Metodologia para aquisição de sinal elétrico e tratamento de dados em reômetros servo controlados.

A ciência na qual se estuda a deformação de um fluido no qual é aplicada uma tensão de cisalhamento é conhecida como reologia e o equipamento utilizado para a realização dos ensaios é chamado de reômetro. Devido a impraticabilidade de uso de reômetros comerciais, diversos pesquisadores desenvolveram reômetros capazes de analisar suspensões de macropartículas, baseados nos mesmos princípios de funcionamento dos equipamentos já existentes. Em alguns casos, a medição do torque do motor é realizada pela aquisição da tensão, uma vez que esta é proporcional ao torque. Entretanto, para melhor compreensão do resultado e para evitar a possibilidade de conclusões precipitadas, vê-se necessária correta interpretação do sinal elétrico, precisando avaliar qual frequência do sinal é relevante para o ensaio e, também, qual a melhor taxa de amostragem. Além da aquisição, para que o ensaio reológico seja realizado com precisão, é indispensável ótimo controle da taxa ou tensão do motor e uma alternativa é a utilização de um servomotor e um servoconversor. No caso desse ser comercial é essencial saber configurá-lo. Para facilitar o usuário leigo, alguns pesquisadores desenvolveram softwares para controle do equipamento e análise dos dados. Assim, o presente trabalho tem como objetivo propor uma metodologia para compreender o sinal aquisitado de um reômetro servo controlado e desenvolvimento do software de análise para o tratamento dos dados obtidos a partir de ensaios reológicos. Verificou-se a melhor configuração do servocontrolador, a melhor taxa de amostragem, de no mínimo 20 amostras/segundo, e, também, desenvolveu-se um filtro digital passa-baixa do tipo FIR para remover a frequência indesejada. Além disso, foi desenvolvido um software utilizando uma rotina em Matlab e uma interface gráfica do usuário (Graphical User Interface - GUI), para o pós-processamento dos dados para auxiliar o usuário leigo no tratamento e interpretação do resultado, que se mostrou eficaz.

Exploring the spatial variation of tides for seafloor mapping data corrections

Senior thesis written for Oceanography 445

Variability in heart rate estimates from different pulse oximeters

Since its introduction, pulse oximetry has become a conventional clinical measure. Besides being arterial blood oxygen saturation (SpO2) measure, pulse oximeters can be used for other cardiovascular measurements, like heart rate (HR) estimations, derived from its photo plethysmographic (PPG) signals. The temporal coherence of the PPG signals and thereby HR estimates are heavily dependent on its minimal phase variability. A Masimo SET Rad-9TM, Novametrix Oxypleth and a custom designed PPG system were investigated for their relative phase variation. R-R intervals from electro-cardiogram (ECG) were recorded concurrently as reference. PPG signals obtained from the 3 systems were evaluated by comparing their respective beat-to-beat (B-B) intervals with the corresponding R-R estimates during a static test. For their relative B-B comparison to the ECG, Novametrix system differed 0.680.52% (p

Neuromuscular response to whole body vibration treatment during static and dynamic squat exercises

The impact of whole body vibrations (vibration stimulus mechanically transferred to the body) on muscular activity and neuromuscular response has been widely studied but without standard protocol and by using different kinds of exercises and parameters. In this study, we investigated how whole body vibration treatments affect electromyographic signal of rectus femoris during static and dynamic squat exercises. The aim was the identification of squat exercise characteristics useful to maximize neuromuscular activation and hence progress in training efficacy. Fourteen healthy volunteers performed both static and dynamic squat exercises without and with vibration treatments. Surface electromyographic signals of rectus femoris were recorded during the whole exercise and processed to reduce artifacts and to extract root mean square values. Paired t-test results demonstrated an increase of the root mean square values (p<0.05) in both static and dynamic squat exercises with vibrations respectively of 63% and 108%. For each exercise, subjects gave a rating of the perceived exertion according to the Borg's scale but there were no significant changes in the perceived exertion rate between exercises with and without vibration. Finally, results from analysis of electromyographic signals identified the static squat with WBV treatment as the exercise with higher neuromuscular system response. © 2012 IEEE.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníese por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Adquisición y análisis de señales cerebrales utilizando el dispositivo MindWave

La actividad cerebral puede ser monitoreada mediante la electroencefalografía y utilizada como un indicador bioeléctrico. Este articulo muestra como un dispositivo de bajo costo y fácil acceso puede utilizarse para el desarrollo de aplicaciones basadas en interfaces cerebro-computador (BCI). Los resultados obtenidos muestran que el dispositivo MindWave puede efectivamente utilizarse para la adquisición de señales relacionadas a la actividad cerebral en diversas actividades cerebrales bajo la influencia de diversos estímulos. Se propone además el uso de la transformada Wavelet para el acondicionamiento de las señales EEG con el objetivo de utilizar algoritmos de inteligencia artificial y técnicas de reconocimiento de patrones para distinguir respuestas cerebrales.

Intelligent Sensor Systems for Structural Health Monitoring Applications

The convergence between the recent developments in sensing technologies, data science, signal processing and advanced modelling has fostered a new paradigm to the Structural Health Monitoring (SHM) of engineered structures, which is the one based on intelligent sensors, i.e., embedded devices capable of stream processing data and/or performing structural inference in a self-contained and near-sensor manner. To efficiently exploit these intelligent sensor units for full-scale structural assessment, a joint effort is required to deal with instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing. In this framework, the main goal of this Thesis is to tackle the multi-faceted nature of the monitoring process, via a full-scale optimization of the hardware and software resources involved by the {SHM} system. The pursuit of this objective has required the investigation of both: i) transversal aspects common to multiple application domains at different abstraction levels (such as knowledge distillation, networking solutions, microsystem {HW} architectures), and ii) the specificities of the monitoring methodologies (vibrations, guided waves, acoustic emission monitoring). The key tools adopted in the proposed monitoring frameworks belong to the embedded signal processing field: namely, graph signal processing, compressed sensing, ARMA System Identification, digital data communication and TinyML.

Design of a wearable platform for PPG analysis with multicore low-power processor

Photoplethysmography (PPG) sensors allow for noninvasive and comfortable heart-rate (HR) monitoring, suitable for compact wearable devices. However, PPG signals collected from such devices often suffer from corruption caused by motion artifacts. This is typically addressed by combining the PPG signal with acceleration measurements from an inertial sensor. Recently, different energy-efficient deep learning approaches for heart rate estimation have been proposed. To test these new solutions, in this work, we developed a highly wearable platform (42mm x 48 mm x 1.2mm) for PPG signal acquisition and processing, based on GAP9, a parallel ultra low power system-on-chip featuring nine cores RISC-V compute cluster with neural network accelerator and 1 core RISC-V controller. The hardware platform also integrates a commercial complete Optical Biosensing Module and an ARM-Cortex M4 microcontroller unit (MCU) with Bluetooth low-energy connectivity. To demonstrate the capabilities of the system, a deep learning-based approach for PPG-based HR estimation has been deployed. Thanks to the reduced power consumption of the digital computational platform, the total power budget is just 2.67 mW providing up to 5 days of operation (105 mAh battery).

Electromyographic validation of the pectoralis major and deltoideus anterior muscles in inverted fyling exercises with loads

Inverted flying exercise with external loads of 25, 50, 75 and 100% of each individual maximum load in the pectoralis major and deltoideus anterior muscles was electromyographically analyzed in eleven male volunteers, using surface electrodes MEDI-TRACE-200 connected to a biological signals acquisition module coupled to a PC/AT computer. Electromyographic signals were processed and the effective values obtained were standardized through maximum voluntary isometric contraction. When the concentric phase of each muscle with the same load was statistically compared with the eccentric phase, it was observed that for all loads all the muscles presented significant electromyographic difference, and that the concentric phase was always higher. By analyzing the different loads for each muscle, it was noticed that in the concentric phase all the muscles presented significant electromyographic activity, being it higher with maximum load. When the effect of each load on different muscle in the concentric and eccentric phases was analyzed, the muscles presented a distinct activity profile.

Physical data acquisition and PWM signal generation using a microcontroller allowing control for DC motor rotation speed

El objetivo de este proyecto es diseñar un sistema capaz de controlar la velocidad de rotación de un motor DC en función del valor de temperatura obtenido de un sensor. Para ello se generará con un microcontrolador una señal PWM, cuyo ciclo de trabajo estará en función de la temperatura medida. En lo que respecta a la fase de diseño, hay dos partes claramente diferenciadas, relativas al hardware y al software. En cuanto al diseño del hardware puede hacerse a su vez una división en dos partes. En primer lugar, hubo que diseñar la circuitería necesaria para adaptar los niveles de tensión entregados por el sensor de temperatura a los niveles requeridos por ADC, requerido para digitalizar la información para su posterior procesamiento por parte del microcontrolador. Por tanto hubo que diseñar capaz de corregir el offset y la pendiente de la función tensión-temperatura del sensor, a fin de adaptarlo al rango de tensión requerido por el ADC. Por otro lado, hubo que diseñar el circuito encargado de controlar la velocidad de rotación del motor. Este circuito estará basado en un transistor MOSFET en conmutación, controlado mediante una señal PWM como se mencionó anteriormente. De esta manera, al variar el ciclo de trabajo de la señal PWM, variará de manera proporcional la tensión que cae en el motor, y por tanto su velocidad de rotación. En cuanto al diseño del software, se programó el microcontrolador para que generase una señal PWM en uno de sus pines en función del valor entregado por el ADC, a cuya entrada está conectada la tensión obtenida del circuito creado para adaptar la tensión generada por el sensor. Así mismo, se utiliza el microcontrolador para representar el valor de temperatura obtenido en una pantalla LCD. Para este proyecto se eligió una placa de desarrollo mbed, que incluye el microcontrolador integrado, debido a que facilita la tarea del prototipado. Posteriormente se procedió a la integración de ambas partes, y testeado del sistema para comprobar su correcto funcionamiento. Puesto que el resultado depende de la temperatura medida, fue necesario simular variaciones en ésta, para así comprobar los resultados obtenidos a distintas temperaturas. Para este propósito se empleó una bomba de aire caliente. Una vez comprobado el funcionamiento, como último paso se diseñó la placa de circuito impreso. Como conclusión, se consiguió desarrollar un sistema con un nivel de exactitud y precisión aceptable, en base a las limitaciones del sistema. SUMMARY: It is obvious that day by day people’s daily life depends more on technology and science. Tasks tend to be done automatically, making them simpler and as a result, user life is more comfortable. Every single task that can be controlled has an electronic system behind. In this project, a control system based on a microcontroller was designed for a fan, allowing it to go faster when temperature rises or slowing down as the environment gets colder. For this purpose, a microcontroller was programmed to generate a signal, to control the rotation speed of the fan depending on the data acquired from a temperature sensor. After testing the whole design developed in the laboratory, the next step taken was to build a prototype, which allows future improvements in the system that are discussed in the corresponding section of the thesis.

Electromyographic Study of the Masseter Muscle after Lower Third Molar Surgery

Third molar extraction surgery is one of the most frequently performed procedures in the areas of buccal-maxillofacial traumatology and surgery. The post-surgery evolution was evaluated based on the clinical evidence obtained so far. The objective of this study was to analyze the post-surgery clinical evolution of the masseter muscle by means of surface electromyography, to evaluate muscle activity. Four analyses were performed: one pre-surgery, to register the normal activity, and three in post-surgery: on the 7(th), 14(th) and 21(st) postoperative days, in a sample of 30 patients. On the 21(st) day, there was near normal recovery of the electrical signal of the masseter in women, but in men this activity did not reach normal levels. Surface electromyography is a safe and reliable tool for post-surgery evolution control of masseter function.

Increased EMG response following electromyographic biofeedback treatment of rectus femoris muscle after spinal cord injury

Objective To study increases in electromyographic (EMG) response from the right and left rectus femoris muscles of individuals with long-term cervical spinal cord injuries after EMG biofeedback treatment. Design Repeated measure trials compared EMG responses before and after biofeedback treatment in patients with spinal cord injuries. Main outcome measures The Neuroeducator was used to analyse and provide feedback of the EMG signal and to measure EMG response. Setting Department of Traumatic Orthopaedics, School of Medicine, University of Sao Paulo, Brazil. Participants Twenty subjects (three men and 17 women), between 21 and 49 years of age, with incomplete spinal cord injury at level C6 or higher (range C2 to C6). Of these subjects, 10 received their spinal cord injuries from motor vehicle accidents, one from a gunshot, five from diving, three from falls and one from spinal disc herniation. Results Significant differences were found in the EMG response of the right rectus femoris muscle between pre-initial (T1), post-initial (T2) and additional (T3) biofeedback treatment with the subjects in a sitting position [mean (standard deviation) T1: 26 mu V (29); T2: 67 mu V (50); T3: 77 mu V (62)]. The mean differences and 95% confidence intervals for these comparisons were as follows: T1 to T2, -40.7 (-53.1 to -29.4); T2 to T3, -9.6 (-26.1 to 2.3). Similar differences were found for the left leg in a sitting position and for both legs in the sit-to-stand condition. Conclusions The EMG responses obtained in this study showed that treatment involving EMG biofeedback significantly increased voluntary EMG responses from right and left rectus femoris muscles in individuals with spinal cord injuries. (C) 2010 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Masticatory process in individuals with maxillary and mandibular osteoporosis: electromyographic analysis

The masseter and temporal muscles of patients with maxillary and mandibular osteoporosis were submitted to electromyographic analysis and compared with a control group. In conclusion, individuals with osteoporosis did not show significantly lower masticatory cycle performance and efficiency compared to the control group during the proposal mastications. This study aimed to examine electromyographically the masseter and temporal muscles of patients with maxillary and mandibular osteoporosis and compare these patients with control patients. Sixty individuals of both genders with an average age of 53.0 +/- 5 years took part in the study, distributed in two groups with 30 individuals each: (1) individuals with osteoporosis; (2) control patients during the habitual and non-habitual mastication. The electromyographic apparel used was a Myosystem-BR1-DataHomins Technology Ltda., with five channels of acquisition and electrodes active differentials. Statistical analysis of the results was performed using SPSS version 15.0 (Chicago, IL, USA). The result of the Student`s t test indicated no significant differences (p > 0.05) between the normalized values of the ensemble average obtained in masticatory cycles in both groups. Based on the results of this study, it was concluded that individuals with osteoporosis did not show significantly lower masticatory cycle performance and efficiency compared to control subjects during the habitual and non-habitual mastications. This result is very important because it demonstrates the functionality of the complex physiological process of mastication in individuals with osteoporosis at the bones that compose the face.

Changes in muscle recruitment patterns during skill acquisition

The control of movement is predicated upon a system of constraints of musculoskeletal and neural origin. The focus of the present study was upon the manner in which such constraints are adapted or superseded during the acquisition of motor skill. Individuals participated in five experimental sessions, ill which they attempted to produce abduction-adduction movements of the index finger in time with an auditory metronome. During each trial, the metronome frequency was increased in eight steps from an individually determined base frequency. Electromyographic (EMC) activity was recorded from first dorsal interosseous (FDI), first volar interosseous (FVI), flexor digitorum superficialis (FDS), and extensor digitorum communis (EDC) muscles. The movements produced on the final day of acquisition more accurately matched the required profile, and exhibited greater spatial and temporal stability, than those generated during initial performance. Tn the early stages of skill acquisition, an alternating pattern of activation in FDI and FVI was maintained, even at the highest frequencies. Tn contrast, as the frequency of movement was increased, activity in FDS and EDC was either tonic or intermittent. As learning proceeded, alterations in recruitment patterns were expressed primarily in the extrinsic muscles (EDC and FDS). These changes took the form of increases in the postural role of these muscles, shifts to phasic patterns of activation, or selective disengagement of these muscles. These findings suggest that there is considerable flexibility in the composition of muscle synergies, which is exploited by individuals during the acquisition of coordination.