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Wong, Sheung Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 63-64). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.iii / Acknowledgements --- p.iv / List of figures --- p.V / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1. --- Segway Personal Transporter (PT) --- p.1 / Chapter 1.2. --- Existing research using Segway Robotic Mobility Platform´ёØ (RMP) --- p.3 / Chapter 1.3. --- The ICSL Segway Rider --- p.9 / Chapter 1.4. --- Thesis outlines --- p.10 / Chapter Chapter 2 --- ICSL Segway Rider --- p.11 / Chapter 2.1. --- Design concept --- p.11 / Chapter 2.2. --- Design overview --- p.12 / Chapter 2.3. --- Actuating components --- p.14 / Chapter 2.4. --- Electronic and sensing components --- p.24 / Chapter 2.5. --- Software development of Segway Rider --- p.28 / Chapter 2.6. --- Chapter summary --- p.31 / Chapter Chapter 3 --- The grand challenge --- p.32 / Chapter 3.1. --- Objective --- p.32 / Chapter 3.2. --- Experiment --- p.33 / Chapter 3.3. --- Running lane tracking by computer vision --- p.34 / Chapter 3.3.1. --- Color space conversion --- p.36 / Chapter 3.3.2. --- Apply binary threshold --- p.37 / Chapter 3.3.3. --- Edge detection --- p.41 / Chapter 3.3.4. --- Hough transform --- p.46 / Chapter 3.3.5. --- Line analysis --- p.49 / Chapter 3.4. --- Chapter summary --- p.51 / Chapter Chapter 4 --- Stand and stay --- p.52 / Chapter 4.1. --- Introduction --- p.52 / Chapter 4.2. --- Box matching method --- p.53 / Chapter 4.3. --- Image processing steps --- p.55 / Chapter 4.4. --- Experiment --- p.58 / Chapter 4.5. --- Chapter summary --- p.60 / Chapter Chapter 5 --- Conclusion and future works --- p.61 / Chapter 5.1. --- Contributions --- p.61 / Chapter 5.2. --- Future works --- p.62 / Bibliography --- p.63

University of Macau / Faculty of Science and Technology / Department of Electromechanical Engineering

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The present work proposes an algorithm to detect and identify the artifact signals produced by the concrete gestural actions of jaw clench and eyebrows raising in the electroencephalography (EEG) signal. Artifacts are signals that manifest in the EEG signal but do not come from the brain but from other sources such as flickering, electrical noise, muscle movements, breathing, and heartbeat. The proposed algorithm makes use of concepts and knowledge in the field of signal processing, such as signal energy, zero crossings, and block processing, to correctly classify the aforementioned artifact signals. The algorithm showed a 90% detection accuracy when evaluated in independent ten-second registers in which the gestural events of interest were induced, then the samples were processed, and the detection was performed. The detection and identification of these devices can be used as commands in a brain–computer interface (BCI) of various applications, such as games, control systems of some type of hardware of special benefit for disabled people, such as a chair wheel, a robot or mechanical arm, a computer pointer control interface, an Internet of things (IoT) control or some communication system. / Revisión por pares