abstract: Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices for real-time data analysis and decision-making in digital signal processing (DSP) applications. Most of the current efforts in DSP education focus on building tools to facilitate understanding of the mathematical principles. However, there is a disconnect between real-world data processing problems and the material presented in a DSP course. Sophisticated mobile interfaces and apps can potentially play a crucial role in providing a hands-on-experience with modern DSP applications to students. In this work, a new paradigm of DSP learning is explored by building an interactive easy-to-use health monitoring application for use in DSP courses. This is motivated by the increasing commercial interest in employing mobile phones for real-time health monitoring tasks. The idea is to exploit the computational abilities of the Android platform to build m-Health modules with sensor interfaces. In particular, appropriate sensing modalities have been identified, and a suite of software functionalities have been developed. Within the existing framework of the AJDSP app, a graphical programming environment, interfaces to on-board and external sensor hardware have also been developed to acquire and process physiological data. The set of sensor signals that can be monitored include electrocardiogram (ECG), photoplethysmogram (PPG), accelerometer signal, and galvanic skin response (GSR). The proposed m-Health modules can be used to estimate parameters such as heart rate, oxygen saturation, step count, and heart rate variability. A set of laboratory exercises have been designed to demonstrate the use of these modules in DSP courses. The app was evaluated through several workshops involving graduate and undergraduate students in signal processing majors at Arizona State University. The usefulness of the software modules in enhancing student understanding of signals, sensors and DSP systems were analyzed. Student opinions about the app and the proposed m-health modules evidenced the merits of integrating tools for mobile sensing and processing in a DSP curriculum, and familiarizing students with challenges in modern data-driven applications. / Dissertation/Thesis / M.S. Electrical Engineering 2013

Heart Rate Variability (HRV) analysis has been garnering attention from researchers due to its wide range of applications. Medical researchers have always been interested in Heart Rate (HR) and HRV analysis, but nowadays, investigators from variety of other fields are also probing the subject. For instance, variation in HR and HRV is connected to emotional arousal. Therefore, knowledge from the fields of affective computing and psychology, can be employed to devise machines that understand the emotional states of humans. Recent advancements in non-contact HR and HRV measurement techniques will likely further boost interest in emotional estimation through . Such measurement methods involve the extraction of the photoplethysmography (PPG) signal from the human's face through a camera. The latest approaches apply Independent Component Analysis (ICA) on the color channels of video recordings to extract a PPG signal. Other investigated methods rely on Eulerian Video Magnification (EVM) to detect subtle changes in skin color associated with PPG. The effectiveness of the EVM in HR estimation has well been established. However, to the best of our knowledge, EVM has not been successfully employed to extract HRV feature from a video of a human face. In contrast, ICA based methods have been successfully used for HRV analysis. As we demonstrate in this thesis, these two approaches for HRV feature extraction are highly sensitive to noise. Hence, when we evaluated them in indoor settings, we obtained mean absolute error in the range of 0.012 and 28.4. Therefore, in this thesis, we present two approaches to minimize the error rate when estimating physiological measurements from recorded facial videos using a standard camera. In our first approach which is based on the EVM method, we succeeded in extracting HRV measurements but we could not get rid of high frequency noise, which resulted in a high error percentage for the result of the High frequency (HF) component. Our second proposed approach solved this issue by applying ICA on the red, green and blue (RGB) colors channels and we were able to achieve lower error rates and less noisy signal as compared to previous related works. This was done by using a Buterworth filter with the subject's specific HR range as its Cut-Off. The methods were tested with 12 subjects from the DISCOVER lab at the University of Ottawa, using artificial lights as the only source of illumination. This made it a challenge for us because artificial light produces HF signals which can interfere with the PPG signal. The final results show that our proposed ICA based method has a mean absolute error (MAE) of 0.006, 0.005, 0.34, 0.57 and 0.419 for the mean HR, mean RR, LF, HF and LF/HF respectively. This approach also shows that these physiological parameters are highly correlated with the results taken from the electrocardiography (ECG).

The dissertation investigates the promises and challenges for application of cardiac signals in biometrics and affective computing, and noninvasive measurement of cardiac signals. We have mainly discussed two major cardiac signals: electrocardiogram (ECG), and photoplethysmogram (PPG). ECG and PPG signals hold strong potential for biometric authentications and identifications. We have shown that by mapping each cardiac beat from time domain to an angular domain using a limit cycle, intra-class variability can be significantly minimized. This is in contrary to conventional time domain analysis. Our experiments with both ECG and PPG signal shows that the proposed method eliminates the effect of instantaneous heart rate on the shape morphology and improves authentication accuracy. For noninvasive measurement of PPG beats, we have developed a systematic algorithm to extract pulse rate from face video in diverse situations using video magnification. We have extracted signals from skin patches and then used frequency domain correlation to filter out non-cardiac signals. We have developed a novel entropy based method to automatically select skin patches from face. We report beat-to-beat accuracy of remote PPG (rPPG) in comparison to conventional average heart rate. The beat-to-beat accuracy is required for applications related to heart rate variability (HRV) and affective computing. The algorithm has been tested on two datasets, one with static illumination condition and the other with unrestricted ambient illumination condition. Automatic skin detection is an intermediate step for rPPG. Existing methods always depend on color information to detect human skin. We have developed a novel standalone skin detection method to show that it is not necessary to have color cues for skin detection. We have used LBP lacunarity based micro-textures features and a region growing algorithm to find skin pixels in an image. Our experiment shows that the proposed method is applicable universally to any image including near infra-red images. This finding helps to extend the domain of many application including rPPG. To the best of our knowledge, this is first such method that is independent of color cues. / Ph. D. / The heart is an integral part of the human body. With every beat, the heart continuously pumps oxygen-enriched blood to providing fuel to our cells and thus enabling life. The heartbeat is initiated by electrical signals generated in the heart muscles. This electrical activity, which are often governed by our autonomic nervous system, can be measured directly by electrocardiogram (ECG) using advanced and often obtrusive instrumentation. Photoplethysmogram (PPG), on the other hand, measures how the blood volume changes and can be readily measured with inexpensive instrumentation at certain locations (e.g. at the fingertip). The ECG and PPG are widely used cardiac signals in medical science for diagnosis and health monitoring. But, these signals hold greater potential than just its medical diagnostic applications. In this work, we have mainly investigated if these signals can be used to identify an individual. Every human heart differs by their size, shape, locations inside body, and internal structure. This motivated us to represent the signals using a mathematical model and use machine learning algorithm to identify individual persons. We have discussed how our method improves the identification accuracy and can be used with current biometric methods like fingerprint in our phone. The measurement procedures of cardiac signals are often cumbersome and need instruments which may not be available outside medical facilities. Therefore, we have investigated alternative method of remote photoplethysmography (rPPG) that are relatively inexpensive and unobtrusive. In this dissertation, we have used face video of an individual to extract the heart rate information. The flow of blood causes small changes in the color of face skin. This is not visible to human eyes without digital magnification, but we have shown how knowledge of distinct behavior of human heart rate and use of advanced computer vision algorithms helped us to extract vital signals like heart rate with a significant accuracy. In addition, to measure rPPG using face video, we integrated a method for automatic detection of skin from images and videos. Existing skin detection methods depended on color information which is not always available within available video sources. We have developed a novel standalone skin detection method to show that it is not necessary to have color cues for skin detection. Our method relies on the context and the texture based appearance of skin. To the best of our knowledge, this is first such method that is independent of color cues. In summary, the dissertation investigates the promises and challenges for application of cardiac signals in biometrics and nonobtrusive measurement of cardiac signals using face video.

The electro-optic technique of measuring the cardiovascular pulse wave known as photoplethysmography (PPG) is clinically utilised for noninvasive characterisation of physiological components by dynamic monitoring of tissue optical absorption. There has been a resurgence of interest in this technique in recent years, driven by the demand for a low cost, compact, simple and portable technology for primary care and community-based clinical settings, and the advancement of computer-based pulse wave analysis techniques. PPG signal provides a means of determining cardiovascular properties during the cardiac cycle and changes with ageing and disease. This thesis focuses on the photoplethysmographic signal for cardiovascular assessment. The contour of the PPG pulse wave is influenced by vascular ageing. Contour analysis of the PPG pulse wave provides a rapid means of assessing vascular tone and arterial stiffness. In this thesis, the parameters extracted from the PPG pulse wave are examined in young adults. The results indicate that the contour parameters of the PPG pulse wave could provide a simple and noninvasive means to study the characteristic change relating to arterial stiffness. The pulsatile component of the PPG signal is due to the pumping action of the heart, and thus could reveal the circulation changes of a specific vascular bed. Heart rate variability (HRV) represents one of the most promising quantitative markers of cardiovascular control. Calculation of HRV from the peripheral pulse wave using PPG, called pulse rate variability (PRV), is investigated. The current work has confirmed that the PPG signal could provide basic information about heart rate (HR) and its variability, and highly suggests a good alternative to understanding dynamics pertaining to the autonomic nervous system (ANS) without the use of an electrocardiogram (ECG) device. Hence, PPG measurement has the potential to be readily accepted in ambulatory cardiac monitoring due to its simplicity and comfort. Noncontact PPG (NPPG) is introduced to overcome the current limitations of contact PPG. As a contactless device, NPPG is especially attractive for physiological monitoring in ambulatory units, NICUs, or trauma centres, where attaching electrodes is either inconvenient or unfeasible. In this research, a prototype for noncontact reflection PPG (NRPPG) with a vertical cavity surface emitting laser (VCSEL) as a light source and a high-speed PiN photodiode as a photodetector is developed. The results from physiological experiments suggest that NRPPG is reliable to extract clinically useful information about cardiac condition and function. In summary, recent evidence demonstrates that PPG as a simple noninvasive measurement offers a fruitful avenue for noninvasive cardiovascular monitoring. Key words: Photoplethysmography (PPG), Cardiovascular assessment, Pulse wave contour analysis, Arterial stiffness, Heart rate (HR), Heart rate variability (HRV), Pulse rate variability (PRV), Autonomic nervous system (ANS), Electrocardiogram (ECG).

<p>In recent years, there has been an increase in the popularity of wearable sensors such as electroencephalography (EEG) sensors, electromyography (EMG) sensors, gyroscopes, accelerometers, and photoplethysmography (PPG) sensors. This work is focused on PPG sensors, which are used to measure heart rate in real time. They are currently used in many commercial products such as Fitbit Watch and Muse Headband. Due to their low cost and relative implementation simplicity, they are easy to add to custom-built wearable devices.</p><p><br></p> <p>We built an Arduino-based wearable wrist sensor-kit that consists of a PPG sensor in addition to other low cost commercial biosensors to measure biosignals such as pulse rate, skin temperature, skin conductivity, and hand motion. The purpose of the sensor-kit is to analyze the effects of stress on students in a classroom based on changes in their biometric signals. We noticed some failures in the measured PPG signal, which could negatively affect the accuracy of our analysis. We conjectured that one of the causes of failure is movement. Therefore, in this thesis, we build automatic failure detection methods and use these methods to study the effect of movement on the signal.</p><p><br></p> <p>Using the sensor-kit, PPG signals were collected in two settings. In the first setting, the participants were in a still sitting position. These measured signals were manually labeled and used in signal analysis and method development. In the second setting, the signals were acquired in three different scenarios with increasing levels of activity. These measured signals were used to investigate the effect of movement on the reliability of the PPG sensor. </p><p><br></p> <p>Four types of failure detection methods were developed: Support Vector Machines (SVM), Deep Neural Networks (DNN), K-Nearest Neighbor (K-NN), and Decision Trees. The classification accuracy is evaluated by comparing the resulting Receiver Operating Characteristic (ROC) curves, Area Above the Curve (AAC), as well as the duration of failure and non-failure sequences. The DNN and Decision Tree results are found to be the most promising and seem to have the highest error detection accuracy. </p> <p> </p> <p>The proposed classifiers are also used to assess the reliability of the PPG sensor in the three activity scenarios. Our findings indicate that there is a significant presence of failures in the measured PPG signals at rest, which increases with movement. They also show that it is hard to obtain long sequences of pulses without failure. These findings should be taken into account when designing wearable systems that use heart rate values as input.</p>

This thesis describes the design and practical realization of a prototype mobile, battery-powered device that is able to periodically recording electrocardiogram and fotopletysmogram or blood pressure. Data from these periodic scanning is processed by microcontroller and stored on a memory card which is part of the proposed device. The device can be connected to a PC via USB like a virtual serial port.

No description available.

Heart rate can say a lot about a person's health. While most conventional methods for heart rate measurement require contact with the subject, these are not always applicable. In this thesis, a non-invasive method for pulse detection is implemented and analyzed. Different signals from the color of the forehead—including the green channel, the hue channel and different ICA and PCA components—are inspected, and their resulted heart rates are weighted together according to the significance of their FFT peaks. The system is tested on videos with different difficulties regarding the amount of movement and setting of the scene. The results show that the approach of weighting measurements from different signals together has great potential. The system in this thesis, however, does not perform very well on videos with a lot of movement because of motion noise. Though, with better, less noisy signals, good results can be expected. / En människas puls säger en hel del om dennes hälsa. För att mäta pulsenanvänds vanligtvis metoder som vidrör människan, vilket iblandär en nackdel. I det här examensarbetet tas en metod för pulsmätningpå avstånd fram, som endast använder klipp från en vanlig videokamera. Färgen i pannan mäts och utifrån den genereras flera signalersom analyseras, vilket resulterar i olika mätvärden för pulsen. Genomatt värdera dessa mätvärden med avseende på hur tydliga signalernaär, beräknas ett viktat medelvärde som ett slutgiltigt estimat på medelpulsen. Metoden testas på videoklipp med varierande svårighetsgrad,beroende på hur mycket rörelser som förekommer och på vilketavstånd från kameran försökspersonen står. Resultaten visar att metodenhar mycket god potential och att man kan man förvänta sig finaresultat med bättre, mindre brusiga signaler.

Environmentally friendly lubricants are in demand in response to the rising concerns and restrictive legislation that surround the use of mineral oil lubricants. One area of importance is understanding the service life of the environmentally friendly base oils of these non-toxic, biodegradable and renewable alternatives. The service life of a lubricant is directly influenced by its degradation behavior, especially oxidation.  In this research, selected environmentally friendly base oils; Glycerol, Rapeseed oil, Polypropylene glycol, Polyethylene glycol, Bis(di-2-ethyl hexyl) sebacate, Squalene and reference mineral oil; paraffin,  were subjected to thermo-oxidative ageing at 150oC. The changes in the chemical structures of the samples were followed using Spectroscopic, chromatographic, rheological and corrosivity studies. Tribological tests were also carried out to quantify the changes in these lubricants. The results obtained showed that the thermal oxidation affected the physicochemical properties and the lubricating ability of the base oils. However, each base oil degraded distinctly to the accelerated ageing conditions.

Made available in DSpace on 2015-04-11T13:52:37Z (GMT). No. of bitstreams: 1 Francisco de Assis Mourao.pdf: 556410 bytes, checksum: 41f775f283a51ff5b6d559081c88f3e4 (MD5) Previous issue date: 2012-12-29 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Some municipalities of Amazonas, in Brazil among others, have in the explotation of mineral resources one significant source of tax revenue. However, these recipes do not appear compatible with the volume of resources extracted from their basements when the transfers occur ICMS. These transfers should meet exactly the concept of Value Added enshrined by economic theory, which underpins the National Accounts of the economy of a country. Since this mismatch occurs, then, the concept deserves to be revised to incorporate solely the concept of cost of exploitation of natural resources dormant when they are inserted in the production process. Taking this assumption into account, the aim of this research was to build a Methodological towards providing a better understanding and way of calculating the value added when referring to natural resources latent. In terms of specific objectives aimed at: a) Conduct a critical literature review on the concept of added value and fundamentals that guide their use for purposes of redistribution of Tax on Goods and Services (ICMS), and must be passed municipalities in the amount of the installment scheduled constitutionally b) Providing a broader view about the latent natural resources, in order to clarify their understanding as factors of production, c) Building a methodology for calculating the value added in terms of the Constitution and present a case study, stating the application of the concepts covered in the proposed methodology, developed in a city in the state of Amazonas, specifically the city of Coari. For the conformation of the methodology started with the theoretical framework embodied in the need to measure all the economic system, especially in the case of the wealth generated in any region. For this reason the concept of Value- Added VA being the core element of the Theory of Social Accounting, was used for the empirical verification of the actions, as this concept is effectively used to organize income statistics regional and / or national level. Based on a methodology similar to the production function of the neo-classical economists approach the VA was performed using the concept of Productive Process General-PPG, which established that the cost of extraction of natural resources Latent-cortex is embodied VA itself in these raw materials latent result of the sum of all costs of the factors applied in the extraction of such resources, yet its commercialization extent that such materials are to be valued at market. It is the end that the VA (at cost factor cf) of the products from underground Coari / AM was computed by the method Latent Natural Resources, 8.62% participates in the formation of VA (at the cost of factors cf) of the state of Amazonas, and that such interest from the point of view of IBGE, results in only 2.77%. This difference of 170% between the macro-regional indicators in question, if considered, would fix a social injustice in favor of Coari. / Alguns municípios do Amazonas, entre outros no Brasil, têm na exploração de recursos minerais fonte expressiva de receitas fiscais. No entanto, estas receitas não se mostram compatíveis com o volume de recursos extraídos de seus subsolos, quando ocorrem os repasses do ICMS. Estes repasses deveriam atender, exatamente, o conceito de Valor Adicionado, consagrado pela Teoria Econômica, que embasa a Contabilidade Nacional da economia de um país. Dado que ocorre essa incompatibilidade, então, o conceito merece ser revisado no sentido de incorporar tão somente o conceito de custo da exploração dos recursos naturais latentes, quando estes se inserem no processo produtivo. Levando essa hipótese em conta, o objetivo desta pesquisa foi construir um adendo metodológico no sentido de dar uma melhor compreensão e forma de cálculo do Valor Adicionado quando se referir aos recursos naturais latentes. No plano dos objetivos específicos visa-se: a) Realizar uma revisão bibliográfica crítica sobre o conceito de valor adicionado e os fundamentos que norteiam sua utilização para fins de redistribuição do Imposto sobre Circulação de Mercadorias e Serviços (ICMS), e que devem ser repassadas aos municípios no montante da parcela constitucionalmente prevista; b) Fornecer uma visão ampliada acerca dos recursos naturais latentes, de modo a esclarecer o seu entendimento como fatores de produção; c) Construir uma metodologia para cálculo do valor adicionado nos termos da Constituição Federal e apresentar um estudo de caso, onde conste a aplicação dos conceitos abordados na metodologia proposta, desenvolvido em um Município do Estado do Amazonas, mais especificamente o Município de Coari. Para a conformação da metodologia partiu-se do marco teórico consubstanciado na necessidade de tudo medir no sistema econômico, mormente em se tratando da riqueza gerada em qualquer região. Por essa razão o conceito de Valor Adicionado-VA sendo elemento basilar da Teoria da Contabilidade Social foi utilizado para a verificação das ações empíricas, uma vez que tal conceito é efetivamente utilizado para organizar as estatísticas da renda regional e/ou nacional. Com base numa metodologia análoga à função de produção dos economistas neo-clássicos, a abordagem do VA foi realizada utilizando-se o conceito de Processo Produtivo Geral-PPG, o que permitiu estabelecer que o Custo da Extração dos Recursos Naturais Latentes-CTEX se consubstancia no próprio VA dessas matérias primas latentes, fruto do somatório de todos os custos dos fatores aplicados na extração dos mencionados recursos, até o momento de sua comercialização, ponto em que tais materiais passam a ser valorados em mercado. Verificou-se ao final, que o VA (ao custo dos fatores-cf) dos produtos do subsolo de Coari/Am, se calculado pela metodologia do Recursos Naturais Latentes, participa com 8,62% para a formação do VA(ao custo dos fatores cf) do Estado do Amazonas, sendo que, essa mesma participação do ponto de vista do IBGE, resulta em apenas 2,77%. Essa diferença de 170% entre os indicadores macrorregionais em questão, se fosse levada em consideração, repararia uma injustiça social a favor de Coari.