Naturalistic Affective Expression Classification by a Multi-Stage Approach Based on Hidden Markov Models

In naturalistic behaviour, the affective states of a person change at a rate much slower than the typical rate at which video or audio is recorded (e.g. 25fps for video). Hence, there is a high probability that consecutive recorded instants of expressions represent a same affective content. In this paper, a multi-stage automatic affective expression recognition system is proposed which uses Hidden Markov Models (HMMs) to take into account this temporal relationship and finalize the classification process. The hidden states of the HMMs are associated with the levels of affective dimensions to convert the classification problem into a best path finding problem in HMM. The system was tested on the audio data of the Audio/Visual Emotion Challenge (AVEC) datasets showing performance significantly above that of a one-stage classification system that does not take into account the temporal relationship, as well as above the baseline set provided by this Challenge. Due to the generality of the approach, this system could be applied to other types of affective modalities

Assessment of Biosignals for Managing a Virtual Keyboard

In this paper we propose an assessment of biosignals for handling an application based on virtual keyboard and automatic scanning. The aim of this work is to measure the effect of using such application, through different interfaces based on electromyography and electrooculography, on cardiac and electrodermal activities. Five people without disabilities have been tested. Each subject wrote twice the same text using an electromyography interface in first test and electrooculography in the second one. Each test was divided into four parts: instruction, initial relax, writing and final relax. The results of the tests show important differences in the electrocardiogram and electrodermal activity among the parts of tests.Junta de Andalucía p08-TIC-363

Multi-score Learning for Affect Recognition: the Case of Body Postures

An important challenge in building automatic affective state recognition systems is establishing the ground truth. When the groundtruth is not available, observers are often used to label training and testing sets. Unfortunately, inter-rater reliability between observers tends to vary from fair to moderate when dealing with naturalistic expressions. Nevertheless, the most common approach used is to label each expression with the most frequent label assigned by the observers to that expression. In this paper, we propose a general pattern recognition framework that takes into account the variability between observers for automatic affect recognition. This leads to what we term a multi-score learning problem in which a single expression is associated with multiple values representing the scores of each available emotion label. We also propose several performance measurements and pattern recognition methods for this framework, and report the experimental results obtained when testing and comparing these methods on two affective posture datasets

Design of a fuzzy affective agent based on typicality degrees of physiological signals

Conference paper presented at International Conference on Information Processing and Management in July 2014Physiology-based emotionally intelligent paradigms provide an opportunity to enhance human computer interactions by continuously evoking and adapting to the user experiences in real-time. However , there are unresolved questions on how to model real- time emotionally intelligent applications through mapping of physiological patterns to users ' affective states. In ·this study, we consider an approach for design of fuzzy affective agent based on the concept of typicality. We propose the use of typicality degrees of physiological patterns to construct the fuzzy rules representing the continuous transitions of user 's affective states. The approach was tested· on experimental data in which physiological measures were recorded on players involved in an action game to characterize various gaming experiences . We show that , in addition to exploitation of the results to characterize users ' affective states through .typicality degrees, this approach is a systematic way to automatically define fuzzy rules from experimental data for an affective agent to be used in real -time continuous assessment of user's affective states.Physiology-based emotionally intelligent paradigms provide an opportunity to enhance human computer interactions by continuously evoking and adapting to the user experiences in real-time. However , there are unresolved questions on how to model real- time emotionally intelligent applications through mapping of physiological patterns to users ' affective states. In ·this study, we consider an approach for design of fuzzy affective agent based on the concept of typicality. We propose the use of typicality degrees of physiological patterns to construct the fuzzy rules representing the continuous transitions of user 's affective states. The approach was tested· on experimental data in which physiological measures were recorded on players involved in an action game to characterize various gaming experiences . We show that , in addition to exploitation of the results to characterize users ' affective states through .typicality degrees, this approach is a systematic way to automatically define fuzzy rules from experimental data for an affective agent to be used in real -time continuous assessment of user's affective states

A game-based corpus for analysing the interplay between game context and player experience

Recognizing players’ affective state while playing video games has been the focus of many recent research studies. In this paper we describe the process that has been followed to build a corpus based on game events and recorded video sessions from human players while playing Super Mario Bros. We present different types of information that have been extracted from game context, player preferences and perception of the game, as well as user features, automatically extracted from video recordings. We run a number of initial experiments to analyse players’ behavior while playing video games as a case study of the possible use of the corpus.peer-reviewe

Discouraging sedentary behaviors using interactive play

Regular physical activity has many benefits, including to a person’s physical, emotional, and cognitive well-being [1]. Although adults should achieve 150 minutes of moderate- to vigorous-intensity physical activity per week, only 15 percent of adults meet these guidelines in at least 10-minute bouts, and only 5 percent of adults meet these guidelines in at least 30-minute bouts on five or more days per week (see [2]). For children, the statistics are even more discouraging. Although kids should get 60 minutes of activity per day, only 7 percent of Canadian youth accumulate 60 minutes per day six days a week (see [2]). The exercise habits adopted by children and pre-teens during this critical period can have lifelong consequences in physical health and self esteem. To encourage physical activity, researchers and developers in HCI have created a variety of “exergames,” which encourage people to exercise by integrating exertion into the game mechanics (e.g., [3]). Many exergames have focused on providing intense physical activity for players and have been shown to yield sufficient exertion to obtain the aforementioned benefits to a player’s well-being. However, recent work among health researchers has shown that there are also negative physiological consequences associated with sedentary behavior and that these consequences are distinct from those that result from a lack of physical activity [1]. Although this may seem surprising, physical activity and sedentary behavior are not mutually exclusive. Even if a person is physically active (e.g., biking to work in the morning), she can also be sedentary (e.g., by primarily sitting for the remaining waking hours); the effects of too much sitting are physiologically distinct from too little exercise [1]. The potential negative health outcomes are of particular relevance to populations who spend large parts of the day sitting, for example, schoolchildren who spend many hours a day sitting at their desks, and groups that struggle to gain access to opportunities for regular physical activity, for example, people with mobility impairments and older adults in long-term care

The Impact of Negative Game Reviews and User Comments on Player Experience

Game reviews and player ratings have an effect on the commercial success of games. They are used extensively by game developers to gauge the success of their titles and by potential buyers to make more informed purchase decisions. However, their potential influence on player experience remains uncertain. We investigated how game reviews and user comments influence players' affective states and experiences during game play. We found that both professional reviews and user comments (especially the negative comments) affected experience measured through game ratings, and that this effect was not mediated by changes in players' moods. Our results are important to the game industry because of the meaningful negative effect that user and critic comments can have on individual player experience and the resulting commercial success of a game.Ye

Games for the assessment and treatment of mental health

Contains fulltext : 177663.pdf (publisher's version ) (Open Access)The community for research on video games for assessment and intervention for mental health spans multiple disciplines, from cognitive sciences, computer science, and interaction design, to psychology, neurobiology, and medicine. The goal of this workshop is to bring together an international group of researchers to discuss the current state of games for mental health and formulate a plan for moving this research agenda forward in the CHI PLAY community. The workshop has the following objectives: Bring together an international group of researchers. Create an overview of current game-based approaches for the assessment and interventions of mental health. Discuss interests and directions for innovation. Identify overlap between research groups. Define a plan for knowledge exchange and collaboration between groups -- e.g., through internships, research visits, invited presentations, and formal and informal collaborations. Participants will have the opportunity to gain knowledge about the state of the international research community interested in game-based solutions for mental health. There will be opportunities to meet peers in different stages of their careers and discuss opportunities for future collaborations to consolidate a community in this emerging area.CHI PLAY '17: Annual Symposium on Computer-Human Interaction in Play (Amsterdam, the Netherlands, October 15 - 18, 2017

Emotional response and visual attention to non-photorealistic images

Non-photorealistic rendering (NPR) algorithms are used to produce stylized images, and have generally been evaluated on the aesthetic qualities of the resulting images. NPR-produced images have been used for aesthetic and practical reasons in media intended to produce an emotional reaction in a consumer (e.g., computer games, films, advertisements, and web sites); however, it is not understood how non-photorealistic rendering affects the emotion portrayed in an image. We conducted a study of subjective emotional response and visual attention to five common NPR approaches, two blurring techniques, and the original image with 42 participants, and found that the NPR algorithms dampened participants emotional responses in terms of arousal (activation) and valence (pleasure). Gaze data revealed that NPR rendering of images might reduce emotional response to an image by producing confusion, creating distracting visual artifacts, causing the loss of meaningful semantic information, or causing users to lose interest in the resulting image