1,259 research outputs found
Inferring Mood in Ubiquitous Conversational Video
Conversational social video is becoming a worldwide trend. Video communication allows a more natural interaction, when aiming to share personal news, ideas, and opinions, by transmitting both verbal content and nonverbal behavior. However, the automatic analysis of natural mood is challenging, since it is displayed in parallel via voice, face, and body. This paper presents an automatic approach to infer 11 natural mood categories in conversational social video using single and multimodal nonverbal cues extracted from video blogs (vlogs) from YouTube. The mood labels used in our work were collected via crowdsourcing. Our approach is promising for several of the studied mood categories. Our study demonstrates that although multimodal features perform better than single channel features, not always all the available channels are needed to accurately discriminate mood in videos
Sensing, Understanding, and Shaping Social Behavior
The ability to understand social systems through the aid of computational tools is central to the emerging field of computational social systems. Such understanding can answer epistemological questions on human behavior in a data-driven manner, and provide prescriptive guidelines for persuading humans to undertake certain actions in real-world social scenarios. The growing number of works in this subfield has the potential to impact multiple walks of human life including health, wellness, productivity, mobility, transportation, education, shopping, and sustenance. The contribution of this paper is twofold. First, we provide a functional survey of recent advances in sensing, understanding, and shaping human behavior, focusing on real-world behavior of users as measured using passive sensors. Second, we present a case study on how trust, which is an important building block of computational social systems, can be quantified, sensed, and applied to shape human behavior. Our findings suggest that:1) trust can be operationalized and predicted via computational methods (passive sensing and network analysis) and 2) trust has a significant impact on social persuasion; in fact, it was found to be significantly more effective than the closeness of ties in determining the amount of behavior change.U.S. Army Research Laboratory (Cooperative Agreement W911NF-09-2-0053
Beyond mobile apps: a survey of technologies for mental well-being
Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties, which facilitate the delivery of state-of-the-art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy, and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health toolkits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the data these ubiquitous devices can record, state of the art machine learning algorithms can lead to the development of robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time
Experiences of aiding autobiographical memory Using the SenseCam
Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible
Experiences of aiding autobiographical memory using the sensecam
Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible
Exploring the Affective Loop
Research in psychology and neurology shows that both body and mind are
involved when experiencing emotions (Damasio 1994, Davidson et al.
2003). People are also very physical when they try to communicate their
emotions. Somewhere in between beings consciously and unconsciously
aware of it ourselves, we produce both verbal and physical signs to make
other people understand how we feel. Simultaneously, this production of
signs involves us in a stronger personal experience of the emotions we
express.
Emotions are also communicated in the digital world, but there is little
focus on users' personal as well as physical experience of emotions in
the available digital media. In order to explore whether and how we can
expand existing media, we have designed, implemented and evaluated
/eMoto/, a mobile service for sending affective messages to others. With
eMoto, we explicitly aim to address both cognitive and physical
experiences of human emotions. Through combining affective gestures for
input with affective expressions that make use of colors, shapes and
animations for the background of messages, the interaction "pulls" the
user into an /affective loop/. In this thesis we define what we mean by
affective loop and present a user-centered design approach expressed
through four design principles inspired by previous work within Human
Computer Interaction (HCI) but adjusted to our purposes; /embodiment/
(Dourish 2001) as a means to address how people communicate emotions in
real life, /flow/ (Csikszentmihalyi 1990) to reach a state of
involvement that goes further than the current context, /ambiguity/ of
the designed expressions (Gaver et al. 2003) to allow for open-ended
interpretation by the end-users instead of simplistic, one-emotion
one-expression pairs and /natural but designed expressions/ to address
people's natural couplings between cognitively and physically
experienced emotions. We also present results from an end-user study of
eMoto that indicates that subjects got both physically and emotionally
involved in the interaction and that the designed "openness" and
ambiguity of the expressions, was appreciated and understood by our
subjects. Through the user study, we identified four potential design
problems that have to be tackled in order to achieve an affective loop
effect; the extent to which users' /feel in control/ of the interaction,
/harmony and coherence/ between cognitive and physical expressions/,/
/timing/ of expressions and feedback in a communicational setting, and
effects of users' /personality/ on their emotional expressions and
experiences of the interaction
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