14,926 research outputs found
Affective games:a multimodal classification system
Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in playerâs psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation
Automatic Measurement of Affect in Dimensional and Continuous Spaces: Why, What, and How?
This paper aims to give a brief overview of the current state-of-the-art in automatic measurement of affect signals in dimensional and continuous spaces (a continuous scale from -1 to +1) by seeking answers to the following questions: i) why has the field shifted towards dimensional and continuous interpretations of affective displays recorded in real-world settings? ii) what are the affect dimensions used, and the affect signals measured? and iii) how has the current automatic measurement technology been developed, and how can we advance the field
Designing gestures for affective input: an analysis of shape, effort and valence
We discuss a user-centered approach to incorporating affective expressions in interactive applications, and argue for a design that addresses both body and mind. In particular, we have studied the problem of finding a set of affective gestures. Based on previous work in movement analysis and emotion theory [Davies, Laban and Lawrence, Russell], and a study of an actor expressing emotional states in body movements, we have identified three underlying dimensions of movements and emotions: shape, effort and valence. From these dimensions we have created a new affective interaction model, which we name the affective gestural plane model. We applied this model to the design of gestural affective input to a mobile service for affective messages
An original framework for understanding human actions and body language by using deep neural networks
The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour.
By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way.
These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively.
While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements;
both are essential tasks in many computer vision applications, including event recognition, and video surveillance.
In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided.
The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements.
All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods
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
Emotion capture based on body postures and movements
In this paper we present a preliminary study for designing interactive
systems that are sensible to human emotions based on the body movements. To do
so, we first review the literature on the various approaches for defining and
characterizing human emotions. After justifying the adopted characterization
space for emotions, we then focus on the movement characteristics that must be
captured by the system for being able to recognize the human emotions.Comment: 22 page
Enactivism, other minds, and mental disorders
Although enactive approaches to cognition vary in terms of their character and scope, all endorse several core claims. The first is that cognition is tied to action. The second is that cognition is composed of more than just in-the-head processes; cognitive activities are externalized via features of our embodiment and in our ecological dealings with the people and things around us. I appeal to these two enactive claims to consider a view called âdirect social perceptionâ : the idea that we can sometimes perceive features of other minds directly in the character of their embodiment and environmental interactions. I argue that if DSP is true, we can probably also perceive certain features of mental disorders as well. I draw upon the developmental psychologist Daniel Sternâs notion of âforms of vitalityââlargely overlooked in these debatesâto develop this idea, and I use autism as a case study. I argue further that an enactive approach to DSP can clarify some ways we play a regulative role in shaping the temporal and phenomenal character of the disorder in question, and it may therefore have practical significance for both the clinical and therapeutic encounter
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