abstract: Movement disorders are becoming one of the leading causes of functional disability due to aging populations and extended life expectancy. Diagnosis, treatment, and rehabilitation currently depend on the behavior observed in a clinical environment. After the patient leaves the clinic, there is no standard approach to continuously monitor the patient and report potential problems. Furthermore, self-recording is inconvenient and unreliable. To address these challenges, wearable health monitoring is emerging as an effective way to augment clinical care for movement disorders. Wearable devices are being used in many health, fitness, and activity monitoring applications. However, their widespread adoption has been hindered by several adaptation and technical challenges. First, conventional rigid devices are uncomfortable to wear for long periods. Second, wearable devices must operate under very low-energy budgets due to their small battery capacities. Small batteries create a need for frequent recharging, which in turn leads users to stop using them. Third, the usefulness of wearable devices must be demonstrated through high impact applications such that users can get value out of them. This dissertation presents solutions to solving the challenges faced by wearable devices. First, it presents an open-source hardware/software platform for wearable health monitoring. The proposed platform uses flexible hybrid electronics to enable devices that conform to the shape of the user’s body. Second, it proposes an algorithm to enable recharge-free operation of wearable devices that harvest energy from the environment. The proposed solution maximizes the performance of the wearable device under minimum energy constraints. The results of the proposed algorithm are, on average, within 3% of the optimal solution computed offline. Third, a comprehensive framework for human activity recognition (HAR), one of the first steps towards a solution for movement disorders is presented. It starts with an online learning framework for HAR. Experiments on a low power IoT device (TI-CC2650 MCU) with twenty-two users show 95% accuracy in identifying seven activities and their transitions with less than 12.5 mW power consumption. The online learning framework is accompanied by a transfer learning approach for HAR that determines the number of neural network layers to transfer among uses to enable efficient online learning. Next, a technique to co-optimize the accuracy and active time of wearable applications by utilizing multiple design points with different energy-accuracy trade-offs is presented. The proposed technique switches between the design points at runtime to maximize a generalized objective function under tight harvested energy budget constraints. Finally, we present the first ultra-low-energy hardware accelerator that makes it practical to perform HAR on energy harvested from wearable devices. The accelerator consumes 22.4 microjoules per operation using a commercial 65 nm technology. In summary, the solutions presented in this dissertation can enable the wider adoption of wearable devices. / Dissertation/Thesis / Human activity recognition dataset / Doctoral Dissertation Computer Engineering 2020

This thesis deals with the idea of energy harvesting from mechanical vibration. It describes the magnetostrictive principle as a possibility to obtain an electrical energy. It is about a generator made of a beam with Terfenol-D material and a coil. The model of this device is created in Matlab/Simulink and FEMM application. For various values of measured vibration, these methods are then compared. In FEMM, you can improve energy gain by modeling of various environmental conditions and with using of another materials (e.g. by adding of permanent magnets). The outcome of the this thesis expresses the ability to harvest the energy with designed magnetostrictive generator compared to the already created models of the piezoelectric and electromagnetic generator. Based on these results, it is possible to determine which generator is more suitable for particular application.

This thesis will deal with the use of artificial intelligence methods for solving optimization problems with multiple variables. A theorethical part presents problems of global optimization and overview of solution methods. For practical reasons, special attention is paid to evolutionary algorithms. The subject of optimization itself is energy harvester based on a piezoelectric effect. Its nature and modeling is devoted to one chapter. A part of the thesis is the implementation of the SOMA algorithm for finding the optimal parameters of the generator for maximum performance.

The aim of the diploma thesis was to design an electromagnetic generator to the purpose of supplying signalling and safety devices on the rail tracks. The electromagnetic generator is a separate electrical source, which is mainly needed for safety in the case of not covered tracks by electrical installation. The thesis proposes two types of electromagnetic generator and one with non-linear stiffness, which are designed for the passage of trains with low frequency.

This master’s thesis deals with energy harvesting from the mechanical energy sources. The mechanical energy may be converted into the electrical by means of a vibrational microgenerator. The thesis deals also with the production of energy from different renewable sources. There have been identified conditions for effective transformation of the remaining energy. In order to satisfy these conditions a circuit for load matching to energy harvester has to be used. Four variants of matching circuit have been designed and analyzed in PSpice or LTspice. It has been designed a system of power factor correction controlled by circuit with discrete components and also by microcontroller. For one of these variants a specific integrated circuit has been used. The designed circuits have been built and their parameters haven been determined by measurement.

This project deals with the problematics of self-powered universal GSM/GPRS module which is used for sensorical applications. The first part analyzes features of mobile networks and applying energy from the environment. The second section expalins the various parts and characteristic of their associated components together with some fundamental functions of the technology for possible realization of sensoric module. The third part describes plan for system realisation and fourth parth illustrates achiaved results.

Orientador: Eduardo Paciência Godoy / Resumo: A colheita de energia emerge como uma das soluções para a alimentação sustentável de sensores e dispositivos de monitoramento em redes sem fio nas aplicações da Internet das Coisas (IoT), permitindo até mesmo prescindir do uso de baterias. Este trabalho descreve o desenvolvimento de uma plataforma de colheita de energia e monitoramento de vazão a partir de redes de distribuição hidráulicas. O sistema experimental montado apresenta um circuito fechado de fluxo variável de água, criado a partir de uma bomba pressurizadora controlável eletronicamente, com sensor de vazão e um micro hidrogerador. A energia elétrica recuperada através do micro hidrogerador é utilizada para alimentar diferentes dispositivos de IoT com comunicação via Wi-Fi dos dados de monitoramento da vazão e volume, assim como, fornece energia para baterias. Os testes operacionais tiveram como objetivo dimensionar a quantidade de energia recuperada, em comparação com a energia consumida pelos dispositivos e bateria conectadas, em função dos valores de vazão obtidos pela plataforma. Nos testes realizados foi possível demonstrar a capacidade da colheita de energia pelo micro hidrogerador, com geração de aproximadamente 1W de energia e sua utilização para a operação contínua de dispositivos em aplicações de IoT. O estudo e implantação de diferentes modos de operação para os dispositivos medidores de vazão possibilitaram a redução do consumo de energia em até 54,4% e maximizaram a eficiência energética da solução, co... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Energy harvesting emerges as one of the solutions for sustainable power supply of sensors and monitoring devices in wireless networks in the Internet of Things (IoT) applications, even allowing to dispense with the use of batteries. This work describes the development of an energy harvesting and flow monitoring platform from hydraulic distribution networks. The assembled experimental system features a variable water flow closed loop, created from an electronically controllable pressurizing pump with flow sensor and a micro hydrogenerator. The electrical energy recovered through the micro-generator supplies different IoT device models with Wi-Fi communication of the flow and volume monitoring data and also provides power for storage batteries. The operational tests aimed to measure the amount of energy recovered, compared to the energy consumed by the connected devices and battery, according to the flow values obtained by the platform. In the tests carried out it was possible to demonstrate the capacity to harvest energy by the micro-generator, generating approximately 1W of energy and its use for the continuous operation of devices in IoT applications. The study and implementation of different modes of operation for flow meter devices make it possible to reduce energy consumption by up to 54.4% and maximize the energy efficiency of the solution, proving the potential of energy recovery as a viable alternative to guarantee the operation of self-sustaining meters for IoT applic... (Complete abstract click electronic access below) / Mestre

We examined energy harvesting using a vertical composite laminate beam with an additional moving mass subjected to kinematic harmonic excitation. The gravity effect and the moving tip mass applied to the system cause considerable changes in effective spring-mass characteristics of the bending beam. Simultaneously, we observed dynamical beam damping by an additional degree of freedom and non-linear effects including friction between the moving mass and the beam structure. The experiments were performed on the beam excited kinematically by a shaker, while beam velocity measurements were made by a scanning laser vibrometer. We applied modal analysis in the limit of a fairly low excitation level. The selected modal vibrations are illustrated by corresponding output time series.

No description available.

Among various energy harvesting technologies reverse electrowetting-on-dielectric energy harvesting (REWOD) has been proved to harvest energy from low frequency motion such as many human motion activities (e.g. walking, running, jogging etc.). Voltage rectification and DC-DC boosting of low magnitude AC voltage from REWOD can be used to reliably self-power the wearable sensors. In this work, a commercial component-based rectifier and DC-DC converter is designed and experimentally verified, for further miniaturization standard 180 nm CMOS process is used to design the rectifier and the DC-DC boost converter.This work also includes the MATLAB based model for REWOD energy harvester for various REWOD models. In REWOD energy harvesting, a mechanical input during the motion causes the electrolyte placed in between two dissimilar electrodes to squeeze back and forth thereby periodically changing the effective interfacial area, hence generating alternating current. The alternating current is given to the rectifier design. There is no realistic model that has been developed yet for this technique. Thereby, a MATLAB based REWOD model is developed for the realistic simulation of the REWOD phenomenon. In the work, a comparison of different REWOD models such as planar surface, rough surface and porous models are performed demonstrating the variations in capacitance, current and voltage.