Face and Body gesture recognition for a vision-based multimodal analyser

users, computers should be able to recognize emotions, by analyzing the human's affective state, physiology and behavior. In this paper, we present a survey of research conducted on face and body gesture and recognition. In order to make human-computer interfaces truly natural, we need to develop technology that tracks human movement, body behavior and facial expression, and interprets these movements in an affective way. Accordingly in this paper, we present a framework for a vision-based multimodal analyzer that combines face and body gesture and further discuss relevant issues

Move, hold and touch: A framework for Tangible gesture interactive systems

© 2015 by the authors. Technology is spreading in our everyday world, and digital interaction beyond the screen, with real objects, allows taking advantage of our natural manipulative and communicative skills. Tangible gesture interaction takes advantage of these skills by bridging two popular domains in Human-Computer Interaction, tangible interaction and gestural interaction. In this paper, we present the Tangible Gesture Interaction Framework (TGIF) for classifying and guiding works in this field. We propose a classification of gestures according to three relationships with objects: move, hold and touch. Following this classification, we analyzed previous work in the literature to obtain guidelines and common practices for designing and building new tangible gesture interactive systems. We describe four interactive systems as application examples of the TGIF guidelines and we discuss the descriptive, evaluative and generative power of TGIF

A Framework For Abstracting, Designing And Building Tangible Gesture Interactive Systems

This thesis discusses tangible gesture interaction, a novel paradigm for interacting with computer that blends concepts from the more popular fields of tangible interaction and gesture interaction. Taking advantage of the human innate abilities to manipulate physical objects and to communicate through gestures, tangible gesture interaction is particularly interesting for interacting in smart environments, bringing the interaction with computer beyond the screen, back to the real world. Since tangible gesture interaction is a relatively new field of research, this thesis presents a conceptual framework that aims at supporting future work in this field. The Tangible Gesture Interaction Framework provides support on three levels. First, it helps reflecting from a theoretical point of view on the different types of tangible gestures that can be designed, physically, through a taxonomy based on three components (move, hold and touch) and additional attributes, and semantically, through a taxonomy of the semantic constructs that can be used to associate meaning to tangible gestures. Second, it helps conceiving new tangible gesture interactive systems and designing new interactions based on gestures with objects, through dedicated guidelines for tangible gesture definition and common practices for different application domains. Third, it helps building new tangible gesture interactive systems supporting the choice between four different technological approaches (embedded and embodied, wearable, environmental or hybrid) and providing general guidance for the different approaches. As an application of this framework, this thesis presents also seven tangible gesture interactive systems for three different application domains, i.e., interacting with the In-Vehicle Infotainment System (IVIS) of the car, the emotional and interpersonal communication, and the interaction in a smart home. For the first application domain, four different systems that use gestures on the steering wheel as interaction means with the IVIS have been designed, developed and evaluated. For the second application domain, an anthropomorphic lamp able to recognize gestures that humans typically perform for interpersonal communication has been conceived and developed. A second system, based on smart t-shirts, recognizes when two people hug and reward the gesture with an exchange of digital information. Finally, a smart watch for recognizing gestures performed with objects held in the hand in the context of the smart home has been investigated. The analysis of existing systems found in literature and of the system developed during this thesis shows that the framework has a good descriptive and evaluative power. The applications developed during this thesis show that the proposed framework has also a good generative power.Questa tesi discute l’interazione gestuale tangibile, un nuovo paradigma per interagire con il computer che unisce i principi dei più comuni campi di studio dell’interazione tangibile e dell’interazione gestuale. Sfruttando le abilità innate dell’uomo di manipolare oggetti fisici e di comunicare con i gesti, l’interazione gestuale tangibile si rivela particolarmente interessante per interagire negli ambienti intelligenti, riportando l’attenzione sul nostro mondo reale, al di là dello schermo dei computer o degli smartphone. Poiché l’interazione gestuale tangibile è un campo di studio relativamente recente, questa tesi presenta un framework (quadro teorico) che ha lo scopo di assistere lavori futuri in questo campo. Il Framework per l’Interazione Gestuale Tangibile fornisce supporto su tre livelli. Per prima cosa, aiuta a riflettere da un punto di vista teorico sui diversi tipi di gesti tangibili che possono essere eseguiti fisicamente, grazie a una tassonomia basata su tre componenti (muovere, tenere, toccare) e attributi addizionali, e che possono essere concepiti semanticamente, grazie a una tassonomia di tutti i costrutti semantici che permettono di associare dei significati ai gesti tangibili. In secondo luogo, il framework proposto aiuta a concepire nuovi sistemi interattivi basati su gesti tangibili e a ideare nuove interazioni basate su gesti con gli oggetti, attraverso linee guida per la definizione di gesti tangibili e una selezione delle migliore pratiche per i differenti campi di applicazione. Infine, il framework aiuta a implementare nuovi sistemi interattivi basati su gesti tangibili, permettendo di scegliere tra quattro differenti approcci tecnologici (incarnato e integrato negli oggetti, indossabile, distribuito nell’ambiente, o ibrido) e fornendo una guida generale per la scelta tra questi differenti approcci. Come applicazione di questo framework, questa tesi presenta anche sette sistemi interattivi basati su gesti tangibili, realizzati per tre differenti campi di applicazione: l’interazione con i sistemi di infotainment degli autoveicoli, la comunicazione interpersonale delle emozioni, e l’interazione nella casa intelligente. Per il primo campo di applicazione, sono stati progettati, sviluppati e testati quattro differenti sistemi che usano gesti tangibili effettuati sul volante come modalità di interazione con il sistema di infotainment. Per il secondo campo di applicazione, è stata concepita e sviluppata una lampada antropomorfica in grado di riconoscere i gesti tipici dell’interazione interpersonale. Per lo stesso campo di applicazione, un secondo sistema, basato su una maglietta intelligente, riconosce quando due persone si abbracciano e ricompensa questo gesto con uno scambio di informazioni digitali. Infine, per l’interazione nella casa intelligente, è stata investigata la realizzazione di uno smart watch per il riconoscimento di gesti eseguiti con oggetti tenuti nella mano. L’analisi dei sistemi interattivi esistenti basati su gesti tangibili permette di dimostrare che il framework ha un buon potere descrittivo e valutativo. Le applicazioni sviluppate durante la tesi mostrano che il framework proposto ha anche un valido potere generativo

Spatially Aware Computing for Natural Interaction

Spatial information refers to the location of an object in a physical or digital world. Besides, it also includes the relative position of an object related to other objects around it. In this dissertation, three systems are designed and developed. All of them apply spatial information in different fields. The ultimate goal is to increase the user friendliness and efficiency in those applications by utilizing spatial information. The first system is a novel Web page data extraction application, which takes advantage of 2D spatial information to discover structured records from a Web page. The extracted information is useful to re-organize the layout of a Web page to fit mobile browsing. The second application utilizes the 3D spatial information of a mobile device within a large paper-based workspace to implement interactive paper that combines the merits of paper documents and mobile devices. This application can overlay digital information on top of a paper document based on the location of a mobile device within a workspace. The third application further integrates 3D space information with sound detection to realize an automatic camera management system. This application automatically controls multiple cameras in a conference room, and creates an engaging video by intelligently switching camera shots among meeting participants based on their activities. Evaluations have been made on all three applications, and the results are promising. In summary, this dissertation comprehensively explores the usage of spatial information in various applications to improve the usability

Symbiotic interaction between humans and robot swarms

Comprising of a potentially large team of autonomous cooperative robots locally interacting and communicating with each other, robot swarms provide a natural diversity of parallel and distributed functionalities, high flexibility, potential for redundancy, and fault-tolerance. The use of autonomous mobile robots is expected to increase in the future and swarm robotic systems are envisioned to play important roles in tasks such as: search and rescue (SAR) missions, transportation of objects, surveillance, and reconnaissance operations. To robustly deploy robot swarms on the field with humans, this research addresses the fundamental problems in the relatively new field of human-swarm interaction (HSI). Four groups of core classes of problems have been addressed for proximal interaction between humans and robot swarms: interaction and communication; swarm-level sensing and classification; swarm coordination; swarm-level learning. The primary contribution of this research aims to develop a bidirectional human-swarm communication system for non-verbal interaction between humans and heterogeneous robot swarms. The guiding field of application are SAR missions. The core challenges and issues in HSI include: How can human operators interact and communicate with robot swarms? Which interaction modalities can be used by humans? How can human operators instruct and command robots from a swarm? Which mechanisms can be used by robot swarms to convey feedback to human operators? Which type of feedback can swarms convey to humans? In this research, to start answering these questions, hand gestures have been chosen as the interaction modality for humans, since gestures are simple to use, easily recognized, and possess spatial-addressing properties. To facilitate bidirectional interaction and communication, a dialogue-based interaction system is introduced which consists of: (i) a grammar-based gesture language with a vocabulary of non-verbal commands that allows humans to efficiently provide mission instructions to swarms, and (ii) a swarm coordinated multi-modal feedback language that enables robot swarms to robustly convey swarm-level decisions, status, and intentions to humans using multiple individual and group modalities. The gesture language allows humans to: select and address single and multiple robots from a swarm, provide commands to perform tasks, specify spatial directions and application-specific parameters, and build iconic grammar-based sentences by combining individual gesture commands. Swarms convey different types of multi-modal feedback to humans using on-board lights, sounds, and locally coordinated robot movements. The swarm-to-human feedback: conveys to humans the swarm's understanding of the recognized commands, allows swarms to assess their decisions (i.e., to correct mistakes: made by humans in providing instructions, and errors made by swarms in recognizing commands), and guides humans through the interaction process. The second contribution of this research addresses swarm-level sensing and classification: How can robot swarms collectively sense and recognize hand gestures given as visual signals by humans? Distributed sensing, cooperative recognition, and decision-making mechanisms have been developed to allow robot swarms to collectively recognize visual instructions and commands given by humans in the form of gestures. These mechanisms rely on decentralized data fusion strategies and multi-hop messaging passing algorithms to robustly build swarm-level consensus decisions. Measures have been introduced in the cooperative recognition protocol which provide a trade-off between the accuracy of swarm-level consensus decisions and the time taken to build swarm decisions. The third contribution of this research addresses swarm-level cooperation: How can humans select spatially distributed robots from a swarm and the robots understand that they have been selected? How can robot swarms be spatially deployed for proximal interaction with humans? With the introduction of spatially-addressed instructions (pointing gestures) humans can robustly address and select spatially- situated individuals and groups of robots from a swarm. A cascaded classification scheme is adopted in which, first the robot swarm identifies the selection command (e.g., individual or group selection), and then the robots coordinate with each other to identify if they have been selected. To obtain better views of gestures issued by humans, distributed mobility strategies have been introduced for the coordinated deployment of heterogeneous robot swarms (i.e., ground and flying robots) and to reshape the spatial distribution of swarms. The fourth contribution of this research addresses the notion of collective learning in robot swarms. The questions that are answered include: How can robot swarms learn about the hand gestures given by human operators? How can humans be included in the loop of swarm learning? How can robot swarms cooperatively learn as a team? Online incremental learning algorithms have been developed which allow robot swarms to learn individual gestures and grammar-based gesture sentences supervised by human instructors in real-time. Humans provide different types of feedback (i.e., full or partial feedback) to swarms for improving swarm-level learning. To speed up the learning rate of robot swarms, cooperative learning strategies have been introduced which enable individual robots in a swarm to intelligently select locally sensed information and share (exchange) selected information with other robots in the swarm. The final contribution is a systemic one, it aims on building a complete HSI system towards potential use in real-world applications, by integrating the algorithms, techniques, mechanisms, and strategies discussed in the contributions above. The effectiveness of the global HSI system is demonstrated in the context of a number of interactive scenarios using emulation tests (i.e., performing simulations using gesture images acquired by a heterogeneous robotic swarm) and by performing experiments with real robots using both ground and flying robots