Come, see and experience affective interactive art

The progress in the field of affective computing enables the realization of affective consumer products, affective games, and affective art. This paper describes the affective interactive art system Mood Swings, which interprets and visualizes affect expressed by a person. Mood Swings is founded on the integration of a framework for affective movements and a color model. This enables Mood Swings to recognize affective movement characteristics as expressed by a person and display a color that matches the expressed emotion. With that, a unique interactive system is introduced, which can be considered as art, a game, or a combination of both

Artificial intelligence's new frontier: artificial companions and the fourth revolution

‘The definitive version is available at www3.interscience.wiley.com '. Copyright Metaphilosophy LLC and Blackwell Publishing Ltd.In this paper I argue that recent technological transformations in the life-cycle of information have brought about a fourth revolution, in the long process of reassessing humanity’s fundamental nature and role in the universe. We are not immobile, at the centre of the universe (Copernicus); we are not unnaturally distinct and different from the rest of the animal world (Darwin); and we are far from being entirely transparent to ourselves (Freud). We are now slowly accepting the idea that we might be informational organisms among many agents (Turing), inforgs not so dramatically different from clever, engineered artefacts, but sharing with them a global environment that is ultimately made of information, the infosphere. This new conceptual revolution is humbling, but also exciting. For in view of this important evolution in our self-understanding, and given the sort of IT-mediated interactions that humans will increasingly enjoy with their environment and a variety of other agents, whether natural or synthetic, we have the unique opportunity of developing a new ecological approach to the whole of reality.Peer reviewe

As You Are, So Shall You Move Your Head: A System-Level Analysis between Head Movements and Corresponding Traits and Emotions

Identifying physical traits and emotions based on system-sensed physical activities is a challenging problem in the realm of human-computer interaction. Our work contributes in this context by investigating an underlying connection between head movements and corresponding traits and emotions. To do so, we utilize a head movement measuring device called eSense, which gives acceleration and rotation of a head. Here, first, we conduct a thorough study over head movement data collected from 46 persons using eSense while inducing five different emotional states over them in isolation. Our analysis reveals several new head movement based findings, which in turn, leads us to a novel unified solution for identifying different human traits and emotions through exploiting machine learning techniques over head movement data. Our analysis confirms that the proposed solution can result in high accuracy over the collected data. Accordingly, we develop an integrated unified solution for real-time emotion and trait identification using head movement data leveraging outcomes of our analysis.Comment: 9 pages, 7 figures, NSysS 201

Sentient Matter: Towards Affective Human-Architecture Interaction

Interactive design has been embedded into every aspect of our lives. Ranging from handy devices to architecturally scaled environments, these designs have not only shifted the way we facilitate interaction with other people, but they also actively reconfigure themselves in response to human stimuli. Following in the wake of interactive experimentation, sentient matter, the idea that matter embodies the capacity to perceive and respond to stimuli, attempts to engage in a challenging arena that few architects and architectural researchers have ventured into. In particular, the creation and simulation of emotive types of interaction between the architectural environment and its inhabitants. This ambition is made possible by the collaboration of multiple disciplines. Cybernetics, specifically the legacy of Pask’s conversation theory, inspires this thesis with the question of why emotion is needed in facilitating human–architecture communication; why emotion appraisal theory (P. Desmet) within psychology supports the feasibility of an architectural environment to elicit emotional changes on its participant as well as the possibility of generating a next-step response by having the participant’s emotive behaviors observed; and why movement notation systems, especially Laban Movement Analysis (a movement rating scale system), helps us to understand how emotions can be identified by motion elements that signify emotive behavior. Through the process of decomposing movement into several qualitative and quantitative factors such as velocity, openness, and smoothness, emotions embodied in motion can be detected and even manipulated by altering those movement factors. Moreover, with the employment of a Kinect sensor, live performance can be analyzed in real time. Based on the above research and inspired by the Kinetic sculptures of Margolin, the final product of this thesis is the development of a prototype that translates human movements that are expressive of emotion into continuous surface transformations, thus making evident how such emotive states might be transcoded into an architectural form. In this process, four typical emotive architectural expressions—joy, anger, excited, and sadness—are researched. This thesis also documents three virtual scenarios in order to examine the effect of this interactive system. Different contexts, kinetic types, and behavioral strategies are presented so that we may explore their potential applications. Sentient matter outlines a framework of syntheses, which is built upon the convergence of embedded computation (intelligence) and physical counterpart (kinetics). In the entire process, it considers people’s participation as materials that fuel the generation of legible emotional behaviors within an architectural environment. Consequently, there is potential for an architectural learning capacity coupled with an evolving data library of human behavioral knowledge. This opens doors for futuristic designs where the paradigm shifts from “What is that building?” to “What is that building doing?

Persona

Dementia shows us human existence without any decoration. We see it is heartbreaking, fragile, and delicate in all details. And we see more similarities than differences in our lives than we might imagine. We are all familiar with sadness, joy, fear, despair, depression, and happiness. People who have dementia feel the same way. Sadly, emotions confuse them and us. Formal and informal caregivers play a major role in caring for people who have dementia. These caregivers, however, frequently face great strain from care, stress, and have an increased risk of depression and anxiety. Their psychological distress is mostly caused by the shifting nature of dementia and its complexity. Despite the growing global impact, a lack of understanding of dementia leads to fears and to stigmatization. For those living with dementia (both the person and their family), the stigma gives rise to social isolation and to delays in looking for diagnosis and help. Therefore, there is an urgent need to raise awareness and understanding of dementia in all strata of society as a move towards enhancing the quality of life of people who have dementia and their caregivers and to adequately prepare formal and informal caregivers. ‘Persona’ is an artistic research project that adopts multiple design strategies to convey a better understanding of dementia to (in)formal caregivers and the public. Centered around scientific studies, and insights from primary caregivers, specialists, designers, and in collaboration with artists, this project aims to create an immersive experience to cultivate empathy, improve competence and alleviate psychological distress, and in doing so, humanize the disease and embrace the fragility of the human mind.Master of Design, Visual CommunicationMAV

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