Spatial peripheral interaction techniques for viewing and manipulating off-screen digital content

When an information space is larger than the display, it is typical for interfaces to only support interacting with content that is rendered within its viewport. To support interacting with off-screen content, our work explores the design and evaluation of several spatial off-screen exploration techniques that make use of the interaction space around the display. These include Paper Distortion, Dynamic Distortion, Dynamic Peephole Inset, Spatial Panning, and Point2Pan. We also contribute a formalized descriptive framework of the off-screen interaction space that divides the around-device space into interaction volumes and analyzes them based on different factors. This framework guided the design of an off-screen interaction system, called Off-Screen Desktop, which implemented our spatial techniques using consumer-level motion sensing hardware. To enable a more detailed analysis of spatial interaction systems, we also developed a web-based visualization system, called SpatialVis, that visualizes log data over a video screen capture of the associated user interface

The design-by-adaptation approach to universal access: learning from videogame technology

This paper proposes an alternative approach to the design of universally accessible interfaces to that provided by formal design frameworks applied ab initio to the development of new software. This approach, design-byadaptation, involves the transfer of interface technology and/or design principles from one application domain to another, in situations where the recipient domain is similar to the host domain in terms of modelled systems, tasks and users. Using the example of interaction in 3D virtual environments, the paper explores how principles underlying the design of videogame interfaces may be applied to a broad family of visualization and analysis software which handles geographical data (virtual geographic environments, or VGEs). One of the motivations behind the current study is that VGE technology lags some way behind videogame technology in the modelling of 3D environments, and has a less-developed track record in providing the variety of interaction methods needed to undertake varied tasks in 3D virtual worlds by users with varied levels of experience. The current analysis extracted a set of interaction principles from videogames which were used to devise a set of 3D task interfaces that have been implemented in a prototype VGE for formal evaluation

Interactive ubiquitous displays based on steerable projection

The ongoing miniaturization of computers and their embedding into the physical environment require new means of visual output. In the area of Ubiquitous Computing, flexible and adaptable display options are needed in order to enable the presentation of visual content in the physical environment. In this dissertation, we introduce the concepts of Display Continuum and Virtual Displays as new means of human-computer interaction. In this context, we present a realization of a Display Continuum based on steerable projection, and we describe a number of different interaction methods for manipulating this Display Continuum and the Virtual Displays placed on it.Mit zunehmender Miniaturisierung der Computer und ihrer Einbettung in der physikalischen Umgebung werden neue Arten der visuellen Ausgabe notwendig. Im Bereich des Ubiquitous Computing (Rechnerallgegenwart) werden flexible und anpassungsfähige Displays benötigt, um eine Anzeige von visuellen Inhalten unmittelbar in der physikalischen Umgebung zu ermöglichen. In dieser Dissertation führen wir das Konzept des Display-Kontinuums und der Virtuellen Displays als Instrument der Mensch-Maschine-Interaktion ein. In diesem Zusammenhang präsentieren wir eine mögliche Display-Kontinuum-Realisierung, die auf der Verwendung steuerbarer Projektion basiert, und wir beschreiben mehrere verschiedene Interaktionsmethoden, mit denen man das Display-Kontinuum und die darauf platzierten Virtuellen Displays steuern kann

The design-by-adaptation approach to universal access: learning from videogame technology

This paper proposes an alternative approach to the design of universally accessible interfaces to that provided by formal design frameworks applied ab initio to the development of new software. This approach, design-byadaptation, involves the transfer of interface technology and/or design principles from one application domain to another, in situations where the recipient domain is similar to the host domain in terms of modelled systems, tasks and users. Using the example of interaction in 3D virtual environments, the paper explores how principles underlying the design of videogame interfaces may be applied to a broad family of visualization and analysis software which handles geographical data (virtual geographic environments, or VGEs). One of the motivations behind the current study is that VGE technology lags some way behind videogame technology in the modelling of 3D environments, and has a less-developed track record in providing the variety of interaction methods needed to undertake varied tasks in 3D virtual worlds by users with varied levels of experience. The current analysis extracted a set of interaction principles from videogames which were used to devise a set of 3D task interfaces that have been implemented in a prototype VGE for formal evaluation

Investigating Data Exploration Techniques Involving Map Based Geotagged Data in a Collaborative Sensemaking Environment

The recent advancement in Global Positioning Systems (GPS) using satellite and geotagging has opened many opportunities for data-driven decision-making in fields such as emergency response, military intelligence, oil exploration and urban planning. The enormity and explosion of geospatial data necessitates the development of improved tools to support analysis and decision-making around this complex data – a process often known as sensemaking. A typical geotagged map can have hundreds of data points that are multi-dimensional, with each point having meaningful information associated with its location, as well as project specific information e.g., photographs, graphs, charts, bulletin data among many other information parameters. Sensemaking activities involving such complex data often involve a team of trained professionals who aim to make sense of this data to answer specific sets of questions, and make key decisions. Researchers are currently exploring the use of surface computing technology, such as, interactive digital tabletops and touch-based tablets to form methodologies to enhance collaborative sensemaking. This thesis examined the impact of two multi-surface interaction techniques that allowed individual group members to explore detailed geotagged data on separate peripheral tablets while sharing a large geographical overview on a digital tabletop. The two interaction techniques differed in the type of user input needed to control the location on the tabletop overview of a bounded “region of interest” (ROI) corresponding to the geotagged data displayed on the personal tablets. One technique (TOUCH) required the ROI to be positioned on the tabletop using direct touch interaction. The other technique (TILT) required the ROI to be positioned via 3-dimensional (up-down, left-right) tilt-gesture made with the personal tablet. Findings from the study revealed that the effectiveness of the respective interaction techniques depended on the stage of sensemaking process, and on which collaboration strategy groups employed during collaborative sensemaking

Sensor fusion with Gaussian processes

This thesis presents a new approach to multi-rate sensor fusion for (1) user matching and (2) position stabilisation and lag reduction. The Microsoft Kinect sensor and the inertial sensors in a mobile device are fused with a Gaussian Process (GP) prior method. We present a Gaussian Process prior model-based framework for multisensor data fusion and explore the use of this model for fusing mobile inertial sensors and an external position sensing device. The Gaussian Process prior model provides a principled mechanism for incorporating the low-sampling-rate position measurements and the high-sampling-rate derivatives in multi-rate sensor fusion, which takes account of the uncertainty of each sensor type. We explore the complementary properties of the Kinect sensor and the built-in inertial sensors in a mobile device and apply the GP framework for sensor fusion in the mobile human-computer interaction area. The Gaussian Process prior model-based sensor fusion is presented as a principled probabilistic approach to dealing with position uncertainty and the lag of the system, which are critical for indoor augmented reality (AR) and other location-aware sensing applications. The sensor fusion helps increase the stability of the position and reduce the lag. This is of great benefit for improving the usability of a human-computer interaction system. We develop two applications using the novel and improved GP prior model. (1) User matching and identification. We apply the GP model to identify individual users, by matching the observed Kinect skeletons with the sensed inertial data from their mobile devices. (2) Position stabilisation and lag reduction in a spatially aware display application for user performance improvement. We conduct a user study. Experimental results show the improved accuracy of target selection, and reduced delay from the sensor fusion system, allowing the users to acquire the target more rapidly, and with fewer errors in comparison with the Kinect filtered system. They also reported improved performance in subjective questions. The two applications can be combined seamlessly in a proxemic interaction system as identification of people and their positions in a room-sized environment plays a key role in proxemic interactions

An Evaluation Of Integrated Zooming and Scrolling On Small-Screens

Speed-dependent automatic zooming (SDAZ) has been proposed for standard desktop displays as a means of overcoming problems associated with the navigation of large information spaces. SDAZ combines zooming and panning facilities into a single operation, with the magnitude of both factors dependent on simple user interaction. Previous research indicated dramatic user performance improvements when using the technique for document and map tasks. In this paper we propose algorithmic extensions to the technique for application on small-screen devices and present a comparative experimental evaluation of user performance with the system and a normative scroll-zoom-pan interface. Users responded positively to the system, particularly in relation to reduced physical navigational workload. However, the reduced screen space reduced the impact of SDAZ in comparison to that reported in previous studies. In fact, for one-dimensional navigation (vertical document navigation) the normative interface out-performed SDAZ. For navigation in two dimensions (map browsing) SDAZ supports more accurate target location, but also produces longer task completion times. Some SDAZ users became lost within the information space and were unable to recover navigational context. We discuss the reasons for these observations and suggest ways in which limitations of SDAZ in the small-screen context may be overcome

Trajectory Prediction with Event-Based Cameras for Robotics Applications

This thesis presents the study, analysis, and implementation of a framework to perform trajectory prediction using an event-based camera for robotics applications. Event-based perception represents a novel computation paradigm based on unconventional sensing technology that holds promise for data acquisition, transmission, and processing at very low latency and power consumption, crucial in the future of robotics. An event-based camera, in particular, is a sensor that responds to light changes in the scene, producing an asynchronous and sparse output over a wide illumination dynamic range. They only capture relevant spatio-temporal information - mostly driven by motion - at high rate, avoiding the inherent redundancy in static areas of the field of view. For such reasons, this device represents a potential key tool for robots that must function in highly dynamic and/or rapidly changing scenarios, or where the optimisation of the resources is fundamental, like robots with on-board systems. Prediction skills are something humans rely on daily - even unconsciously - for instance when driving, playing sports, or collaborating with other people. In the same way, predicting the trajectory or the end-point of a moving target allows a robot to plan for appropriate actions and their timing in advance, interacting with it in many different manners. Moreover, prediction is also helpful for compensating robot internal delays in the perception-action chain, due for instance to limited sensors and/or actuators. The question I addressed in this work is whether event-based cameras are advantageous or not in trajectory prediction for robotics. In particular, if classical deep learning architecture used for this task can accommodate for event-based data, working asynchronously, and which benefit they can bring with respect to standard cameras. The a priori hypothesis is that being the sampling of the scene driven by motion, such a device would allow for more meaningful information acquisition, improving the prediction accuracy and processing data only when needed - without any information loss or redundant acquisition. To test the hypothesis, experiments are mostly carried out using the neuromorphic iCub, a custom version of the iCub humanoid platform that mounts two event-based cameras in the eyeballs, along with standard RGB cameras. To further motivate the work on iCub, a preliminary step is the evaluation of the robot's internal delays, a value that should be compensated by the prediction to interact in real-time with the object perceived. The first part of this thesis sees the implementation of the event-based framework for prediction, to answer the question if Long Short-Term Memory neural networks, the architecture used in this work, can be combined with event-based cameras. The task considered is the handover Human-Robot Interaction, during which the trajectory of the object in the human's hand must be inferred. Results show that the proposed pipeline can predict both spatial and temporal coordinates of the incoming trajectory with higher accuracy than model-based regression methods. Moreover, fast recovery from failure cases and adaptive prediction horizon behavior are exhibited. Successively, I questioned how much the event-based sampling approach can be convenient with respect to the classical fixed-rate approach. The test case used is the trajectory prediction of a bouncing ball, implemented with the pipeline previously introduced. A comparison between the two sampling methods is analysed in terms of error for different working rates, showing how the spatial sampling of the event-based approach allows to achieve lower error and also to adapt the computational load dynamically, depending on the motion in the scene. Results from both works prove that the merging of event-based data and Long Short-Term Memory networks looks promising for spatio-temporal features prediction in highly dynamic tasks, and paves the way to further studies about the temporal aspect and to a wide range of applications, not only robotics-related. Ongoing work is now focusing on the robot control side, finding the best way to exploit the spatio-temporal information provided by the predictor and defining the optimal robot behavior. Future work will see the shift of the full pipeline - prediction and robot control - to a spiking implementation. First steps in this direction have been already made thanks to a collaboration with a group from the University of Zurich, with which I propose a closed-loop motor controller implemented on a mixed-signal analog/digital neuromorphic processor, emulating a classical PID controller by means of spiking neural networks