a self-organizing map, and a hierarchical Bayesian network builds probabilistic models to identify behaviors and infer the emotional state of individuals and the crowd. This model is trained and tested using data produced from simulated scenarios that resemble real-life environments. The conducted experiments tested the efficiency of our method to learn, detect and associate behaviors with emotional states yielding accuracy levels of 74% for individuals and 81% for the crowd, similar in performance with existing methods for pedestrian behavior detection but with novel concepts regarding the analysis of crowds.

Brain-computer interface (BCI) technologies, or technologies that use online brain signal processing, have a great promise to improve human interactions with computers, their environment, and even other humans. Despite this promise, there are no current serious BCI technologies in widespread use, due to the lack of robustness in BCI technologies. The key neural aspect of this lack of robustness is human variability, which has two main components: (1) individual differences in neural signals and (2) intraindividual variability over time. In order to develop widespread BCI technologies, it will be necessary to address this lack of robustness. However, it is currently unknown how neural variability affects BCI performance. To accomplish these goals, it is essential to obtain data from large numbers of individuals using BCI technologies over considerable lengths of time. One promising method for this is through the use of BCI technologies embedded into games with a purpose (GWAP). GWAP are a game-based form of crowdsourcing which players choose to play for enjoyment and during which the player performs key tasks which cannot be automated but that are required to solve research questions. By embedding BCI paradigms in GWAP and recording neural and behavioral data, it should be possible to much more clearly understand the differences in neural signals between individuals and across different time scales, enabling the development of novel and increasingly robust adaptive BCI algorithms.

Brain-computer interface (BCI) technologies, or technologies that use online brain signal processing, have a great promise to improve human interactions with computers, their environment, and even other humans. Despite this promise, there are no current serious BCI technologies in widespread use, due to the lack of robustness in BCI technologies. The key neural aspect of this lack of robustness is human variability, which has two main components: (1) individual differences in neural signals and (2) intraindividual variability over time. In order to develop widespread BCI technologies, it will be necessary to address this lack of robustness. However, it is currently unknown how neural variability affects BCI performance. To accomplish these goals, it is essential to obtain data from large numbers of individuals using BCI technologies over considerable lengths of time. One promising method for this is through the use of BCI technologies embedded into games with a purpose (GWAP). GWAP are a game-based form of crowdsourcing which players choose to play for enjoyment and during which the player performs key tasks which cannot be automated but that are required to solve research questions. By embedding BCI paradigms in GWAP and recording neural and behavioral data, it should be possible to much more clearly understand the differences in neural signals between individuals and across different time scales, enabling the development of novel and increasingly robust adaptive BCI algorithms.

Game developers have identified, explored and discussed many of the key issues that arise for players interacting in game worlds. However, there is a need to assess the thoughts and opinions of game-players on these issues. This paper reports the results of two player-centered studies aimed at investigating these issues from the player’s perspective. The first study, a focus group, supports some of the issues identified by game developers; consistency, intuitiveness and freedom of expression, and identifies new issues; immersion and physics. The second study, a questionnaire, examined the relationship of these issues to game-type preference and game-playing experience. This paper represents important initial exploratory research that supplements the existing literature by focusing on the player’s perspective and exploring which issues and context have the most impact on player enjoyment.