An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices

In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.PostprintPeer reviewe

Ambient Gestures

We present Ambient Gestures, a novel gesture-based system designed to support ubiquitous ‘in the environment’ interactions with everyday computing technology. Hand gestures and audio feedback allow users to control computer applications without reliance on a graphical user interface, and without having to switch from the context of a non-computer task to the context of the computer. The Ambient Gestures system is composed of a vision recognition software application, a set of gestures to be processed by a scripting application and a navigation and selection application that is controlled by the gestures. This system allows us to explore gestures as the primary means of interaction within a multimodal, multimedia environment. In this paper we describe the Ambient Gestures system, define the gestures and the interactions that can be achieved in this environment and present a formative study of the system. We conclude with a discussion of our findings and future applications of Ambient Gestures in ubiquitous computing

Making touch-based kiosks accessible to blind users through simple gestures

Touch-based interaction is becoming increasingly popular and is commonly used as the main interaction paradigm for self-service kiosks in public spaces. Touch-based interaction is known to be visually intensive, and current non-haptic touch-display technologies are often criticized as excluding blind users. This study set out to demonstrate that touch-based kiosks can be designed to include blind users without compromising the user experience for non-blind users. Most touch-based kiosks are based on absolute positioned virtual buttons which are difficult to locate without any tactile, audible or visual cues. However, simple stroke gestures rely on relative movements and the user does not need to hit a target at a specific location on the display. In this study, a touch-based train ticket sales kiosk based on simple stroke gestures was developed and tested on a panel of blind and visually impaired users, a panel of blindfolded non-visually impaired users and a control group of non-visually impaired users. The tests demonstrate that all the participants managed to discover, learn and use the touch-based self-service terminal and complete a ticket purchasing task. The majority of the participants completed the task in less than 4 min on the first attempt

Surgical Applications of Compliant Mechanisms:A Review

Current surgical devices are mostly rigid and are made of stiff materials, even though their predominant use is on soft and wet tissues. With the emergence of compliant mechanisms (CMs), surgical tools can be designed to be flexible and made using soft materials. CMs offer many advantages such as monolithic fabrication, high precision, no wear, no friction, and no need for lubrication. It is therefore beneficial to consolidate the developments in this field and point to challenges ahead. With this objective, in this article, we review the application of CMs to surgical interventions. The scope of the review covers five aspects that are important in the development of surgical devices: (i) conceptual design and synthesis, (ii) analysis, (iii) materials, (iv) maim facturing, and (v) actuation. Furthermore, the surgical applications of CMs are assessed by classification into five major groups, namely, (i) grasping and cutting, (ii) reachability and steerability, (iii) transmission, (iv) sensing, and (v) implants and deployable devices. The scope and prospects of surgical devices using CMs are also discussed

Non-disruptive use of light fields in image and video processing

In the age of computational imaging, cameras capture not only an image but also data. This captured additional data can be best used for photo-realistic renderings facilitating numerous post-processing possibilities such as perspective shift, depth scaling, digital refocus, 3D reconstruction, and much more. In computational photography, the light field imaging technology captures the complete volumetric information of a scene. This technology has the highest potential to accelerate immersive experiences towards close-toreality. It has gained significance in both commercial and research domains. However, due to lack of coding and storage formats and also the incompatibility of the tools to process and enable the data, light fields are not exploited to its full potential. This dissertation approaches the integration of light field data to image and video processing. Towards this goal, the representation of light fields using advanced file formats designed for 2D image assemblies to facilitate asset re-usability and interoperability between applications and devices is addressed. The novel 5D light field acquisition and the on-going research on coding frameworks are presented. Multiple techniques for optimised sequencing of light field data are also proposed. As light fields contain complete 3D information of a scene, large amounts of data is captured and is highly redundant in nature. Hence, by pre-processing the data using the proposed approaches, excellent coding performance can be achieved.Im Zeitalter der computergestützten Bildgebung erfassen Kameras nicht mehr nur ein Bild, sondern vielmehr auch Daten. Diese erfassten Zusatzdaten lassen sich optimal für fotorealistische Renderings nutzen und erlauben zahlreiche Nachbearbeitungsmöglichkeiten, wie Perspektivwechsel, Tiefenskalierung, digitale Nachfokussierung, 3D-Rekonstruktion und vieles mehr. In der computergestützten Fotografie erfasst die Lichtfeld-Abbildungstechnologie die vollständige volumetrische Information einer Szene. Diese Technologie bietet dabei das größte Potenzial, immersive Erlebnisse zu mehr Realitätsnähe zu beschleunigen. Deshalb gewinnt sie sowohl im kommerziellen Sektor als auch im Forschungsbereich zunehmend an Bedeutung. Aufgrund fehlender Kompressions- und Speicherformate sowie der Inkompatibilität derWerkzeuge zur Verarbeitung und Freigabe der Daten, wird das Potenzial der Lichtfelder nicht voll ausgeschöpft. Diese Dissertation ermöglicht die Integration von Lichtfelddaten in die Bild- und Videoverarbeitung. Hierzu wird die Darstellung von Lichtfeldern mit Hilfe von fortschrittlichen für 2D-Bilder entwickelten Dateiformaten erarbeitet, um die Wiederverwendbarkeit von Assets- Dateien und die Kompatibilität zwischen Anwendungen und Geräten zu erleichtern. Die neuartige 5D-Lichtfeldaufnahme und die aktuelle Forschung an Kompressions-Rahmenbedingungen werden vorgestellt. Es werden zudem verschiedene Techniken für eine optimierte Sequenzierung von Lichtfelddaten vorgeschlagen. Da Lichtfelder die vollständige 3D-Information einer Szene beinhalten, wird eine große Menge an Daten, die in hohem Maße redundant sind, erfasst. Die hier vorgeschlagenen Ansätze zur Datenvorverarbeitung erreichen dabei eine ausgezeichnete Komprimierleistung

Predicting and Reducing the Impact of Errors in Character-Based Text Entry

This dissertation focuses on the effect of errors in character-based text entry techniques. The effect of errors is targeted from theoretical, behavioral, and practical standpoints. This document starts with a review of the existing literature. It then presents results of a user study that investigated the effect of different error correction conditions on popular text entry performance metrics. Results showed that the way errors are handled has a significant effect on all frequently used error metrics. The outcomes also provided an understanding of how users notice and correct errors. Building on this, the dissertation then presents a new high-level and method-agnostic model for predicting the cost of error correction with a given text entry technique. Unlike the existing models, it accounts for both human and system factors and is general enough to be used with most character-based techniques. A user study verified the model through measuring the effects of a faulty keyboard on text entry performance. Subsequently, the work then explores the potential user adaptation to a gesture recognizer’s misrecognitions in two user studies. Results revealed that users gradually adapt to misrecognition errors by replacing the erroneous gestures with alternative ones, if available. Also, users adapt to a frequently misrecognized gesture faster if it occurs more frequently than the other error-prone gestures. Finally, this work presents a new hybrid approach to simulate pressure detection on standard touchscreens. The new approach combines the existing touch-point- and time-based methods. Results of two user studies showed that it can simulate pressure detection more reliably for at least two pressure levels: regular (~1 N) and extra (~3 N). Then, a new pressure-based text entry technique is presented that does not require tapping outside the virtual keyboard to reject an incorrect or unwanted prediction. Instead, the technique requires users to apply extra pressure for the tap on the next target key. The performance of the new technique was compared with the conventional technique in a user study. Results showed that for inputting short English phrases with 10% non-dictionary words, the new technique increases entry speed by 9% and decreases error rates by 25%. Also, most users (83%) favor the new technique over the conventional one. Together, the research presented in this dissertation gives more insight into on how errors affect text entry and also presents improved text entry methods