Augmenting User Interfaces with Haptic Feedback

Computer assistive technologies have developed considerably over the past decades. Advances in computer software and hardware have provided motion-impaired operators with much greater access to computer interfaces. For people with motion impairments, the main di�culty in the communication process is the input of data into the system. For example, the use of a mouse or a keyboard demands a high level of dexterity and accuracy. Traditional input devices are designed for able-bodied users and often do not meet the needs of someone with disabilities. As the key feature of most graphical user interfaces (GUIs) is to point-and-click with a cursor this can make a computer inaccessible for many people. Human-computer interaction (HCI) is an important area of research that aims to improve communication between humans and machines. Previous studies have identi�ed haptics as a useful method for improving computer access. However, traditional haptic techniques su�er from a number of shortcomings that have hindered their inclusion with real world software. The focus of this thesis is to develop haptic rendering algorithms that will permit motion-impaired operators to use haptic assistance with existing graphical user interfaces. The main goal is to improve interaction by reducing error rates and improving targeting times. A number of novel haptic assistive techniques are presented that utilise the three degrees-of-freedom (3DOF) capabilities of modern haptic devices to produce assistance that is designed speci�- cally for motion-impaired computer users. To evaluate the e�ectiveness of the new techniques a series of point-and-click experiments were undertaken in parallel with cursor analysis to compare the levels of performance. The task required the operator to produce a prede�ned sentence on the densely populated Windows on-screen keyboard (OSK). The results of the study prove that higher performance levels can be i ii achieved using techniques that are less constricting than traditional assistance

Longitudinal Study of Two Virtual Cursors for People With Motor Impairments: A Performance and Satisfaction Analysis on Web Navigation

The lack of dexterity in the upper limbs of people with motor impairments may prevent the use of standard pointing devices, such as mice, to access graphical user interfaces. In these cases, pointing and clicking are usually performed by means of alternative devices such as joysticks, trackballs or standard keyboards. However, target acquisition can still be challenging for this group of people due to their physical condition. Based on previous works, we developed two virtual cursors: the novel cross cursor and the standard area cursor. They are devoted to assist two different groups of users with link selection within web pages: keyboard-only users, and joystick and trackball users, respectively. Both virtual cursors have been evaluated and compared with the original unassisted cursor in a longitudinal study. Eight people with motor impairments participated in an unsupervised experiment from their own personal computers at home. For a period of six weeks, each participant used both a virtual cursor and the original unassisted cursor to freely navigate the Web, and to perform predefined target acquisition tasks. Interaction data was automatically logged throughout the study along with subjective assessments concerning the usability of the virtual cursor being tested. Results show significant improvements for both virtual cursors in six of the seven cursor parameters studied, albeit with performance variations between some participants. The virtual cursors were extensively used for free web navigation and in their subjective assessments both were positively endorsed by participants who also put forward improvement suggestions for future developments

物理/バーチャル空間の接続と分離を媒介する可動壁に関する研究

Tohoku University博士（情報科学）thesi

Assisted Interaction for Improving Web Accessibility: An Approach Driven and Tested by Userswith Disabilities

148 p.Un porcentaje cada vez mayor de la población mundial depende de la Web para trabajar, socializar, opara informarse entre otras muchas actividades. Los beneficios de la Web son todavía más cruciales paralas personas con discapacidades ya que les permite realizar un sinfín de tareas que en el mundo físico lesestán restringidas debido distintas barreras de accesibilidad. A pesar de sus ventajas, la mayoría depáginas web suelen ignoran las necesidades especiales de las personas con discapacidad, e incluyen undiseño único para todos los usuarios. Existen diversos métodos para combatir este problema, como porejemplo los sistemas de ¿transcoding¿, que transforman automáticamente páginas web inaccesibles enaccesibles. Para mejorar la accesibilidad web a grupos específicos de personas, estos métodos requiereninformación sobre las técnicas de adaptación más adecuadas que deben aplicarse.En esta tesis se han realizado una serie de estudios sobre la idoneidad de diversas técnicas de adaptaciónpara mejorar la navegación web para dos grupos diferentes de personas con discapacidad: personas conmovilidad reducida en miembros superiores y personas con baja visión. Basado en revisionesbibliográficas y estudios observacionales, se han desarrollado diferentes adaptaciones de interfaces web ytécnicas alternativas de interacción, que posteriormente han sido evaluadas a lo largo de varios estudioscon usuarios con necesidades especiales. Mediante análisis cualitativos y cuantitativos del rendimiento yla satisfacción de los participantes, se han evaluado diversas adaptaciones de interfaz y métodosalternativos de interacción. Los resultados han demostrado que las técnicas probadas mejoran el acceso ala Web y que los beneficios varían según la tecnología asistiva usada para acceder al ordenador

On the usefulness of off-the-shelf computer peripherals for people with Parkinson’s Disease

People who suffer from Parkinson’s Disease face many challenges using computers, and mice are particularly problematic input devices. This article describes usability tests of standard peripherals for use by people with Parkinson’s Disease in order to search for optimal combinations relative to the needs of this user group. The results are used to determine their effect upon inertia, muscle stiffness, tremor, pain, strain and coordination and show that widely available equipment could significantly improve mouse pointer control for many users. The results reflect the diversity of challenges experienced by computer users with Parkinson’s Disease, and also illustrate how projector-based technology may improve computer interaction without risking strain injuries

Fuzzy Mouse Cursor Control System for Computer Users with Spinal Cord Injuries

People with severe motor-impairments due to Spinal Cord Injury (SCI) or Spinal Cord Dysfunction (SCD), often experience difficulty with accurate and efficient control of pointing devices (Keates et al., 02). Usually this leads to their limited integration to society as well as limited unassisted control over the environment. The questions “How can someone with severe motor-impairments perform mouse pointer control as accurately and efficiently as an able-bodied person?” and “How can these interactions be advanced through use of Computational Intelligence (CI)?” are the driving forces behind the research described in this paper. Through this research, a novel fuzzy mouse cursor control system (FMCCS) is developed. The goal of this system is to simplify and improve efficiency of cursor control and its interactions on the computer screen by applying fuzzy logic in its decision-making to make disabled Internet users use the networked computer conveniently and easily. The FMCCS core consists of several fuzzy control functions, which define different user interactions with the system. The development of novel cursor control system is based on utilization of motor functions that are still available to most complete paraplegics, having capability of limited vision and breathing control. One of the biggest obstacles of developing human computer interfaces for disabled people focusing primarily on eyesight and breath control is user’s limited strength, stamina, and reaction time. Within the FMCCS developed in this research, these limitations are minimized through the use of a novel pneumatic input device and intelligent control algorithms for soft data analysis, fuzzy logic and user feedback assistance during operation. The new system is developed using a reliable and cheap sensory system and available computing techniques. Initial experiments with healthy and SCI subjects have clearly demonstrated benefits and promising performance of the new system: the FMCCS is accessible for people with severe SCI; it is adaptable to user specific capabilities and wishes; it is easy to learn and operate; point-to-point movement is responsive, precise and fast. The integrated sophisticated interaction features, good movement control without strain and clinical risks, as well the fact that quadriplegics, whose breathing is assisted by a respirator machine, still possess enough control to use the new system with ease, provide a promising framework for future FMCCS applications. The most motivating leverage for further FMCCS development is however, the positive feedback from persons who tested the first system prototype

Application of information theory and statistical learning to anomaly detection

In today\u27s highly networked world, computer intrusions and other attacks area constant threat. The detection of such attacks, especially attacks that are new or previously unknown, is important to secure networks and computers. A major focus of current research efforts in this area is on anomaly detection.;In this dissertation, we explore applications of information theory and statistical learning to anomaly detection. Specifically, we look at two difficult detection problems in network and system security, (1) detecting covert channels, and (2) determining if a user is a human or bot. We link both of these problems to entropy, a measure of randomness information content, or complexity, a concept that is central to information theory. The behavior of bots is low in entropy when tasks are rigidly repeated or high in entropy when behavior is pseudo-random. In contrast, human behavior is complex and medium in entropy. Similarly, covert channels either create regularity, resulting in low entropy, or encode extra information, resulting in high entropy. Meanwhile, legitimate traffic is characterized by complex interdependencies and moderate entropy. In addition, we utilize statistical learning algorithms, Bayesian learning, neural networks, and maximum likelihood estimation, in both modeling and detecting of covert channels and bots.;Our results using entropy and statistical learning techniques are excellent. By using entropy to detect covert channels, we detected three different covert timing channels that were not detected by previous detection methods. Then, using entropy and Bayesian learning to detect chat bots, we detected 100% of chat bots with a false positive rate of only 0.05% in over 1400 hours of chat traces. Lastly, using neural networks and the idea of human observational proofs to detect game bots, we detected 99.8% of game bots with no false positives in 95 hours of traces. Our work shows that a combination of entropy measures and statistical learning algorithms is a powerful and highly effective tool for anomaly detection

An investigation into gaze-based interaction techniques for people with motor impairments

The use of eye movements to interact with computers offers opportunities for people with impaired motor ability to overcome the difficulties they often face using hand-held input devices. Computer games have become a major form of entertainment, and also provide opportunities for social interaction in multi-player environments. Games are also being used increasingly in education to motivate and engage young people. It is important that young people with motor impairments are able to benefit from, and enjoy, them. This thesis describes a program of research conducted over a 20-year period starting in the early 1990's that has investigated interaction techniques based on gaze position intended for use by people with motor impairments. The work investigates how to make standard software applications accessible by gaze, so that no particular modification to the application is needed. The work divides into 3 phases. In the first phase, ways of using gaze to interact with the graphical user interfaces of office applications were investigated, designed around the limitations of gaze interaction. Of these, overcoming the inherent inaccuracies of pointing by gaze at on-screen targets was particularly important. In the second phase, the focus shifted from office applications towards immersive games and on-line virtual worlds. Different means of using gaze position and patterns of eye movements, or gaze gestures, to issue commands were studied. Most of the testing and evaluation studies in this, like the first, used participants without motor-impairments. The third phase of the work then studied the applicability of the research findings thus far to groups of people with motor impairments, and in particular,the means of adapting the interaction techniques to individual abilities. In summary, the research has shown that collections of specialised gaze-based interaction techniques can be built as an effective means of completing the tasks in specific types of games and how these can be adapted to the differing abilities of individuals with motor impairments

Visualization and Analysis Tools for Neuronal Tissue

The complex nature of neuronal cellular and circuit structure poses challenges for understanding tissue organization. New techniques in electron microscopy allow for large datasets to be acquired from serial sections of neuronal tissue. These techniques reveal all cells in an unbiased fashion, so their segmentation produces complex structures that must be inspected and analyzed. Although several software packages provide 3D representations of these structures, they are limited to monoscopic projection, and are tailored to the visualization of generic 3D data. On the other hand, stereoscopic display has been shown to improve the immersive experience, with significant gains in understanding spatial relationships and identifying important features. To leverage those benefits, we have developed a 3D immersive virtual reality data display system that besides presenting data visually allows augmenting and interacting with them in a form that facilitates human analysis.;To achieve a useful system for neuroscientists, we have developed the BrainTrek system, which is a suite of software applications suited for the organization, rendering, visualization, and modification of neuron model scenes. A middle cost point CAVE system provides high vertex count rendering of an immersive 3D environment. A standard head- and wand-tracking allows movement control and modification of the scene via the on-screen, 3D menu, while a tablet touch screen provides multiple navigation modes and a 2D menu. Graphic optimization provides theoretically limitless volumes to be presented and an on-screen mini-map allows users to quickly orientate themselves. A custom voice note-taking mechanism has been installed, allowing scenes to be described and revisited. Finally, ray-casting support allows numerous analytical features, including 3D distance and volume measurements, computation and presentation of statistics, and point-and-click retrieval and presentation of raw electron microscopy data. The extension of this system to the Unity3D platform provides a low-cost alternative to the CAVE. This allows users to visualize, explore, and annotate 3D cellular data in multiple platforms and modalities, ranging from different operating systems, different hardware platforms (e.g., tablets, PCs, or stereo head-mounted displays), to operating in an online or off-line fashion. Such approach has the potential to not only address visualization and analysis needs of neuroscientists, but also to become a tool for educational purposes, as well as for crowdsourcing upcoming needs for sheer amounts of neuronal data annotation

Detecting Abnormal Behavior in Web Applications

The rapid advance of web technologies has made the Web an essential part of our daily lives. However, network attacks have exploited vulnerabilities of web applications, and caused substantial damages to Internet users. Detecting network attacks is the first and important step in network security. A major branch in this area is anomaly detection. This dissertation concentrates on detecting abnormal behaviors in web applications by employing the following methodology. For a web application, we conduct a set of measurements to reveal the existence of abnormal behaviors in it. We observe the differences between normal and abnormal behaviors. By applying a variety of methods in information extraction, such as heuristics algorithms, machine learning, and information theory, we extract features useful for building a classification system to detect abnormal behaviors.;In particular, we have studied four detection problems in web security. The first is detecting unauthorized hotlinking behavior that plagues hosting servers on the Internet. We analyze a group of common hotlinking attacks and web resources targeted by them. Then we present an anti-hotlinking framework for protecting materials on hosting servers. The second problem is detecting aggressive behavior of automation on Twitter. Our work determines whether a Twitter user is human, bot or cyborg based on the degree of automation. We observe the differences among the three categories in terms of tweeting behavior, tweet content, and account properties. We propose a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Furthermore, we shift the detection perspective from automation to spam, and introduce the third problem, namely detecting social spam campaigns on Twitter. Evolved from individual spammers, spam campaigns manipulate and coordinate multiple accounts to spread spam on Twitter, and display some collective characteristics. We design an automatic classification system based on machine learning, and apply multiple features to classifying spam campaigns. Complementary to conventional spam detection methods, our work brings efficiency and robustness. Finally, we extend our detection research into the blogosphere to capture blog bots. In this problem, detecting the human presence is an effective defense against the automatic posting ability of blog bots. We introduce behavioral biometrics, mainly mouse and keyboard dynamics, to distinguish between human and bot. By passively monitoring user browsing activities, this detection method does not require any direct user participation, and improves the user experience