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

Choreographic and Somatic Approaches for the Development of Expressive Robotic Systems

As robotic systems are moved out of factory work cells into human-facing environments questions of choreography become central to their design, placement, and application. With a human viewer or counterpart present, a system will automatically be interpreted within context, style of movement, and form factor by human beings as animate elements of their environment. The interpretation by this human counterpart is critical to the success of the system's integration: knobs on the system need to make sense to a human counterpart; an artificial agent should have a way of notifying a human counterpart of a change in system state, possibly through motion profiles; and the motion of a human counterpart may have important contextual clues for task completion. Thus, professional choreographers, dance practitioners, and movement analysts are critical to research in robotics. They have design methods for movement that align with human audience perception, can identify simplified features of movement for human-robot interaction goals, and have detailed knowledge of the capacity of human movement. This article provides approaches employed by one research lab, specific impacts on technical and artistic projects within, and principles that may guide future such work. The background section reports on choreography, somatic perspectives, improvisation, the Laban/Bartenieff Movement System, and robotics. From this context methods including embodied exercises, writing prompts, and community building activities have been developed to facilitate interdisciplinary research. The results of this work is presented as an overview of a smattering of projects in areas like high-level motion planning, software development for rapid prototyping of movement, artistic output, and user studies that help understand how people interpret movement. Finally, guiding principles for other groups to adopt are posited.Comment: Under review at MDPI Arts Special Issue "The Machine as Artist (for the 21st Century)" http://www.mdpi.com/journal/arts/special_issues/Machine_Artis

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence

A user-centred approach to affective interaction

We have built eMoto, a mobile service for sending and receiving affective messages, with the explicit aim of addressing the inner experience of emotions. eMoto is a designed artifact that carries emotional experiences only achieved through interaction. Following on the theories of embodiment, we argue emotional experiences can not be design in only design for. eMoto is the result of a user-centered design approach, realized through a set of initial brain-storming methods, a persona, a Laban-analysis of body language and a two-tiered evaluation method. eMoto is not a system that could have been designed from theory only, but require an iterative engagement with end-users, however, in combination with theoretical work. More specifically, we will show how we have managed to design an ambiguous and open system that allows for users’ emotional engagement

Building artificial personalities: expressive communication channels based on an interlingua for a human-robot dance

The development of artificial personalities requires that we develop a further understanding of how personality is communicated. This can be done through developing humanrobot interaction (HRI). In this paper we report on the development of the SpiderCrab robot. This uses an interlingua based on Laban Movement Analysis (LMA) to intermediate a human-robot dance. Specifically, we developed measurements to analyse data in real time from a simple vision system and implemented a simple stochastic dancing algorithm on a custom built robot. This shows how, through some simple rules, a personality can emerge by biasing random behaviour. The system was tested with professional dancers and members of the public and the results (formal and anecdotal) are presented herein

Automatic Recognition and Generation of Affective Movements

Body movements are an important non-verbal communication medium through which affective states of the demonstrator can be discerned. For machines, the capability to recognize affective expressions of their users and generate appropriate actuated responses with recognizable affective content has the potential to improve their life-like attributes and to create an engaging, entertaining, and empathic human-machine interaction. This thesis develops approaches to systematically identify movement features most salient to affective expressions and to exploit these features to design computational models for automatic recognition and generation of affective movements. The proposed approaches enable 1) identifying which features of movement convey affective expressions, 2) the automatic recognition of affective expressions from movements, 3) understanding the impact of kinematic embodiment on the perception of affective movements, and 4) adapting pre-defined motion paths in order to "overlay" specific affective content. Statistical learning and stochastic modeling approaches are leveraged, extended, and adapted to derive a concise representation of the movements that isolates movement features salient to affective expressions and enables efficient and accurate affective movement recognition and generation. In particular, the thesis presents two new approaches to fixed-length affective movement representation based on 1) functional feature transformation, and 2) stochastic feature transformation (Fisher scores). The resulting representations are then exploited for recognition of affective expressions in movements and for salient movement feature identification. For functional representation, the thesis adapts dimensionality reduction techniques (namely, principal component analysis (PCA), Fisher discriminant analysis, Isomap) for functional datasets and applies the resulting reduction techniques to extract a minimal set of features along which affect-specific movements are best separable. Furthermore, the centroids of affect-specific clusters of movements in the resulting functional PCA subspace along with the inverse mapping of functional PCA are used to generate prototypical movements for each affective expression. The functional discriminative modeling is however limited to cases where affect-specific movements also have similar kinematic trajectories and does not address the interpersonal and stochastic variations inherent to bodily expression of affect. To account for these variations, the thesis presents a novel affective movement representation in terms of stochastically-transformed features referred to as Fisher scores. The Fisher scores are derived from affect-specific hidden Markov model encoding of the movements and exploited to discriminate between different affective expressions using a support vector machine (SVM) classification. Furthermore, the thesis presents a new approach for systematic identification of a minimal set of movement features most salient to discriminating between different affective expressions. The salient features are identified by mapping Fisher scores to a low-dimensional subspace where dependencies between the movements and their affective labels are maximized. This is done by maximizing Hilbert Schmidt independence criterion between the Fisher score representation of movements and their affective labels. The resulting subspace forms a suitable basis for affective movement recognition using nearest neighbour classification and retains the high recognition rates achieved by SVM classification in the Fisher score space. The dimensions of the subspace form a minimal set of salient features and are used to explore the movement kinematic and dynamic cues that connote affective expressions. Furthermore, the thesis proposes the use of movement notation systems from the dance community (specifically, the Laban system) for abstract coding and computational analysis of movement. A quantification approach for Laban Effort and Shape is proposed and used to develop a new computational model for affective movement generation. Using the Laban Effort and Shape components, the proposed generation approach searches a labeled dataset for movements that are kinematically similar to a desired motion path and convey a target emotion. A hidden Markov model of the identified movements is obtained and used with the desired motion path in the Viterbi state estimation. The estimated state sequence is then used to generate a novel movement that is a version of the desired motion path, modulated to convey the target emotion. Various affective human movement corpora are used to evaluate and demonstrate the efficacy of the developed approaches for the automatic recognition and generation of affective expressions in movements. Finally, the thesis assesses the human perception of affective movements and the impact of display embodiment and the observer's gender on the affective movement perception via user studies in which participants rate the expressivity of synthetically-generated and human-generated affective movements animated on anthropomorphic and non-anthropomorphic embodiments. The user studies show that the human perception of affective movements is mainly shaped by intended emotions, and that the display embodiment and the observer's gender can significantly impact the perception of affective movements