This framework provides means for implementing adaptive systems using mobile devices with wearable sensors.Īffective computing research has advanced emotion recognition systems using facial expressions, voices, gaits, and physiological signals, yet these methods are often impractical.
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Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. Furthermore, as a test-bed application for our work, we selected computer games. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. We begin with discussion of selected issues regarding the applications of affective computing techniques. In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. Finally, we validate the effectiveness of our approach using two groups of recruited subjects. Next, we use machine learning techniques to build a model that establishes the connection between mental states and the extracted multimodal signals. First, we extract a suite of multimodal time-series signals using modern computer vision and signal processing techniques, from recruited participants while they are immersed in online social media that elicit emotions and emotion transitions. In this study, we investigate how users’ online social activities and physiological signals detected through ubiquitous sensors can be utilized in realistic scenarios for monitoring their mental health states. With the widespread adoption of social media and mobile devices, and rapid advances in artificial intelligence, a unique opportunity arises for tackling mental health problems. Mental illness is becoming a major plague in modern societies and poses challenges to the capacity of current public health systems worldwide.
Thus, this work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behavioural modalities, along with contextual measurements, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system. The multimodal nature of stress and the research conducted in this area suggest that the developed method will depend on several modalities. Unfortunately, an automatic, continuous and unobtrusive early stress detection method does not exist yet. To prevent stress from becoming chronic and provoking irreversible damages, it is necessary to detect it in its early stages. Continuous high mental workloads and non-stop technological development, which leads to constant change and need for adaptation, makes the problem increasingly serious for office workers.
Stress is a major problem of our society, as it is the cause of many health problems and huge economic losses in companies.
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