Topics

An important goal of pervasive computing is to seamlessly integrate devices into the users’ everyday life, and explore various approaches to understanding information about users and surrounding environments. The human emotion is one of the most crucial and attractive information for pervasive computing. In many application scenarios, it would be beneficial if the emotion states could be detected with embedded sensors in smartphones and wearable devices, in order to raise user awareness, as well as understand how these emotions influence users’ decision making and reasoning. However, the emotion detection has proved to be a complex and challenging task, and thus, varying difficulties, regarding sensing modalities, data analysis, and application, arise.

1. Sensing Modalities – what to sense and how to sense it with embedded sensors in mobile devices and/or biosensors in wearable devices.
2. Data analysis – different approaches to emotion detection based on different types of collected data.
3. Application – How to effectively use the emotion information in pervasive computing and context-aware applications.

While there have been contributions targeting some of these challenges, there are still unsolved problems. The proposed workshop will explore the challenges of sensing, modeling, recognition, and synthesis of emotion detection with embedded sensors in smartphones and wearable devices for pervasive computing. Therefore, we invite unique contributions addressing these challenges and provide a workshop to facilitate collaboration among research groups focusing on emotion detection for pervasive computing.

Topics of interest include, but are not limited to:

• Theory, experimental design, computational models, algorithms, and evolutional investigation in emotion detection for pervasive computing.
• Emotion representations and signal characteristics that describe and identify emotions.
• Mobile data measurement/collection platforms for emotion detection.
• Approaches to obtain reliable ground truth and/or affective data annotation for emotion research.
• Emotion detection algorithms/approaches using data/signals collected with mobile devices and wearable devices.
• User studies and evaluation techniques for emotion detection.
• Automated systems that model and detect emotions.
• Awareness of emotions in collaboration or crowdsourcing.
• The novel use of emotion information in the pervasive computing applications.
• The application of emotion information for the work-life balance.
• The combined research of emotion recognition and artificial intelligence (AI).
• The investigation of the human-robot interaction.