Improving Displacement Measurement for Evaluating Longitudinal Road Profiles

2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper introduces a half-wavelength peak matching (HWPM) model, which improves the accuracy of vehicle based longitudinal road profilers used in evaluating road unevenness and mega-textures. In this application, the HWPM model is designed for profilers which utilize a laser displacement sensor with an accelerometer for detecting surface irregularities. The process of converting acceleration to displacement by double integration (which is used in most rofilers) is error-prone, and although there are techniques to minimize the effect of this error, this paper proposes a novel approach for improving the generated road profile results. The technique amends the vertical displacement derived from the accelerometer samples, by using data from the laser displacement sensor as a reference. The vehicle based profiler developed for this experiment (which uses the HWPM model) shows a huge improvement in detected longitudinal irregularities when compared with pre-processed results, and uses a 3-m rolling straight edge as a benchmark.Peer reviewe

Prototipo basado en sensores inerciales para el seguimiento de la actividad física

En el transcurso de esta tesis, se presentará el desarrollo de un prototipo basado en sensores inerciales comerciales de 6DoF, que permite la captura de movimiento y su posterior visualización, como la rotación de algunas articulaciones a partir de la información del acelerómetro y giroscopio. La fase de desarrollo del hardware presenta los componentes de adquisición de las señales inerciales mediante el protocolo I2C entre los sensores y la tarjeta madre, utilizando la tarjeta de desarrollo STM32 Minimo, realizando el filtrado y procesamiento digital de éstas señales; en la fase de visualización, se realiza una interfaz gráfica en Matlab® (GUIDE), que se enlaza por bluetooth al prototipo; lo que permite analizar las señales entregadas por los sensores. Adicionalmente, con el fin de hacer pruebas de funcionamiento del prototipo desarrollado, se realizó una prueba piloto en el Laboratorio de Análisis de Movimiento de la UAM®, en el que se hizo una comparación de las cámaras de OptiTrack y los sensores inerciales. A través de la prueba piloto, se comprueba la fiabilidad del prototipo que está entregando ángulos de rotación (Yaw, Pitch, Roll) en los segmentos corporales.In the course of this thesis, the development of a prototype based on commercial inertial sensors of 6DoF will be presented, which allows the capture of movement and its subsequent visualization, such as the rotation of some articulations from the information of the accelerometer and gyroscope. The hardware development phase presents the acquisition components of the inertial signals through the I2C protocol between the sensors and the motherboard, using the STM32 Minimo development card, performing the filtering and digital processing of these signals; in the visualization phase, a graphical interface is made in Matlab® (GUIDE), which is linked by bluetooth to the prototype; what allows to analyze the signals delivered by the sensors. Additionally, in order to test the performance of the developed prototype, a pilot test was carried out in the Motion Analysis Laboratory of the UAM®, in which a comparison was made of the OptiTrack cameras and the inertial sensors. Through the pilot test, the fability of the prototype is verified, which is delivering angles of rotation (Yaw, Pitch, Roll) in the body segments

Evaluation of Pavement Roughness and Vehicle Vibrations for Road Surface Profiling

The research explores aspects of road surface measurement and monitoring, targeting some of the main challenges in the field, including cost and portability of high-speed inertial profilers. These challenges are due to the complexities of modern profilers to integrate various sensors while using advanced algorithms and processes to analyse measured sensor data. Novel techniques were proposed to improve the accuracy of road surface longitudinal profiles using inertial profilers. The thesis presents a Half-Wavelength Peak Matching (HWPM) model, designed for inertial profilers that integrate a laser displacement sensor and an accelerometer to evaluate surface irregularities. The model provides an alternative approach to drift correction in accelerometers, which is a major challenge when evaluating displacement from acceleration. The theory relies on using data from the laser displacement sensor to estimate a correction offset for the derived displacement. The study also proposes an alternative technique to evaluating vibration velocity, which improves on computational factors when compared to commonly used methods. The aim is to explore a different dimension to road roughness evaluation, by investigating the effect of surface irregularities on vehicle vibration. The measured samples show that the drift in the displacement calculated from the accelerometer increased as the vehicle speed at which the road measurement was taken increased. As such, the significance of the HWPM model is more apparent at higher vehicle speeds, where the results obtained show noticeable improvements to current techniques. All results and analysis carried out to validate the model are based on real-time data obtained from an inertial profiler that was designed and developed for the research. The profiler, which is designed for portability, scalability and accuracy, provides a Power Over Ethernet (POE) enabled solution to cope with the demand for high data transmission rates.

Desenvolvimento de um dispositivo wearable aplicado para apoio ao treinamento em tiro com arco

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2015.Tiro com arco é um dos esportes mais antigos da humanidade ainda praticados e está estreitamente ligado ao desenvolvimento da civilização. Na atualidade, o tiro com arco é um esporte olímpico que consiste na habilidade de usar um arco para atirar flechas em um alvo com precisão, controle, foco, repetitividade e determinação. As principais habilidades para atingir alta performance em tiro com arco são: o controle de fatores psicofisiológicos, como ansiedade, estresse e frequência cardíaca e a repetitividade da técnica de tiro, que pode ser observada na ativação dos músculos do antebraço dominante no tiro e na repetição de uma posição postural entre cada tiro. O objetivo deste trabalho está focado no desenvolvimento de um dispositivo wearable open source e open hardware para o apoio ao treinamento e avaliação de atletas da modalidade de tiro com arco, utilizando o registro da Variabilidade de Frequência Cardíaca, Envoltorio da Eletromiografia e Posições Posturais do atleta durante o treinamento. A motivação deste projeto encontra-se na necessidade de pesquisadores e esportistas de terem acesso a dispositivos que possam monitorar e avaliar a evolução das habilidades dos atletas. São apresentados tanto o projeto, quanto a prototipação do dispositivo desde a etapa da concepção teórica até a aplicação do teste piloto com atletas voluntários. O dispositivo portátil foi configurado para estabelecer uma comunicação Wi-Fi com um PC de controle e assim gerenciar por meio de uma interface gráfica a coleta e registro dos sinais eletrofisiológicos e biomecânicos durante o treinamento do atleta. O sistema está preparado para armazenar os sinais registrados em arquivos de texto. Um teste piloto foi aplicado para avaliar o funcionamento do dispositivo durante o treinamento de atletas. O teste demostrou que o dispositivo desenvolvido é uma ferramenta promissora para apoiar e avaliar o treinamento de atletas da modalidade de tiro com arco.Abstract : Archery is one of the oldest and still practiced sports in human history that is closely linked to the development of civilization. Nowadays, archery is an Olympic sport that consists in the ability to use a bow to shoot arrows at a target with precision, control, focus, repeatability and determination. The key skills to achieve high performance in archery are the control of psychophysiological factors such as anxiety, stress and heart rate and repeatability of the shooting technique, which can be observed in the activation of the forearm dominant muscles at shoots and the repetition of a postural position during each shot. The aim of this work was to develop a wearable open source and open hardware device to support the training and evaluation of athletes shooting technique with arch. It is based on the registration of Heart Rate Variability, Linear electromyogram Envelope and postural positions of the athlete during training. This project was motivated by the needs of the researchers and athletes to benefit from devices that could monitor and evaluate the development of the skills of athletes during training. This work presents the design and prototyping of the device from the stage of theoretical conception to implementation of pilot testing with volunteer athletes. The portable device is configured to establish a Wi-Fi link with a PC control and thus manage by means of a graphical interface to collect and record the electrophysiological and biomechanical signals during the athlete's training. The pilot test was applied to assess the functioning of the device during athletes? training sessions. The pilot tests demonstrated that the developed device is a promising tool to support and evaluate the training of athletes shooting technique and performance

Studies on Sensor Aided Positioning and Context Awareness

This thesis studies Global Navigation Satellite Systems (GNSS) in combination with sensor systems that can be used for positioning and obtaining richer context information. When a GNSS is integrated with sensors, such as accelerometers, gyroscopes and barometric altimeters, valuable information can be produced for several applications; for example availability or/and performance of the navigation system can be increased. In addition to position technologies, GNSS devices are integrated more often with different types of technologies to fulfil several needs, e.g., different types of context recognition. The most common integrated devices are accelerometer, gyroscope, and magnetometer but also other sensors could be used.More specifically, this thesis presents sensor aided positioning with two satellite signals with altitude assistance. The method uses both pseudorange and Doppler measurements. The system is required to be stationary during the process and a source of altitude information, e.g., a MEMS barometer, is needed in addition to a basic GNSS receiver. Authentic pseudorange and Doppler measurements with simulated altitude were used used to test the algorithm. Results showed that normally the accuracy of couple of kilometers is acquired. Thesis also studies on what kind of errors barometric altimeter might encounter especially in personal positioning. The results show that barometers in differential mode provide highly accurate altitude solution (within tens of centimeters), but local disturbances in pressure need to be acknowledged in the application design. For example, heating, ventilating, and air conditioning in a car can have effect of few meters. Thus this could cause problems if the barometer is used as a altimeter for under meter-level positioning or navigation.We also explore methods for sensor aided GNSS systems for context recognition. First, the activity and environment recognition from mobile phone sensor and radio receiver data is investigated. The aim is in activity (e.g., walking, running, or driving a vehicle) and environment (e.g., street, home, or restaurant) detection. The thesis introduces an algorithm for user specific adaptation of the context model parameters using the feedback from the user, which can provide a confidence measure about the correctness of a classification. A real-life data collection campaign validate the proposed method. In addition, the thesis presents a concept for automated crash detection to motorcycles. In this concept, three different inertial measurement units are attached to the motorist’s helmet, torso of the motorist, and to the rear of the motor cycle. A maximum a posteriori classifier is trained to classify the crash and normal driving. Crash dummy tests were done by throwing the dummy from different altitudes to simulate the effect of crash to the motorist and real data is collected by driving the motorcycle. Preliminary results proved the potential of the proposed method could be applicable in real situations. In all the proposed systems in this thesis, knowledge of the context can help the positioning system, but also positioning system can help in determining the context