Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Research on the properties of circadian systems amenable to study in space

Three areas of inquiry are reported for the Skylab Experiment S-071 whose objective was to study the circadian system of a mammal during space flight. The thermoregulatory behavior of the Perognathus longimembris, or little pocket mouse, was studied under conditions of constant dark and constant temperature in the prolonged weightless environment of Skylab. The following specific questions were studied: (1) the effects of weightlessness on circadian periodicity in the little pocket mouse; (2) stability of the free-running circadian period of body temperature of the little pocket mouse exposed to simulated launch stress; and (3) characteristics of the circadian rhythm of body temperature in the little pocket mouse. Diagrams of the electronic circuitry and hardware used in the experiment are shown and results are given in both graphical and tabular form. The methods used in the experiment are fully documented, along with conclusions and recommendations for future research

How to capture the heart ? Reviewing 20 years of emotion measurement in advertising.

In the latest decades, emotions have become an important research topic in all behavioral sciences, and not the least in advertising. Yet, advertising literature on how to measure emotions is not straightforward. The major aim of this article is to give an update on the different methods used for measuring emotions in advertising and to discuss their validity and applicability. We further draw conclusions on the relation between emotions and traditional measures of advertising effectiveness. We finally formulate recommendations on the use of the different methods and make suggestions for future research.Research; Emotions; Science; Advertising; Effectiveness; Recommendations;

Speech-based recognition of self-reported and observed emotion in a dimensional space

The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance

xDelia final report: emotion-centred financial decision making and learning

xDelia is a 3-year pan-European project building on the knowledge, skills, and competences of seven partner organisations from a variety of research disciplines and from business. The principal objective of xDelia is to develop technology-enhanced learning approaches that help improve the financial decision making of investors who trade frequently using an electronic trading platform. We focus on emotions, and how they affect maladaptive decision biases and trading performance. Our earlier field work with traders has shown that the development of emotion regulation skills is a key facet of trader expertise. For that reason we consider expert traders our benchmark for adaptive behaviour rather than normative rationality. Our goal is to provide investors with the tools and techniques to develop greater self-awareness of internal states, increase their ability to reflect critically on emotion-informed choices, develop emotion management skills, and support the transfer of these skills to the real-world practice setting of financial trading. This report provides a comprehensive overview of what xDelia is about and what we have achieved over the life of the project. In the sections that follow, we explain the decision problems investors are faced with in a fast paced environment and the limitations of traditional approaches to reduce cognitive errors; introduce an alternative, technology-enhanced learning approach of diagnosis and feedback, skill development, and transfer; describe the learning intervention comprising twelve autonomous learning elements that we have developed; and present evidence from thirty-five studies we have conducted on learning effects and stakeholder acceptance

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial