Today, advances in wireless sensor networks are making it possible to capture large amounts of information about a person and their interaction within their home environment. However, what is missing is how to ensure the security of the collected data and its use to alter human behavior for positive benefit. In this research, exploration was conducted involving the "infrastructure" and "intelligence" aspects of a wireless sensor network through a Behavior Modification Sensor System. First was to understand how a secure wireless sensor network could be established through the symmetric distribution of keys (the securing of the infrastructure), and it involves the mathematical analysis of a novel key pre-distribution scheme. Second explores via field testing the "intelligence" level of the system. This was meant to support the generation of persuasive messages built from the integration of a person's physiological and living pattern data in persuading physical activity behavior change associated with daily walking steps. This system was used by an elderly female in a three-month study. Findings regarding the "infrastructure" or the novel key pre-distribution scheme in comparison to three popular key distribution methods indicates that it offers greater network resiliency to security threats (i.e., 1/2^32 times lower), better memory utilization (i.e., 53.9% less), but higher energy consumption (i.e., 2% higher) than its comparison group. Findings from the "intelligence" level of the research posit that using a person's physiological and living pattern data may allow for more "information rich" and stronger persuasive messages. Findings indicate that the study participant was able to change and improve her average daily walking steps by 61% over a pre-treatment period. As the study participant increased her physical activity, changes in her living pattern were also observed (e.g., time spent watching television decreased while time spent engaged in walking increased by an average of 15 minutes per day). Reinforcement of these findings were noted between a pre and post-study survey that indicated the study participant moved from a contemplation stage of change where physical activity engagement was intended but not acted upon to an action stage of change where physical activity engagement dominated the new behavior.

Selecting one color can be done with relative ease, but this task becomes more difficult with each subsequent color. Colormoo is an online tool aimed at solving this problem. We implement three algorithms for generating color palettes based off of a starting color. Data is collected for each palette that is generated. Our analysis reveals two of the algorithms are preferred, but under different circumstances. Furthermore, we find that users prefer palettes containing colors that are compatible, but not too similar. With refined heuristics, we believe these techniques can be extended and applied beyond the field of graphic design alone.

Eye-tracking devices can tell us where on the screen a person is looking. Researchers frequently analyze eye-tracking data manually, by examining every frame of a visual stimulus used in an eye-tracking experiment so as to match 2D screen-coordinates provided by the eye-tracker to related objects and content within the stimulus. Such task requires significant manual effort and is not feasible for analyzing data collected from many users, long experimental sessions, and heavily interactive and dynamic visual stimuli. In this dissertation, we present a novel analysis method. We would instrument visualizations that have open source code, and leverage real-time information about the layout of the rendered visual content, to automatically relate gaze-samples to visual objects drawn on the screen. Since such visual objects are shown in a visualization stand for data, the method would allow us to necessarily detect data that users focus on or Data of Interest (DOI). This dissertation has two contributions. First, we demonstrated the feasibility of collecting DOI data for real life visualization in a reliable way which is not self-evident. Second, we formalized the process of collecting and interpreting DOI data and test whether the automated DOI detection can lead to research workflows, and insights not possible with traditional, manual approaches.

Physiotherapy patients, victims of accidents, strokes and injuries are submitted for several months of repetitive exercise in rehabilitation sessions, depending almost entirely of aid and monitoring of the physical therapist. The performed exercises should be supervised and evaluated regularly in order to measure the patient's progress in treatment. The evolution of new interaction technologies with the patient, through low-cost motion detection sensors, enables the creation for physical therapy support software. Furthermore, the introduction of Exergames and BrainTraining games in the fields of medicine and physical therapy has shown that serious games can be used to assist the treatment of diseases and patients in special conditions. However, few studies demonstrate the importance or relevant results regarding the usability of solutions. This paper presents the development and evaluation of a gaming system to assist physiotherapists in quality analysis and workout efficiency. The evaluation through specific questionnaires showed the quality of the developed system on the usability and functionality of the games. Also from the final collected data, it is presented an ideal standard for players movement data, that can be used as a reference during treatment of patients. / Pacientes de fisioterapia, vítimas de acidentes, derrames e lesões são submetidos por vários meses a sessões repetitivas de exercícios para reabilitação, dependendo quase que totalmente do auxílio e acompanhamento do fisioterapeuta. Os exercícios realizados devem ser supervisionados e avaliados regularmente, com o intuito de medir o progresso do paciente no tratamento. A evolução de novas tecnologias de interação com o paciente, através de sensores de detecção de movimentos de baixo custo, possibilita a criação de softwares de apoio ao tratamento fisioterapêutico. Além disso, a introdução de Exergames e BrainTraining games nos campos da medicina e fisioterapia tem demonstrado que é possível utilizar jogos sérios para auxiliar nos tratamentos de doenças e condições especiais dos pacientes. Contudo, poucos estudos demonstram a importância ou resultados relevantes quanto à usabilidade das soluções. Este trabalho apresenta o desenvolvimento e avaliação de um sistema de jogos para auxiliar fisioterapeutas na análise da qualidade e eficiência da sessão de exercícios. A avaliação realizada através de questionários específicos demonstrou a qualidade do sistema desenvolvido quanto à usabilidade e funcionalidade dos jogos apresentados. Também a partir dos dados finais coletados, apresenta-se um padrão ideal de dados para os movimentos dos jogadores, que pode ser usado como referência durante o tratamento de pacientes.

Gesture recognition is becoming increasingly popular as an input mechanism for human-computer interfaces. The availability of MEMS (Micro-Electromechanical System) 3-axis linear accelerometers allows for the design of an inexpensive mobile gesture recognition system. Wearable inertial sensors are a low-cost, low-power solution to recognize gestures and, more generally, track the movements of a person. Gesture recognition algorithms have traditionally only been implemented in cases where ample system resources are available, i.e. on desktop computers with fast processors and large amounts of memory. In the cases where a gesture recognition algorithm has been implemented on a resource-constrained device, only the simplest algorithms were implemented to recognize only a small set of gestures. Current gesture recognition technology can be improved by making algorithms faster, more robust, and more accurate. The most dramatic results in optimization are obtained by completely changing an algorithm to decrease the number of computations. Algorithms can also be optimized by profiling or timing the different sections of the algorithm to identify problem areas. Gestures have two aspects of signal characteristics that make them difficult to recognize: segmentation ambiguity and spatio-temporal variability. Segmentation ambiguity refers to not knowing the gesture boundaries, and therefore reference patterns have to be matched with all possible segments of input signals. Spatio-temporal variability refers to the fact that each repetition of the same gesture varies dynamically in shape and duration, even for the same gesturer. The objective of this study was to evaluate the various gesture recognition algorithms currently in use, after which the most suitable algorithm was optimized in order to implement it on a mobile device. Gesture recognition techniques studied include hidden Markov models, artificial neural networks and dynamic time warping. A dataset for evaluating the gesture recognition algorithms was gathered using a mobile device’s embedded accelerometer. The algorithms were evaluated based on computational efficiency, recognition accuracy and storage efficiency. The optimized algorithm was implemented in a user application on the mobile device to test the empirical validity of the study. / Dissertation (MEng)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted

In an increasingly mobile world, the combination of mobile computing devices, publicly accessible Wi-Fi hotspots, and camera phones pose a significant threat to alphanumeric passwords in public environments. Graphical passwords, introduced as an alternative to alphanumerical passwords, help prevent successful shoulder-surfing attacks – covertly observing or recording a password login session, however, most cannot prevent intersection analysis on the data collected through shoulder-surfing. ColorDots is a new graphical password scheme designed to be easy to use and learn, to prevent successful shoulder-surfing attacks, and to hinder intersection analysis. A software implementation of ColorDots is tested, and the results analyzed. This study showed the ColorDots graphical password scheme does prevent shoulder-surfing, and hinders intersection analysis on digital recordings of multiple shoulder-surfing attacks. Furthermore, ColorDots may be just as convenient to use as alphanumeric passwords, while improving password security in public environments.

This thesis presents the design for a novel haptic interface for large-format touchscreens. Techniques such as electrovibration, ultrasonic vibration, and external braked devices have been developed by other researchers to deliver haptic feedback to touchscreen users. However, these methods do not address the need for spatial constraints that only restrict user motion in the direction of the constraint. This technology gap contributes to the lack of haptic technology available for touchscreen-based upper-limb rehabilitation, despite the prevalent use of haptics in other forms of robotic rehabilitation. The goal of this thesis is to display kinesthetic haptic constraints to the touchscreen user in the form of boundaries and paths, which assist or challenge the user in interacting with the touchscreen. The presented prototype accomplishes this by steering a single wheel in contact with the display while remaining driven by the user. It employs a novel embedded force sensor, which it uses to measure the interaction force between the user and the touchscreen. The haptic response of the device is controlled using this force data to characterize user intent. The prototype can operate in a simulated free mode as well as simulate rigid and compliant obstacles and path constraints. A data architecture has been created to allow the prototype to be used as a peripheral add-on device which reacts to haptic environments created and modified on the touchscreen. The long-term goal of this work is to create a haptic system that enables a touchscreen-based rehabilitation platform for people with upper limb impairments.

In augmented reality, information is often distributed between real and virtual contexts, and often appears at different distances from the viewer. This raises the issues of (1) context switching, when attention is switched between real and virtual contexts, (2) focal distance switching, when the eye accommodates to see information in sharp focus at a new distance, and (3) transient focal blur, when information is seen out of focus, during the time interval of focal distance switching. This dissertation research has quantified the impact of context switching, focal distance switching, and transient focal blur on human performance and eye fatigue in both monocular and binocular viewing conditions. Further, this research has developed a novel font that when seen out-of-focus looks sharper than standard fonts. This SharpView font promises to mitigate the effect of transient focal blur. Developing this font has required (1) mathematically modeling out-of-focus blur with Zernike polynomials, which model focal deficiencies of human vision, (2) developing a focus correction algorithm based on total variation optimization, which corrects out-of-focus blur, and (3) developing a novel algorithm for measuring font sharpness. Finally, this research has validated these fonts through simulation and optical camera-based measurement. This validation has shown that, when seen out of focus, SharpView fonts are as much as 40 to 50% sharper than standard fonts. This promises to improve font legibility in many applications of augmented reality.

Using an explicit integration method in physically based animations has many advantages including conceptual and computational simplicity, however, it re- quires small time steps to ensure low numerical instability. Simulations with large numbers of individually interacting components such as cloth, hair, and fluid models, are limited by the sections of particles most susceptible to error. This results in the need for smaller time steps than required for the majority of the system. These sections can be diverse and dynamic, quickly changing in size and location based on forces in the system. Identifying and handling these trou- blesome sections could allow for a larger time step to be selected, while preventing a breakdown in the simulation. This thesis presents Smarticles (smart particles), a method of individually de- tecting particles exhibiting signs of instability and stabilizing them with minimal adverse effects to visual accuracy. As a result, higher levels of error introduced from large time steps can be tolerated with minimal overhead. Two separate approaches to Smarticles were implemented. They attempt to find oscillating particles by analyzing a particle’s (1) past behavior and (2) behavior with re- spect to its neighbors along a strand. Both versions of Smarticles attempt to correct unstable particles using velocity dampening. Smarticles was applied to a two dimensional hair simulation modeled as a continuum using smooth particle hydrodynamic. Hair strands are formed by linking particles together using one of two methods: position based dynamics or mass-spring forces. Both versions of Smarticles, as well as a control of normal particles, were directly compared and evaluated based on stability and visual fluidity. Hair particles were exposed to various forms of external forces under increasing time step lengths. Testing showed that both versions of Smarticles working together allowed an average increase of 18.62% in the time step length for hair linked with position based dynamics. In addition, Smarticles was able to significantly reduce visible instability at even larger time steps. While these results suggest Smarticles is successful, the method used to correct particle instability may jeopardize other important aspects of the simulation. A more accurate correction method would likely need to be developed to make Smarticles an advantageous method.

This master's thesis explores the effectiveness of an educational intervention using an interactive notebook to support and supplement instruction in a foundational-level programming course. A quantitative, quasi-experimental group comparison method was employed, where students were placed into either a control or a treatment group. Data was collected from assignment and final grades, as well as self-reported time spent using the notebook. Independent t-tests and correlation were used for data analysis. Results were inconclusive but did indicate that the intervention had a possible effect. Further studies may explore better efficacy, implementation, and satisfaction of interactive notebooks across a larger population and multiple class topics.