A Research Report Submitted to the Faculty of Commerce, Law and Management, Witwatersrand University School of Economics and Business Sciences, In partial fulfilment of the requirements of a Master Degree in Marketing, May 2017 / The consumer market of Smart wearable technology has shown a massive growth, therefore convincing that Smart wearable technology will be the next great thing, with market analysts forecasting its market to be worth over $30 billion by 2020. However this belief is mainly driven by major new technology manufacturers to produce Smart wearable devices that commoditise cellphones, tablets, and portable computers to influence consumer purchase intention. Consumers purchase intention is crucial for every business survival, therefore cannot be overemphasised. With the increasing number of Smart wearable technology brands on the electronics market, South African consumers have to make a choice on which brands to purchase. This study examines the factors influencing the purchase intention of Smart wearable technology in South Africa, with a special focus on product quality, design, price, and consumer attitude. From the academic side, the study makes a significant contribution by exploring the impact of product price and consumer attitude on consumer purchase intention. As a result, manufacturers in the wearable technology industry may apply this study information to develop proper strategies that will help influence more people to purchase wearable devices and ensure Smart wearable technology market growth. The study data were collected through the aid of a self-administered hardcopy questionnaire, which was circulated by the researcher in the University of the Witwatersrand Johannesburg. The research findings show that both consumer attitude and product price have a significant positive effect on the intention to purchase Smart wearable devices. Nevertheless, to be more precise, the effect of consumer’s attitude on purchase intention goes through the positive effect of a product design on consumer’s attitude. Both product quality and price are found to extend the effect of positivity of consumer’s attitude toward the product or brand, and the price tag of the product. These scenarios are fully supported in hypotheses one, two, and three. Although both quality and design positively influence product price, Product design is found to have an enlarging effect on product price. Generally, it can be stated that the design of a product successfully influence the price set for product. / XL2018

This paper evaluates the effectiveness of a wearable device, developed by the author, to detect different types of epileptic seizures and monitor epileptic patients. The device uses GSR, Pulse, EMG, body temperature and 3-axis accelerometer sensors to detect epilepsy. The device first learns the signal patterns of the epileptic patient in ideal condition. The signal pattern generated during the epileptic seizure, which are distinct from other signal patterns, are detected and analyzed by the algorithms developed by the author. Based on an analysis, the device successfully detected different types of epileptic seizures. The author conducted an experiment on himself to determine the effectiveness of the device and the algorithms. Based on the simulation results, the algorithms are 100 percent accurate in detecting different types of epileptic seizures. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

The Humanoid Walking Robot (HWR) is a research platform for the study of legged and wearable robots actuated with Hydro Muscles. The fluid operated HWR is representative of a class of biologically inspired, and in some aspects highly biomimetic robotic musculoskeletal appendages showing certain advantages in comparison to more conventional artificial limbs and braces for physical therapy/rehabilitation, assistance of daily living, and augmentation. The HWR closely mimics the human body structure and function, including the skeleton, ligaments, tendons, and muscles. The HWR can emulate close to human-like movements even when subjected to simplified control laws. One of the main drawbacks of this approach is the inaccessibility of an appropriate fluid flow management support system, in the form of affordable, lightweight, compact, and good quality valves suitable for robotics applications. To resolve this shortcoming, the Compact Robotic Flow Control Valve (CRFC Valve) is introduced and successfully proof-of-concept tested. The HWR added with the CRFC Valve has potential to be a highly energy efficient, lightweight, controllable, affordable, and customizable solution that can resolve single muscle action.

Observa-se nos tempos atuais um crescente interesse e demanda por novas tecnologias de sensoriamento e interação. A monitoração, com o objetivo de reconhecimento de movimentos humanos, permite oferecer serviços personalizados em diferentes áreas, dentre elas a área de cuidados médicos. Essa monitoração pode ser realizada por meio de diferentes técnicas como o uso de câmeras de vídeo, instrumentação do ambiente onde o indivíduo habita, ou pelo uso de dispositivos pessoais acoplados ao corpo. Os dispositivos acoplados ao corpo apresentam vantagens como baixo custo, uso confortável, além de muitas vezes serem despercebidos pelo usuário, diminuindo a sensação de invasão de privacidade durante a monitoração. Além disso, o dispositivo sensor pode ser facilmente acoplado ao corpo pelo próprio usuário, tornando o seu uso efetivo. Deste modo, este trabalho apresenta o desenvolvimento de um sistema que emprega técnicas de inteligência computacional e um acelerômetro facilmente acoplado ao punho do usuário para efetuar, de maneira confortável e não invasiva, o reconhecimento de movimentos básicos da rotina de uma pessoa. Aplicando máquinas de vetores de suporte para classificar os sinais e a razão discriminante de Fisher para efetuar a seleção das características mais significativas, o sistema apresentou uma taxa de sucesso em torno de 93% no reconhecimento de movimentos básicos efetuados por indivíduos monitorados. O sistema apresenta potencialidade para ser integrado a um hardware embarcado de baixo custo, responsável pelo gerenciamento da aquisição dos dados e pelo encaminhamento das informações a um sistema de monitoramento ou armazenamento. As informações providas por este sistema podem ser destinadas à promoção da saúde e bem estar do indivíduo, bem como utilizadas em diagnósticos ou monitoramento remoto de pacientes em um ambiente de vida assistida. / Nowadays it is observed a growing interest and demand for new sensing technologies and interaction. Monitoring with the objective of recognizing human movements, allows us to offer personalized services in different areas, among them healthcare. This monitoring can be performed through the use of different techniques such as the use of video cameras, living environment instrumentation, or the use of personal devices attached to the body, also known as wearable devices. These wearable devices have some advantages such as low cost, comfortable to use, and are often unnoticed by the user, reducing the feeling of privacy invasion during the monitoring. In addition, the sensing device can be easily attached to the body by the user itself, making its use effective. Thus, this work presents the development of a system that uses computational intelligence techniques and an accelerometer which is easily attached to the users wrist to perform, in a comfortable and non-invasive manner, the recognition of basic movements of a persons routine. By applying support vector machines to classify the signals and Fishers discriminant ratio to select the most significant features, the system has shown a success rate of 93% in the recognition of basic movements performed by monitored individuals. The system has the potential to be integrated into a low-cost embedded hardware, which is responsible for managing the data acquisition and routing the movement data to a remote monitoring system or storage. The information provided by the system can be designed to promote the health and wellness of the individual, as well used in diagnostics or remote patient monitoring in an ambient assisted living (AAL).

The contact patterns among individuals can significantly affect the progress of an infectious outbreak within a population. Gathering data about these interaction and mixing patterns is essential to assess computational modeling of infectious diseases. Various self-report approaches have been designed in different studies to collect data about contact rates and patterns. Recent advances in sensing technology provide researchers with a bilateral automated data collection devices to facilitate contact gathering overcoming the disadvantages of previous approaches. In this study, a novel unilateral wearable sensing architecture has been proposed that overcome the limitations of the bi-lateral sensing. Our unilateral wearable sensing system gather contact data using hybrid sensor arrays embedded in wearable shirt. A smartphone application has been used to transfer the collected sensors data to the cloud and apply deep learning model to estimate the number of human contacts and the results are stored in the cloud database. The deep learning model has been developed on the hand labelled data over multiple experiments. This model has been tested and evaluated, and these results were reported in the study. Sensitivity analysis has been performed to choose the most suitable image resolution and format for the model to estimate contacts and to analyze the model's consumption of computer resources.

Cada vez mais o rastreamento do olhar tem sido usado para interação humano-computador em diversos cenários, como forma de interação (usualmente substituindo o mouse, principalmente para pessoas com deficiências físicas) ou estudo dos padrões de atenção de uma pessoa (em situações como fazendo compras no mercado, olhando uma página na internet ou dirigindo um carro). Ao mesmo tempo, dispositivos vestíveis tais quais pequenas telas montadas na cabeça e sensores para medir dados relativos à saúde e exercício físico realizado por um usuário, também têm avançado muito nos últimos anos, finalmente chegando a se tornarem acessíveis aos consumidores. Essa forma de tecnologia se caracteriza por dispositivos que o usuário usa junto de seu corpo, como uma peça de roupa ou acessório. O dispositivo e o usuário estão em constante interação e tais sistemas são feitos para melhorar a execução de uma ação pelo usuário (por exemplo dando informações sobre a ação em questão) ou facilitar a execução de várias tarefas concorrentemente. O uso de rastreadores de olhar em computação vestível permite uma nova forma de interação para tais dispositivos, possibilitando que o usuário interaja com eles enquanto usa as mãos para realizar outra ação. Em dispositivos vestíveis, o consumo de energia é um fator importante do sistema que afeta sua utilidade e deve ser considerado em seu design. Infelizmente, rastreadores oculares atuais ignoram seu consumo e focam-se principalmente em precisão e acurácia, seguindo a ideia de que trabalhar com imagens de alta resolução e frequência maior implica em melhor desempenho. Porém tratar mais quadros por segundo ou imagens com resolução maior demandam mais poder de processamento do computador, consequentemente aumentando o gasto energético. Um dispositivo que seja mais econômico tem vários benefícios, por exemplo menor geração de calor e maior vida útil de seus componentes eletrônicos. Contudo, o maior impacto é o aumento da duração da bateria para dispositivos vestíveis. Pode-se economizar energia diminuindo resolução e frequência da câmera usada, mas os efeitos desses parâmetros na precisão e acurácia da estimação do olhar não foram investigados até o presente. Neste trabalho propomos criar uma plataforma de testes, que permita a integração de alguns algoritmos de rastreamento de olhar disponíveis, tais como Starburst, ITU Gaze Tracker e Pupil, para estudar e comparar o impacto da variação de resolução e frequência na acurácia e precisão dos algoritmos. Por meio de um experimento com usuários analisamos o desempenho e consumo desses algoritmos sob diversos valores de resolução e frequência. Nossos resultados indicam que apenas a diminuição da resolução de 480 para 240 linhas (mantendo a proporção da imagem) já acarreta em ao menos 66% de economia de energia em alguns rastreadores sem perda significativa de acurácia. / Eye tracking has been used more and more in human-computer interaction in several scenarios, as a form of interaction (mainly replacing the mouse for the physically handicapped) or as a means to study attention patterns of a person (performing activities such as grocery shopping, reading web pages or driving a car). At the same time, wearable devices such as small head-mounted screens and health-related sensors, have improved considerably in these years, finally becoming accessible to mainstream consumers. This form of technology is defined by devices that an user uses alongside his body, like a piece of clothing or accessory. The device and the user are in constant interaction and such systems are usually made to improve the user\'s ability to execute a task (for example, by giving contextualized information about the task in question) or to facilitate the parallel execution of several tasks. The use of eye trackers in wearable computing allows a new form of interaction in these devices, allowing the user to interact with them while performing another action with his hands. In wearable devices, the energy consumption is an important factor of the system which affects its utility and must be considered in its design. Unfortunately, current eye trackers ignore energy consumption and instead mainly focus on precision and accuracy, following the idea that working with higher resolution and higher frequency images will improve performance. However, processing more frames, or larger frames, per second require more computing power, consequentially increasing energy expense. A device that is more economical has several benefits, such as less heat generation and a greater life-span of its components. Yet the greatest impact is the increased battery duration for the wearable devices. Energy can be saved by lowering the frequency and resolution of the camera used by the tracker, but the effect of these parameters in the precision and accuracy of eye tracking have not been researched until now. In this work we propose an eye tracking testing platform, that allows integration with existing eye tracking algorithms such as Starburst, ITU Gaze Tracker and Pupil, to study and compare the impact of varying the resolution and frequency of the camera on accuracy and precision of the algorithms. Through a user experiment we analyzed the performance and consumption of these algorithms under various resolution and frequency values. Our result indicate that only lowering the resolution from 480 to 240 lines (keeping the image aspect ratio) already amounts to a 66% energy economy in some trackers without any meaningful loss of accuracy.

Wearable assistive robotics have the potential to address an unmet medical need of reducing disability in individuals with chronic hand impairments due to neurological trauma. Despite myriad prior works, few patients have seen the benefits of such devices. Following application experience with tendon-actuated soft robotic gloves and a collaborator's orthosis with novel flat-spring actuators, we identified two common assumptions regarding hand orthosis design. The first was reliance on incomplete studies of grasping forces during activities of daily living as a basis for design criteria, leading to poor optimization. The second was a neglect of increases in muscle tone following neurological trauma, rendering most devices non-applicable to a large subset of the population. To address these gaps, we measured joint torques during activities of daily living with able-bodied subjects using dexterity representative of orthosis-aided motion. Next, we measured assistive torques needed to extend the fingers of individuals with increased flexor tone following TBI. Finally, we applied this knowledge to design a cable actuated orthosis for assisting finger extension, providing a basis for future work focused on an under-represented subgroup of patients.

Children are born with an intrinsic motivation to play games. Over the past decade, educational video games have invaded mainstream classroom instruction and researchers are “considering how games might be used in pursuit of engaging, effective learning experiences� (Squire and Jenkins, 2003). This research encompasses designing math games using a constructivist and embodied cognition pedagogy in an effort to answer the question: “Will overlapping wearable technology and mathematical objectives with traditional children’s games show improved efficacy in students’ math skills and increase students’ motivation to learn math in 4th thru 6th grade students?� Methods of research include a usability study and four subsequent iterative studies to improve the game and the technology, measuring students’ math self-efficacy and motivation to learn math. The final goal of this thesis is to design, test and document an engaging children’s math learning game using wearable technology that requires active physical experiences while involved in deep thinking and complex problem solving (Gee, 2003) within real world environments, beyond classrooms, pencil and paper, and even beyond traditional computer games in front of a computer screen.

Noninvasive pulse oximetry represents an area of potential interest to the army, because it could provide cost-effective, safe, fast and real-time physiological assessment in a combat injured soldier. Consequently, there is a need to develop a reliable, battery-powered, wearable pulse oximeter to acquire and process photoplethysmographic (PPG) signals using an optimized sensor configuration. A key requirement in the optimal design of a wearable wireless pulse oximeter is low power management without compromising signal quality. This research investigated the advantage gained by increasing the area of the photodetector and decreasing the light emitting diode (LED) driving currents to reduce the overall power requirement of a reflectance mode pulse oximeter sensor. In vitro and preliminary in vivo experiments were conducted to evaluate a multiple photodetector reflectance sensor setup to simulate a varying detection area. It was concluded that a reflection pulse oximeter sensor employing a large area photodetector is preferred over a similar transmission type sensor for extending the battery life of a wireless pulse oximeter intended for future telemedicine applications.

A WIoT is a wireless network of low-power sensing nodes placed on the human body. While operating, these networks routinely collect physiological signals to send to offsite medical professionals for review. In this manner, these networks support a concept known as pervasive healthcare in which patients can be continuously monitored and treated remotely. Given that these networks are used to guide medical treatment and depend on transmitting sensitive data, it is important to ensure that the communication channel remains secure. Symmetric pairwise cryptography is a traditional scheme that can be used to provide such security. The scheme functions by sharing a cryptographic key between a pair of sensors. Once shared, the key can then be used by both parties to encrypt and decrypt all future messages. To configure a WIoT to support the use of symmetric pairwise cryptography a key distribution protocol is required. Schemes for pre-deployment are often used to perform this distribution. These schemes usually require inserting key information into WIoT devices before they can be used in the network. Unfortunately, this need to manually configure WIoT devices can decrease their usability. In this thesis we propose and evaluate an alternative approach to key distribution that uses physiological signals derived from accelerometer and gyroscope sensors. The evaluation of our approach indicates that more study is required to determine techniques that will enable ballistocardiography-derived physiological signals to provide secure key distribution.