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The Hemispherical Resonating Gyroscope (HRG) is an inertial sensor, flagship of Safran’s industry. When exiting the assembly line, it has its own physical flaws. In order to identify and correct them, operators perform several tests on the sensor: this process corresponds to the calibration step of the sensors. The latter is done by a Matlab calibration bench, which allows to calculate compensation polynomial functions, which are then included in the algorithms of the sensor’s implementation card. However, the link between the calculated functions and the sensor’s flaws is not obvious and therefore, it is impossible to check their truthfulness without further verification. In this document, an interfacing method between a calibration bench and a virtual HRG, modeled in Simulink, has been described. After presenting the sensor’s capabilities, several interfacing methods are discussed, before keeping the more dynamical one, based on object oriented programming and the implementation of a time continuity between Simulink data recordings. Such interfacing allows for the simulation of the behavior of a gyroscope during calibration, and the comparison of these results to the ones obtained on real sensors. This comparison highlighted a certain consistency between the results and also several flaws caused by the interfacing. Particularly, the fact that the signal discretization has a significant impact on the errors. Moreover, one can notice that the simulation time is significantly longer than the calibration time and suggests that the interfacing time may require optimization of its efficiency. / HRG är en tröghetssensor som är flaggskeppet inom Safrans industri. När sensorn lämnar monteringslinjen är den inte felfri. För att identifiera och kompensera för dessa fel utför operatörerna flera tester på sensorn, i flera olika kalibreringssteg. De senare görs med hjälp av en Matlab-kalibreringsbänk, som gör det möjligt att beräkna kompensationspolynomfunktioner, som sedan implementeras i algoritmerna på sensorns implementeringskort. Kopplingen mellan de beräknade funktionerna och sensorns fel är dock inte uppenbar och därför är det omöjligt att kontrollera deras noggrannhet utan ytterligare kontroller. I detta dokument beskrivs en gränssnittsmetod mellan en kalibreringsbänk och en virtuell HRG, modellerad i Simulink. Efter att ha presenterat sensorns funktion har flera gränssnittsmetoder studerats, innan man valde den mer dynamiska, baserad på objektorienterad programmering och implementeringen av en tidskontinuitet mellan Simulinkdatainspelningar. Ett sådant gränssnitt gjorde det möjligt att få vissa resultat som simulerar gyroskopets beteende under en kalibrering och att jämföra dessa resultat med dem som erhållits på verkliga sensorer. Jämförelsen visade på en viss överensstämmelse mellan resultaten, men också på flera brister som orsakats av gränssnittet. I synnerhet det faktum att signaldiskretiseringen har en betydande inverkan på felen. Dessutom kan man notera att simuleringstiden är mycket längre än kalibreringstiden och leder till tanken att det finns sätt att förbättra gränssnittet för att göra det mer tidseffektivt.

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The master's thesis "Lower extremity stabilization function assessment using inertial sensors and functional tests in women's american football players" adresses postural control assessment in terms of functional joint stability using inertial sensors. The theoretical part is devoted to matters of functional joint stability, its control and it provides review of assessment options. Also inertial measurement unit function and application for human motion tracking are discussed. The last chapter covers American football considering the research group in the study. The experimental part is in form of study, which uses inertial sensor-based parameters to evaluate functional joint stability that are placed on four body segments and the results are compared with functional test results. We evaluated one-legged stance and single-leg hop test. One-legged stance, bear position and squat were chosen for the functional tests. Obtained data were also discussed in the context of history of injury in the last two years. Results confirm applicability of inertial measurement units for lower extremity stabilization objectification. We proved correlation of thight, shin and instep time to stabilization (TTS) with one-legged stance score and similarly thight and shin TTS with bear position score.

A demanda crescente por petróleo impulsiona a exploração marítima desta riqueza para águas cada vez mais profundas. O aumento da lâmina dágua exige novas soluções de engenharia principalmente no que se refere à operação de unidades flutuantes de produção. Dentre os desafios impostos pelos novos ambientes de prospecção, destaca-se o processo de ancoragem, neste texto explorado sob a ótica da chamada estaca-torpedo. Embora já tenha sido empregada com sucesso na ancoragem de risers e FPSOs, esta solução encontra-se em constante desenvolvimento, sendo a principal fonte de informação acerca dos lançamentos da estaca-torpedo proveniente de uma unidade de medição inercial (UMI). A presente pesquisa baseou-se no estudo desta UMI e teve como objetivos principais verificar seu desempenho e compreender a empregabilidade deste tipo de monitoração em operações submarinas de maneira mais ampla. Além do estudo detalhado dos sensores, foi dada especial atenção aos algoritmos empregados no tratamento dos sinais provenientes da UMI. Foram estudadas técnicas de correção do sinal, quantificação de ruído, desafios inerentes aos sistemas do tipo strapdown e o processo de integração. Como resultado final foi desenvolvido um algoritmo baseado em quatérnios, alternativo ao atualmente empregado para o processamento dos sinais provenientes da UMI que equipa a estaca-torpedo. / The increasing demand on crude oils constantly pushes the offshore exploitation to deeper waters. As the water depth grows, new engineering challenges arise, especially concerning to the operation of floating production units. Among all the technical issues inherent to the new prospection environment, the mooring system is a significant topic and the development of the torpedo-pile takes place at this scenario. This mooring system has already been successfully applied in anchoring risers and FPSOs; nevertheless, it is in constant study and improvement. The major source of information about the torpedo-pile deployment comes from an inertial measurement unit (IMU). The research presented here is based on this IMU and had as main objective verify its performance and also comprehend the applicability of such kind of unit in other subsea processes. Along with the detailed sensors study, it was given special attention to the algorithms used to process the signals from the IMU. Signal correction techniques and noise quantification were investigated as long as challenges intrinsically related to strapdown navigation systems and the integration process. In the end, an alternative data processing algorithm based on quaternions was produced, to be employed in torpedo-pile launching together with its IMU.

Recognizing human activities using deep learning methods has significance in many fields such as sports, motion tracking, surveillance, healthcare and robotics. Inertial sensors comprising of accelerometers and gyroscopes are commonly used for sensor based HAR. In this study, a Bidirectional Long Short-Term Memory (BLSTM) approach is explored for human activity recognition and classification for closely related activities on a body worn inertial sensor data that is provided by the UTD-MHAD dataset. The BLSTM model of this study could achieve an overall accuracy of 98.05% for 15 different activities and 90.87% for 27 different activities performed by 8 persons with 4 trials per activity per person. A comparison of this BLSTM model is made with the Unidirectional LSTM model. It is observed that there is a significant improvement in the accuracy for recognition of all 27 activities in the case of BLSTM than LSTM.

This research concerns the development of a smart sensory system for tracking a hand-held moving device to millimeter accuracy, for slow or nearly static applications over extended periods of time. Since different operators in different applications may use the system, the proposed design should provide the accurate position, orientation, and velocity of the object without relying on the knowledge of its operation and environment, and based purely on the motion that the object experiences. This thesis proposes the design of the integration a low-cost Local Positioning System (LPS) and a low-cost StrapDown Inertial Navigation System (SDINS) with the association of the modified EKF to determine 3D position and 3D orientation of a hand-held tool within a required accuracy. A hybrid LPS/SDINS combines and complements the best features of two different navigation systems, providing a unique solution to track and localize a moving object more precisely. SDINS provides continuous estimates of all components of a motion, but SDINS loses its accuracy over time because of inertial sensors drift and inherent noise. LPS has the advantage that it can possibly get absolute position and velocity independent of operation time; however, it is not highly robust, is computationally quite expensive, and exhibits low measurement rate. This research consists of three major parts: developing a multi-camera vision system as a reliable and cost-effective LPS, developing a SDINS for a hand-held tool, and developing a Kalman filter for sensor fusion. Developing the multi-camera vision system includes mounting the cameras around the workspace, calibrating the cameras, capturing images, applying image processing algorithms and features extraction for every single frame from each camera, and estimating the 3D position from 2D images. In this research, the specific configuration for setting up the multi-camera vision system is proposed to reduce the loss of line of sight as much as possible. The number of cameras, the position of the cameras with respect to each other, and the position and the orientation of the cameras with respect to the center of the world coordinate system are the crucial characteristics in this configuration. The proposed multi-camera vision system is implemented by employing four CCD cameras which are fixed in the navigation frame and their lenses placed on semicircle. All cameras are connected to a PC through the frame grabber, which includes four parallel video channels and is able to capture images from four cameras simultaneously. As a result of this arrangement, a wide circular field of view is initiated with less loss of line-of-sight. However, the calibration is more difficult than a monocular or stereo vision system. The calibration of the multi-camera vision system includes the precise camera modeling, single camera calibration for each camera, stereo camera calibration for each two neighboring cameras, defining a unique world coordinate system, and finding the transformation from each camera frame to the world coordinate system. Aside from the calibration procedure, digital image processing is required to be applied into the images captured by all four cameras in order to localize the tool tip. In this research, the digital image processing includes image enhancement, edge detection, boundary detection, and morphologic operations. After detecting the tool tip in each image captured by each camera, triangulation procedure and optimization algorithm are applied in order to find its 3D position with respect to the known navigation frame. In the SDINS, inertial sensors are mounted rigidly and directly to the body of the tracking object and the inertial measurements are transformed computationally to the known navigation frame. Usually, three gyros and three accelerometers, or a three-axis gyro and a three-axis accelerometer are used for implementing SDINS. The inertial sensors are typically integrated in an inertial measurement unit (IMU). IMUs commonly suffer from bias drift, scale-factor error owing to non-linearity and temperature changes, and misalignment as a result of minor manufacturing defects. Since all these errors lead to SDINS drift in position and orientation, a precise calibration procedure is required to compensate for these errors. The precision of the SDINS depends not only on the accuracy of calibration parameters but also on the common motion-dependent errors. The common motion-dependent errors refer to the errors caused by vibration, coning motion, sculling, and rotational motion. Since inertial sensors provide the full range of heading changes, turn rates, and applied forces that the object is experiencing along its movement, accurate 3D kinematics equations are developed to compensate for the common motion-dependent errors. Therefore, finding the complete knowledge of the motion and orientation of the tool tip requires significant computational complexity and challenges relating to resolution of specific forces, attitude computation, gravity compensation, and corrections for common motion-dependent errors. The Kalman filter technique is a powerful method for improving the output estimation and reducing the effect of the sensor drift. In this research, the modified EKF is proposed to reduce the error of position estimation. The proposed multi-camera vision system data with cooperation of the modified EKF assists the SDINS to deal with the drift problem. This configuration guarantees the real-time position and orientation tracking of the instrument. As a result of the proposed Kalman filter, the effect of the gravitational force in the state-space model will be removed and the error which results from inaccurate gravitational force is eliminated. In addition, the resulting position is smooth and ripple-free. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. If the sampling rate of the vision system decreases from 20 fps to 5 fps, the errors are still acceptable for many applications.

This research concerns the development of a smart sensory system for tracking a hand-held moving device to millimeter accuracy, for slow or nearly static applications over extended periods of time. Since different operators in different applications may use the system, the proposed design should provide the accurate position, orientation, and velocity of the object without relying on the knowledge of its operation and environment, and based purely on the motion that the object experiences. This thesis proposes the design of the integration a low-cost Local Positioning System (LPS) and a low-cost StrapDown Inertial Navigation System (SDINS) with the association of the modified EKF to determine 3D position and 3D orientation of a hand-held tool within a required accuracy. A hybrid LPS/SDINS combines and complements the best features of two different navigation systems, providing a unique solution to track and localize a moving object more precisely. SDINS provides continuous estimates of all components of a motion, but SDINS loses its accuracy over time because of inertial sensors drift and inherent noise. LPS has the advantage that it can possibly get absolute position and velocity independent of operation time; however, it is not highly robust, is computationally quite expensive, and exhibits low measurement rate. This research consists of three major parts: developing a multi-camera vision system as a reliable and cost-effective LPS, developing a SDINS for a hand-held tool, and developing a Kalman filter for sensor fusion. Developing the multi-camera vision system includes mounting the cameras around the workspace, calibrating the cameras, capturing images, applying image processing algorithms and features extraction for every single frame from each camera, and estimating the 3D position from 2D images. In this research, the specific configuration for setting up the multi-camera vision system is proposed to reduce the loss of line of sight as much as possible. The number of cameras, the position of the cameras with respect to each other, and the position and the orientation of the cameras with respect to the center of the world coordinate system are the crucial characteristics in this configuration. The proposed multi-camera vision system is implemented by employing four CCD cameras which are fixed in the navigation frame and their lenses placed on semicircle. All cameras are connected to a PC through the frame grabber, which includes four parallel video channels and is able to capture images from four cameras simultaneously. As a result of this arrangement, a wide circular field of view is initiated with less loss of line-of-sight. However, the calibration is more difficult than a monocular or stereo vision system. The calibration of the multi-camera vision system includes the precise camera modeling, single camera calibration for each camera, stereo camera calibration for each two neighboring cameras, defining a unique world coordinate system, and finding the transformation from each camera frame to the world coordinate system. Aside from the calibration procedure, digital image processing is required to be applied into the images captured by all four cameras in order to localize the tool tip. In this research, the digital image processing includes image enhancement, edge detection, boundary detection, and morphologic operations. After detecting the tool tip in each image captured by each camera, triangulation procedure and optimization algorithm are applied in order to find its 3D position with respect to the known navigation frame. In the SDINS, inertial sensors are mounted rigidly and directly to the body of the tracking object and the inertial measurements are transformed computationally to the known navigation frame. Usually, three gyros and three accelerometers, or a three-axis gyro and a three-axis accelerometer are used for implementing SDINS. The inertial sensors are typically integrated in an inertial measurement unit (IMU). IMUs commonly suffer from bias drift, scale-factor error owing to non-linearity and temperature changes, and misalignment as a result of minor manufacturing defects. Since all these errors lead to SDINS drift in position and orientation, a precise calibration procedure is required to compensate for these errors. The precision of the SDINS depends not only on the accuracy of calibration parameters but also on the common motion-dependent errors. The common motion-dependent errors refer to the errors caused by vibration, coning motion, sculling, and rotational motion. Since inertial sensors provide the full range of heading changes, turn rates, and applied forces that the object is experiencing along its movement, accurate 3D kinematics equations are developed to compensate for the common motion-dependent errors. Therefore, finding the complete knowledge of the motion and orientation of the tool tip requires significant computational complexity and challenges relating to resolution of specific forces, attitude computation, gravity compensation, and corrections for common motion-dependent errors. The Kalman filter technique is a powerful method for improving the output estimation and reducing the effect of the sensor drift. In this research, the modified EKF is proposed to reduce the error of position estimation. The proposed multi-camera vision system data with cooperation of the modified EKF assists the SDINS to deal with the drift problem. This configuration guarantees the real-time position and orientation tracking of the instrument. As a result of the proposed Kalman filter, the effect of the gravitational force in the state-space model will be removed and the error which results from inaccurate gravitational force is eliminated. In addition, the resulting position is smooth and ripple-free. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. If the sampling rate of the vision system decreases from 20 fps to 5 fps, the errors are still acceptable for many applications.

This thesis presents the development of high resolution, wide dynamic range sigma-delta type readout circuits for capacitive MEMS accelerometers. Designed readout circuit employs fully differential closed loop structure with digital output, achieving high oversampling ratio and high resolution. The simulations of the readout circuit together with the accelerometer sensor are performed using the models constructed in Cadence and Matlab Simulink environments. The simulations verified the stability and proper operation of the accelerometer system. The sigma-delta readout circuit is implemented using XFab 0.6 &micro / m CMOS process. Readout circuit is combined with Silicon-On-Glass (SOG) and Dissolved Wafer Process (DWP) accelerometers. Both open loop and closed loop tests of the accelerometer system are performed. Open loop test results showed high sensitivity up to 8.1 V/g and low noise level of 4.8 &micro / g/&amp / #61654 / Hz. Closed loop circuit is implemented on a PCB together with the external filtering and decimation electronics, providing 16-bit digital output at 800 Hz sampling rate. High acceleration tests showed &plusmn / 18.5 g of linear acceleration range with high linearity, using DWP accelerometers. The noise tests in closed loop mode are performed using Allan variance technique, by acquiring the digital data. Allan variance tests provided 86 &micro / g/&amp / #61654 / Hz of noise level and 74 &micro / g of bias drift. Temperature sensitivity tests of the readout circuit in closed loop mode is also performed, which resulted in 44 mg/&ordm / C of temperature dependency. Two different types of new adaptive sigma-delta readout circuits are designed in order to improve the resolution of the systems by higher frequency operation. The two circuits both change the acceleration range of operation of the system, according to the level of acceleration. One of the adaptive circuits uses variation of feedback time, while the other circuit uses multi-bit feedback method. The simulation results showed micro-g level noise in closed loop mode without the addition of the mechanical noise of the sensor.

Micromachining technologies enabled the use of miniaturized transducers in many high technology sensing systems. These transducers have many advantages like small-size, low-cost and high-reliability. One of the applications micro-machined transducers are used is inertial navigation systems, where the exact position of a moving frame is continuously monitored by tracking the linear and angular motions of the frame. Other than navigation applications, inertial sensors are used in health and military applications as well as consumer electronics. Today accelerometers capable of measuring accelerations from 0.5g-1g range up to several thousand g&rsquo / s are commercially available in the market which have been fabricated using micromachining technologies. The aim of this research is to develop such a state-of-the-art micro-machined accelerometer system, whose performance is expected to reach tactical-grade level. In order to achieve these performance values a MATLAB algorithm is developed to optimize the accelerometer performances in the desired levels. Expected performance parameters of the designed accelerometer structures are extracted from the simulations done by both Coventorware finite element modeling tool and MATLAB. Designed structures are then fabricated with silicon-on-glass, dissolved wafer and dissolved epitaxial wafer processes. These fabrication results are compared and it is observed that highest yield accelerometers are fabricated with the SOG process. But these accelerometers could not be able to satisfy tactical grade performance parameters. Best performances are obtained with DWP, but due to high internal stress, yield of the sensors were very low. DEWP increased the yield of this process from 2-3% to 45-50% but the expected operation range of the designs dropped to &plusmn / 12.5g range. Using the fabricated accelerometers in DEWP a three axial accelerometer package is prepared and tests results proved that this three axial accelerometer system was satisfying the tactical grade requirements. In addition to these a three axial monolithic accelerometer fabrication technique is proposed and sensors are designed which are suitable for this process. Best performances achieved with single axis accelerometers were 153&micro / g/&radic / Hz noise floor, 50&micro / g bias drift, 0.38% non-linearity and a maximum operation range of 33.5g which has the higher dynamic range among its counterparts in the literature. Performance results achieved with the three axes accelerometer were ~150&micro / g bias drift, &lt / 200&micro / g/&radic / Hz noise density, ~0.4% non-linearity with higher than &plusmn / 10g operation range.