Shaping Robot Gestures to Shape Users' Perception: the Effect of Amplitude and Speed on Godspeed Ratings

This work analyses the relationship between the way robots gesture and the way those gestures are perceived by human users. In particular, this work shows how modifying the amplitude and speed of a gesture affect the Godspeed scores given to those gestures, by means of an experiment involving 45 stimuli and 30 observers. The results suggest that shaping gestures aimed at manifesting the inner state of the robot (e.g., cheering or showing disappointment) tends to change the perception of Animacy (the dimension that accounts for how driven by endogenous factors the robot is perceived to be), while shaping gestures aimed at achieving an interaction effect (e.g., engaging and disengaging) tends to change the perception of Anthropomorphism, Likeability and Perceived Safety (the dimensions that account for the social aspects of the perception)

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Theories and Models of Teams and Groups

This article describes some of the theoretical approaches used by social scientists as well as those used by computer scientists to study the team and group phenomena. The purpose of this article is to identify ways in which these different fields can share and develop theoretical models and theoretical approaches, in an effort to gain a better understanding and further develop team and group research

Social Perception of Pedestrians and Virtual Agents Using Movement Features

In many tasks such as navigation in a shared space, humans explicitly or implicitly estimate social information related to the emotions, dominance, and friendliness of other humans around them. This social perception is critical in predicting others’ motions or actions and deciding how to interact with them. Therefore, modeling social perception is an important problem for robotics, autonomous vehicle navigation, and VR and AR applications. In this thesis, we present novel, data-driven models for the social perception of pedestrians and virtual agents based on their movement cues, including gaits, gestures, gazing, and trajectories. We use deep learning techniques (e.g., LSTMs) along with biomechanics to compute the gait features and combine them with local motion models to compute the trajectory features. Furthermore, we compute the gesture and gaze representations using psychological characteristics. We describe novel mappings between these computed gaits, gestures, gazing, and trajectory features and the various components (emotions, dominance, friendliness, approachability, and deception) of social perception. Our resulting data-driven models can identify the dominance, deception, and emotion of pedestrians from videos with an accuracy of more than 80%. We also release new datasets to evaluate these methods. We apply our data-driven models to socially-aware robot navigation and the navigation of autonomous vehicles among pedestrians. Our method generates robot movement based on pedestrians’ dominance levels, resulting in higher rapport and comfort. We also apply our data-driven models to simulate virtual agents with desired emotions, dominance, and friendliness. We perform user studies and show that our data-driven models significantly increase the user’s sense of social presence in VR and AR environments compared to the baseline methods.Doctor of Philosoph

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Designing a 3D Gestural Interface to Support User Interaction with Time-Oriented Data as Immersive 3D Radar Chart

The design of intuitive three-dimensional user interfaces is vital for interaction in virtual reality, allowing to effectively close the loop between a human user and the virtual environment. The utilization of 3D gestural input allows for useful hand interaction with virtual content by directly grasping visible objects, or through invisible gestural commands that are associated with corresponding features in the immersive 3D space. The design of such interfaces remains complex and challenging. In this article, we present a design approach for a three-dimensional user interface using 3D gestural input with the aim to facilitate user interaction within the context of Immersive Analytics. Based on a scenario of exploring time-oriented data in immersive virtual reality using 3D Radar Charts, we implemented a rich set of features that is closely aligned with relevant 3D interaction techniques, data analysis tasks, and aspects of hand posture comfort. We conducted an empirical evaluation (n=12), featuring a series of representative tasks to evaluate the developed user interface design prototype. The results, based on questionnaires, observations, and interviews, indicate good usability and an engaging user experience. We are able to reflect on the implemented hand-based grasping and gestural command techniques, identifying aspects for improvement in regard to hand detection and precision as well as emphasizing a prototype's ability to infer user intent for better prevention of unintentional gestures.Comment: 30 pages, 6 figures, 2 table

Haptic Media Scenes

The aim of this thesis is to apply new media phenomenological and enactive embodied cognition approaches to explain the role of haptic sensitivity and communication in personal computer environments for productivity. Prior theory has given little attention to the role of haptic senses in influencing cognitive processes, and do not frame the richness of haptic communication in interaction design—as haptic interactivity in HCI has historically tended to be designed and analyzed from a perspective on communication as transmissions, sending and receiving haptic signals. The haptic sense may not only mediate contact confirmation and affirmation, but also rich semiotic and affective messages—yet this is a strong contrast between this inherent ability of haptic perception, and current day support for such haptic communication interfaces. I therefore ask: How do the haptic senses (touch and proprioception) impact our cognitive faculty when mediated through digital and sensor technologies? How may these insights be employed in interface design to facilitate rich haptic communication? To answer these questions, I use theoretical close readings that embrace two research fields, new media phenomenology and enactive embodied cognition. The theoretical discussion is supported by neuroscientific evidence, and tested empirically through case studies centered on digital art. I use these insights to develop the concept of the haptic figura, an analytical tool to frame the communicative qualities of haptic media. The concept gauges rich machine- mediated haptic interactivity and communication in systems with a material solution supporting active haptic perception, and the mediation of semiotic and affective messages that are understood and felt. As such the concept may function as a design tool for developers, but also for media critics evaluating haptic media. The tool is used to frame a discussion on opportunities and shortcomings of haptic interfaces for productivity, differentiating between media systems for the hand and the full body. The significance of this investigation is demonstrating that haptic communication is an underutilized element in personal computer environments for productivity and providing an analytical framework for a more nuanced understanding of haptic communication as enabling the mediation of a range of semiotic and affective messages, beyond notification and confirmation interactivity

The computerization of archaeology: survey on AI techniques

This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; b) The analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; c) The cataloguing and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; d) The detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version