Fast Incremental Learning Strategy Driven by Confusion Reject for Online Handwriting Recognition

International audienceIn this paper, we present a new incremental learning strategy for handwritten character recognition systems. This learning strategy enables the recognition system to learn “rapidly” any new character from very few examples. The presented strategy is driven by a confusion detection mechanism in order to control the learning process. Artificial characters generation techniques are used to overcome the problem of lack of learning data when introducing a new character from unseen class. The results show that a good recognition rate (about 90%) is achieved after only 5 learning examples. Moreover, the rate quickly rises to 94% after 10 examples, and approximately 97% after 30 examples. A reduction of error of 40% is obtained by using the artificial characters generation techniques

On-Demand Myoelectric Control Using Wake Gestures to Eliminate False Activations During Activities of Daily Living

While myoelectric control has recently become a focus of increased research as a possible flexible hands-free input modality, current control approaches are prone to inadvertent false activations in real-world conditions. In this work, a novel myoelectric control paradigm -- on-demand myoelectric control -- is proposed, designed, and evaluated, to reduce the number of unrelated muscle movements that are incorrectly interpreted as input gestures . By leveraging the concept of wake gestures, users were able to switch between a dedicated control mode and a sleep mode, effectively eliminating inadvertent activations during activities of daily living (ADLs). The feasibility of wake gestures was demonstrated in this work through two online ubiquitous EMG control tasks with varying difficulty levels; dismissing an alarm and controlling a robot. The proposed control scheme was able to appropriately ignore almost all non-targeted muscular inputs during ADLs (>99.9%) while maintaining sufficient sensitivity for reliable mode switching during intentional wake gesture elicitation. These results highlight the potential of wake gestures as a critical step towards enabling ubiquitous myoelectric control-based on-demand input for a wide range of applications

WatchTrace: Design and Evaluation of an At-Your-Side Gesture Paradigm

In this thesis, we present the exploration and evaluation of a gesture interaction paradigm performed with arms at rest at the side of one's body. This gesture stance is informed persisting challenges in mid-air arm gesture interactions in relation to fatigue and social acceptability. The proposed arms-down posture reduces physical effort by minimizing the shoulder torque placed on the user. While this interaction posture has been previously explored, the gesture vocabulary in previous research has been small and limited. The design of this gesture interaction is motivated by the ability to provide rich and expressive input; the user performs gestures by moving the whole arm at the side of the body to create two-dimensional visual traces, as in hand-drawing in a bounded plane parallel to the ground. Within this space, we present the results of two studies that investigate the use of side-gesture input for interaction. First, we explore the users' mental model for using this interaction by conducting an elicitation study on a set of everyday tasks one would perform on a large display in public to semi-public contexts. The takeaway from this study presents the need for a dynamic and expressive set of gesture vocabulary including ideographic and alphanumeric gesture constructs that can be combined or chained together. We then explore the feasibility of designing such a gesture recognition system using commodity hardware and recognition techniques, dubbed WatchTrace, which supports alphanumeric gestures of up to length three, providing a vibrant, dynamic, and feasible gestural vocabulary. Finally, we explore potential approaches to improve the recognition through the use of adaptive thresholds, n-best lists, and changing reject rates among other conventional techniques in the field of gesture classification

Context-aware gestural interaction in the smart environments of the ubiquitous computing era

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyTechnology is becoming pervasive and the current interfaces are not adequate for the interaction with the smart environments of the ubiquitous computing era. Recently, researchers have started to address this issue introducing the concept of natural user interface, which is mainly based on gestural interactions. Many issues are still open in this emerging domain and, in particular, there is a lack of common guidelines for coherent implementation of gestural interfaces. This research investigates gestural interactions between humans and smart environments. It proposes a novel framework for the high-level organization of the context information. The framework is conceived to provide the support for a novel approach using functional gestures to reduce the gesture ambiguity and the number of gestures in taxonomies and improve the usability. In order to validate this framework, a proof-of-concept has been developed. A prototype has been developed by implementing a novel method for the view-invariant recognition of deictic and dynamic gestures. Tests have been conducted to assess the gesture recognition accuracy and the usability of the interfaces developed following the proposed framework. The results show that the method provides optimal gesture recognition from very different view-points whilst the usability tests have yielded high scores. Further investigation on the context information has been performed tackling the problem of user status. It is intended as human activity and a technique based on an innovative application of electromyography is proposed. The tests show that the proposed technique has achieved good activity recognition accuracy. The context is treated also as system status. In ubiquitous computing, the system can adopt different paradigms: wearable, environmental and pervasive. A novel paradigm, called synergistic paradigm, is presented combining the advantages of the wearable and environmental paradigms. Moreover, it augments the interaction possibilities of the user and ensures better gesture recognition accuracy than with the other paradigms

Enhancing Usability, Security, and Performance in Mobile Computing

We have witnessed the prevalence of smart devices in every aspect of human life. However, the ever-growing smart devices present significant challenges in terms of usability, security, and performance. First, we need to design new interfaces to improve the device usability which has been neglected during the rapid shift from hand-held mobile devices to wearables. Second, we need to protect smart devices with abundant private data against unauthorized users. Last, new applications with compute-intensive tasks demand the integration of emerging mobile backend infrastructure. This dissertation focuses on addressing these challenges. First, we present GlassGesture, a system that improves the usability of Google Glass through a head gesture user interface with gesture recognition and authentication. We accelerate the recognition by employing a novel similarity search scheme, and improve the authentication performance by applying new features of head movements in an ensemble learning method. as a result, GlassGesture achieves 96% gesture recognition accuracy. Furthermore, GlassGesture accepts authorized users in nearly 92% of trials, and rejects attackers in nearly 99% of trials. Next, we investigate the authentication between a smartphone and a paired smartwatch. We design and implement WearLock, a system that utilizes one\u27s smartwatch to unlock one\u27s smartphone via acoustic tones. We build an acoustic modem with sub-channel selection and adaptive modulation, which generates modulated acoustic signals to maximize the unlocking success rate against ambient noise. We leverage the motion similarities of the devices to eliminate unnecessary unlocking. We also offload heavy computation tasks from the smartwatch to the smartphone to shorten response time and save energy. The acoustic modem achieves a low bit error rate (BER) of 8%. Compared to traditional manual personal identification numbers (PINs) entry, WearLock not only automates the unlocking but also speeds it up by at least 18%. Last, we consider low-latency video analytics on mobile devices, leveraging emerging mobile backend infrastructure. We design and implement LAVEA, a system which offloads computation from mobile clients to edge nodes, to accomplish tasks with intensive computation at places closer to users in a timely manner. We formulate an optimization problem for offloading task selection and prioritize offloading requests received at the edge node to minimize the response time. We design and compare various task placement schemes for inter-edge collaboration to further improve the overall response time. Our results show that the client-edge configuration has a speedup ranging from 1.3x to 4x against running solely by the client and 1.2x to 1.7x against the client-cloud configuration

Design and recognition of microgestures for always-available input

Gestural user interfaces for computing devices most commonly require the user to have at least one hand free to interact with the device, for example, moving a mouse, touching a screen, or performing mid-air gestures. Consequently, users find it difficult to operate computing devices while holding or manipulating everyday objects. This limits the users from interacting with the digital world during a significant portion of their everyday activities, such as, using tools in the kitchen or workshop, carrying items, or workout with sports equipment. This thesis pushes the boundaries towards the bigger goal of enabling always-available input. Microgestures have been recognized for their potential to facilitate direct and subtle interactions. However, it remains an open question how to interact using gestures with computing devices when both of the user’s hands are occupied holding everyday objects. We take a holistic approach and focus on three core contributions: i) To understand end-users preferences, we present an empirical analysis of users’ choice of microgestures when holding objects of diverse geometries. Instead of designing a gesture set for a specific object or geometry and to identify gestures that generalize, this thesis leverages the taxonomy of grasp types established from prior research. ii) We tackle the critical problem of avoiding false activation by introducing a novel gestural input concept that leverages a single-finger movement, which stands out from everyday finger motions during holding and manipulating objects. Through a data-driven approach, we also systematically validate the concept’s robustness with different everyday actions. iii) While full sensor coverage on the user’s hand would allow detailed hand-object interaction, minimal instrumentation is desirable for real-world use. This thesis addresses the problem of identifying sparse sensor layouts. We present the first rapid computational method, along with a GUI-based design tool that enables iterative design based on the designer’s high-level requirements. Furthermore, we demonstrate that minimal form-factor devices, like smart rings, can be used to effectively detect microgestures in hands-free and busy scenarios. Overall, the presented findings will serve as both conceptual and technical foundations for enabling interaction with computing devices wherever and whenever users need them.Benutzerschnittstellen für Computergeräte auf Basis von Gesten erfordern für eine Interaktion meist mindestens eine freie Hand, z.B. um eine Maus zu bewegen, einen Bildschirm zu berühren oder Gesten in der Luft auszuführen. Daher ist es für Nutzer schwierig, Geräte zu bedienen, während sie Gegenstände halten oder manipulieren. Dies schränkt die Interaktion mit der digitalen Welt während eines Großteils ihrer alltäglichen Aktivitäten ein, etwa wenn sie Küchengeräte oder Werkzeug verwenden, Gegenstände tragen oder mit Sportgeräten trainieren. Diese Arbeit erforscht neue Wege in Richtung des größeren Ziels, immer verfügbare Eingaben zu ermöglichen. Das Potential von Mikrogesten für die Erleichterung von direkten und feinen Interaktionen wurde bereits erkannt. Die Frage, wie der Nutzer mit Geräten interagiert, wenn beide Hände mit dem Halten von Gegenständen belegt sind, bleibt jedoch offen. Wir verfolgen einen ganzheitlichen Ansatz und konzentrieren uns auf drei Kernbeiträge: i) Um die Präferenzen der Endnutzer zu verstehen, präsentieren wir eine empirische Analyse der Wahl von Mikrogesten beim Halten von Objekte mit diversen Geometrien. Anstatt einen Satz an Gesten für ein bestimmtes Objekt oder eine bestimmte Geometrie zu entwerfen, nutzt diese Arbeit die aus früheren Forschungen stammenden Taxonomien an Griff-Typen. ii) Wir adressieren das Problem falscher Aktivierungen durch ein neuartiges Eingabekonzept, das die sich von alltäglichen Fingerbewegungen abhebende Bewegung eines einzelnen Fingers nutzt. Durch einen datengesteuerten Ansatz validieren wir zudem systematisch die Robustheit des Konzepts bei diversen alltäglichen Aktionen. iii) Auch wenn eine vollständige Sensorabdeckung an der Hand des Nutzers eine detaillierte Hand-Objekt-Interaktion ermöglichen würde, ist eine minimale Ausstattung für den Einsatz in der realen Welt wünschenswert. Diese Arbeit befasst sich mit der Identifizierung reduzierter Sensoranordnungen. Wir präsentieren die erste, schnelle Berechnungsmethode in einem GUI-basierten Designtool, das iteratives Design basierend auf den Anforderungen des Designers ermöglicht. Wir zeigen zudem, dass Geräte mit minimalem Formfaktor wie smarte Ringe für die Erkennung von Mikrogesten verwendet werden können. Insgesamt dienen die vorgestellten Ergebnisse sowohl als konzeptionelle als auch als technische Grundlage für die Realisierung von Interaktion mit Computergeräten wo und wann immer Nutzer sie benötigen.Bosch Researc

AUGMENTED TOUCH INTERACTIONS WITH FINGER CONTACT SHAPE AND ORIENTATION

Touchscreen interactions are far less expressive than the range of touch that human hands are capable of - even considering technologies such as multi-touch and force-sensitive surfaces. Recently, some touchscreens have added the capability to sense the actual contact area of a finger on the touch surface, which provides additional degrees of freedom - the size and shape of the touch, and the finger's orientation. These additional sensory capabilities hold promise for increasing the expressiveness of touch interactions - but little is known about whether users can successfully use the new degrees of freedom. To provide this baseline information, we carried out a study with a finger-contact-sensing touchscreen, and asked participants to produce a range of touches and gestures with different shapes and orientations, with both one and two fingers. We found that people are able to reliably produce two touch shapes and three orientations across a wide range of touches and gestures - a result that was confirmed in another study that used the augmented touches for a screen lock application

Design and Evaluation of a Presentation Maestro: Controlling Electronic Presentations Through Gesture

Gesture-based interaction has long been seen as a natural means of input for electronic presentation systems; however, gesture-based presentation systems have not been evaluated in real-world contexts, and the implications of this interaction modality are not known. This thesis describes the design and evaluation of Maestro, a gesture-based presentation system which was developed to explore these issues. This work is presented in two parts. The first part describes Maestro's design, which was informed by a small observational study of people giving talks; and Maestro's evaluation, which involved a two week field study where Maestro was used for lecturing to a class of approximately 100 students. The observational study revealed that presenters regularly gesture towards the content of their slides. As such, Maestro supports several gestures which operate directly on slide content (e.g., pointing to a bullet causes it to be highlighted). The field study confirmed that audience members value these content-centric gestures. Conversely, the use of gestures for navigating slides is perceived to be less efficient than the use of a remote. Additionally, gestural input was found to result in a number of unexpected side effects which may hamper the presenter's ability to fully engage the audience. The second part of the thesis presents a gesture recognizer based on discrete hidden Markov models (DHMMs). Here, the contributions lie in presenting a feature set and a factorization of the standard DHMM observation distribution, which allows modeling of a wide range of gestures (e.g., both one-handed and bimanual gestures), but which uses few modeling parameters. To establish the overall robustness and accuracy of the recognition system, five new users and one expert were asked to perform ten instances of each gesture. The system accurately recognized 85% of gestures for new users, increasing to 96% for the expert user. In both cases, false positives accounted for fewer than 4% of all detections. These error rates compare favourably to those of similar systems

Multimodal Shared-Control Interaction for Mobile Robots in AAL Environments

This dissertation investigates the design, development and implementation of cognitively adequate, safe and robust, spatially-related, multimodal interaction between human operators and mobile robots in Ambient Assisted Living environments both from the theoretical and practical perspectives. By focusing on different aspects of the concept Interaction, the essential contribution of this dissertation is divided into three main research packages; namely, Formal Interaction, Spatial Interaction and Multimodal Interaction in AAL. As the principle package, in Formal Interaction, research effort is dedicated to developing a formal language based interaction modelling and management solution process and a unified dialogue modelling approach. This package aims to enable a robust, flexible, and context-sensitive, yet formally controllable and tractable interaction. This type of interaction can be used to support the interaction management of any complex interactive systems, including the ones covered in the other two research packages. In the second research package, Spatial Interaction, a general qualitative spatial knowledge based multi-level conceptual model is developed and proposed. The goal is to support a spatially-related interaction in human-robot collaborative navigation. With a model-based computational framework, the proposed conceptual model has been implemented and integrated into a practical interactive system which has been evaluated by empirical studies. It has been particularly tested with respect to a set of high-level and model-based conceptual strategies for resolving the frequent spatially-related communication problems in human-robot interaction. Last but not least, in Multimodal Interaction in AAL, attention is drawn to design, development and implementation of multimodal interaction for elderly persons. In this elderly-friendly scenario, ageing-related characteristics are carefully considered for an effective and efficient interaction. Moreover, a standard model based empirical framework for evaluating multimodal interaction is provided. This framework was especially applied to evaluate a minutely developed and systematically improved elderly-friendly multimodal interactive system through a series of empirical studies with groups of elderly persons