This thesis focuses on the development of a low power energy harvesting system specifically targeted for wireless sensor nodes (WSN) and wireless body area network (WBAN) applications. The idea for the system is derived from the operation of a micro-grid and therefore is termed as a pico-grid and it is capable of simultaneously delivering power from multiple and multitype energy harvesters to the load at the same time, through the proposed parallel load sharing mechanism achieved by a voltage droop control method. Solar panels and thermoelectric generator (TEG) are demonstrated as the main energy harvesters for the system. Since the magnitude of the output power of the harvesters is time-varying, the droop gain in the droop feedback circuitry should be designed to be dynamic and self-adjusted according to this variation. This ensures that the maximum power is capable to be delivered to the load at all times. To achieve this, the droop gain is integrated with a light dependent resistor (LDR) and thermistor whose resistance varies with the magnitude of the source of energy for the solar panel and TEG, respectively. The experimental results demonstrate a successful variation droop mechanism and all connected sources are able to share equal load demands between them, with a maximum load sharing error of 5 %. The same mechanism is also demonstrated to work for maximum power point tracking (MPPT) functionality. This concept can potentially be extended to any other types of energy harvester. The integration of energy storage elements becomes a necessity in the pico-grid, in order to support the intermittent and sporadic nature of the output power for the harvesters. A rechargeable battery and supercapacitor are integrated in the system, and each is accurately designed to be charged when the loading in the system is low and discharged when the loading in the system is high. The dc bus voltage which indicates the magnitude of the loading in the system is utilised as the signal for the desired mode of operation. The constructed system demonstrates a successful operation of charging and discharging at specific levels of loading in the system. The system is then integrated and the first wearable prototype of the pico-grid is built and tested. A successful operation of the prototype is demonstrated and the load demand is shared equally between the source converters and energy storage. Furthermore, the pico-grid is shown to possess an inherent plug-and-play capability for the source and load converters. Few recommendations are presented in order to further improve the feasibility and reliability of the prototype for real world applications. Next, due to the opportunity of working with a new semiconductor compound and accessibility to the fabrication facilities, a ZnON thin film diode is fabricated and intended to be implemented as a flexible rectifier circuit. The fabrication process can be done at low temperature, hence opening up the possibility of depositing the device on a flexible substrate. From the temperature dependent I-V measurements, a novel method of extracting important parameters such as ideality factor, barrier height, and series resistance of the diode based on a curve fitting method is proposed. It is determined that the ideality factor of the fabricated diode is high (> 2 at RT), due to the existence of other transport mechanism apart from thermionic emission that dominates the conduction process at lower temperature. It is concluded that the high series resistance of the fabricated diode (3.8 kΩ at RT) would mainly hinder the performance of the diode in a rectifier circuit.

Wearable systems continue to gain new markets by addressing improved performance and lower size, weight and cost. Both civilian and military markets have incorporated wearable technologies to enhance and facilitate user's tasks and activities. A wearable system is a heterogeneous system composed of diverse electronic modules: data processing, input and output modules. The system is constructed to be body-borne and therefore, several constraints are put on wearable systems regarding wearability (size, weight, placement, etc.) and robustness rendering the task of designing wearable systems challenging. In this thesis, an overview of wearable systems was given by discussing definition, technology challenges, market analysis and design methodologies. Main research targeted at network architectures and robustness to environmental stresses and electromagnetic interference (EMI). The network architecture designated the data communication on the intermodule level - topology and infrastructure. A deeper analysis of wearable requirements on the network architecture was made and a new architecture is proposed based on DC power line communication network (DC-PLC). In addition, wired data communication was compared to wireless data communication by introducing statistical communication model and looking at multiple design attributes: power efficiency, scalability, and wearability. The included papers focused on wearable systems related issues including analysis of present situation, environmental and electrical robustness studies, theoretical and computer aided modelling, and experimental testing to demonstrate new wearable architectural concepts. A roadmap was given by examining the past and predicting the future of wearable systems in terms of technology, market, and architecture. However, the roadmap was updated within this thesis to include new market growth figures that proved to be far less than was predicted in 2004. User and application environmental requirements to be applied on future wearable systems were identified. A procedure is presented to address EMI and evaluated solutions in wearable application through modelling and simulation. Environmental robustness and wearability of wearable systems in general, and washability and conductive textile in particular are investigated. A measurement-based methodology to model electrical properties of conductive textile when subjected to washing was given. Employing a wired data communication network was found to be more appropriate for wearable systems than wireless networks when prioritizing power efficiency. The wearability and scalability of the wired networks was enhanced through conductive textile and DC-PLC, respectively. A basic wearable application was built to demonstrate the suitability of DC-PLC communication with conductive textile as infrastructure. The conductive textile based on metal filament showed better mechanical robustness than metal plated conductive textile. A more advanced wearable demonstrator, where DC-PLC network was implemented using transceivers, further strengthened the proposed wearable architecture. Based on the overview, the theoretical, modelling and experimental work, a possible approach of designing wearable systems that met several contradicting requirements was given.

The main vision of wearable computing is to make electronic systems an important part of everyday clothing in the future which will serve as intelligent personal assistants. Wearable devices have the potential to be wearable computers and not mere input/output devices for the human body. The present thesis focuses on introducing a new wearable computing paradigm, where the processing elements are closely coupled with the sensors that are distributed using Instruction Systolic Array (ISA) architecture. The thesis describes a novel, multiple sensor, multiple processor system architecture prototype based on the Instruction Systolic Array paradigm for distributed computing on fabrics. The thesis introduces new programming model to implement the distributed computer on fabrics. The implementation of the concept has been validated using parallel algorithms. A real-time shape sensing and reconstruction application has been implemented on this architecture and has demonstrated a physical design for a wearable system based on the ISA concept constructed from off-the-shelf microcontrollers and sensors. Results demonstrate that the real time application executes on the prototype ISA implementation thus confirming the viability of the proposed architecture for fabric-resident computing devices.

Chronic cardiovascular disease (CVD) is increasingly becoming a burden for global healthcare systems. This burden can be attributed in part to traditional methods of managing CVD in an aging population that involves periodic meetings between the patient and their healthcare provider. There is growing interest in developing continuous monitoring systems to assist in the management of CVD. Monitoring systems can utilize advances in wearable devices and health records, which provides minimally invasive methods to monitor a patient’s health. Despite these advances, the algorithms deployed to automatically analyze the wearable sensor and health data is considered too computationally expensive to run on the mobile device. Instead, current mobile devices continuously transmit the collected data to a server for analysis at great computational and data transmission expense. In this thesis a novel mobile system designed for monitoring CVD is presented. Unlike existing systems, the proposed system allows for the continuous monitoring of physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each algorithm for monitoring CVD is also discussed. Both models were able to classify CVD risk with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, unlike current systems the resource requirements for each component in the system was evaluated. The MLP was found to be more efficient when running on the mobile device compared to the SVM. The results of thesis also show that the MLAs complexity was not a barrier to deployment on a mobile device. / Thesis / Master of Applied Science (MASc) / In this thesis, a novel mobile system for monitoring cardiovascular (CVD) disease is presented. The system allows for the continuous monitoring of both physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a remote server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each MLA for monitoring CVD is also discussed. Both models were able to classify CVD severity with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, the resource requirements for each component in the system were evaluated. The results show that the MLAs complexity was not a barrier to deployment on a mobile device.

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