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The aim of this master’s thesis was to describe the basic measurement methods for measurement of inertial sensor, their physical principles and errors. The next step was to select a specific parameter, then test it on a number of sensors and evaluate the results. Measurement of inertial sensors is very important for the parametrization of their errors and their subsequent mathematical model by which it is possible to minimize their impact on inertial navigation. The practical part is dedicated to the measurement of stability of the offset. Data acquisition card NI-USB 6215, which can supply two accelerometers at the same time using analog outputs, was used for data acquisition and power supply. It was tested on seven inertial sensor from four manufacturers. Two connection methods with NI-USB 6215, by whose it was determined which one is better to suppress the crosstalk between channels, were used for measurement. The NI PXI 4462 was used to verify that the NI-USB 6215 card is sufficient. The parameters for description of the changes in inertial sensors were established, transition between the initial and final value of the output measurement, variance of the values on which the sensor fixates after 72 iterations, and the fixation time of the sensor.

The aim of this master’s thesis is to describe the basic measurement methods of micro-electromechanical inertial sensor, their physical principles and errors. Measurement of inertial sensors is very important for the parameterization of their errors and their subsequent mathematical model by which it is possible to minimize the measurement error impact on inertial navigation. The practical part is dedicated to create automated measurement setup for measurement stability of the offset. Hardware and software from National Instruments is used in measurement chain. The work is next focused on measuring seven inertial sensors based on three different physical principles. In addition to creating measurement setup, we also defined three inertial sensor parameters, describing theoretical behavior of the sensor output.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / In pursuit to understanding the flight behavior and characterizing the stability of a flarestabilized projectile, an experiment was conducted to assess the robustness of an inertial sensor suite the size of a dime (17.5mm) by integrating to a telemetry system for recording. The system had to survive launch acceleration exceeding 25,000G’s. This is the beginning of an effort to reduce the size of telemetry systems and diagnostic devices for use in medium caliber munitions and smaller. A description of the telemetry system and subsystem will be presented along with the results.

In the thesis, we present the structures of interacting multiple model (IMM) algorithm-based filter design for real-time motion orientation estimation and tracking by using inertial sensor measurements in three-dimensional space. The major sensor such as gyroscope, though has high-sensitivity characteristics, suffers from bias build-up and error drift over time. The complementary sensors such as accelerometer and magnetometer, on the other hand, have low sensitivity, but do not suffer from bias problems. By using individual inertial and magnetic sensors, measurements of multiple modes can be interactively computed. The IMM based designs show the advantages of weighting individual sensors in different motion states. We propose a signal processing architecture based on the IMM algorithm. It is composed of three parallel Kalman filters (KFs), each deals with measured signals from accelerometer, magnetometer and gyroscope, respectively. The accelerometer cannot effectively sense the rotation around the vertical axis; while the magnetometer can only sense the rotation around vertical axis. Therefore, estimation accuracy with the parallel filtering arrangement of the IMM algorithm-based structure may be affected. A scheme using the residual signal, which is computed in the IMM, provides the information of gyroscope-based KF to the other two filters for feasible calculation of update weights. Related research also usually combined the information of major and complementary sensors in estimator designs. In the literature, existing ¡§Triad¡¨ methods with quaternion-based extended Kalman filter (EKF), process the measurements from major and complementary sensors. To compensate the functions, we propose to use a gyroscope-based EKF and a Triad EKF in forming a parallel multiple model-based structure. The analysis and performance evaluation shows advantages and disadvantages of using EKFs and KFs in IMM-based filtering approachs. Simulation results validate the proposed estimator design concept, and show that the scheme is capable of reducing the overall estimation errors by flexible computation of model weights.

abstract: This thesis presents a multi-modal motion tracking system for stroke patient rehabilitation. This system deploys two sensor modules: marker-based motion capture system and inertial measurement unit (IMU). The integrated system provides real-time measurement of the right arm and trunk movement, even in the presence of marker occlusion. The information from the two sensors is fused through quaternion-based recursive filters to promise robust detection of torso compensation (undesired body motion). Since this algorithm allows flexible sensor configurations, it presents a framework for fusing the IMU data and vision data that can adapt to various sensor selection scenarios. The proposed system consequently has the potential to improve both the robustness and flexibility of the sensing process. Through comparison between the complementary filter, the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the particle filter (PF), the experimental part evaluated the performance of the quaternion-based complementary filter for 10 sensor combination scenarios. Experimental results demonstrate the favorable performance of the proposed system in case of occlusion. Such investigation also provides valuable information for filtering algorithm and strategy selection in specific sensor applications. / Dissertation/Thesis / M.S. Electrical Engineering 2010

Wearable sensors are at the heart of the digital health revolution. Integral to the use of these sensors for monitoring conditions impacting balance and mobility are accurate estimates of joint angles. To this end a simple and novel method of estimating hip joint angles from small wearable magnetic and inertial sensors is proposed and its performance is established relative to optical motion capture in a sample of human subjects. Improving upon previous work, this approach does not require precise sensor placement or specific calibration motions, thereby easing deployment outside of the research laboratory. Specific innovations include the determination of sensor to segment rotations based on functionally determined joint centers, and the development of a novel filtering algorithm for estimating the relative orientation of adjacent body segments. Hip joint angles and range of motion determined from the proposed approach and an existing method are compared to those from an optical motion capture system during walking at a variety of speeds and tasks designed to exercise the hip through its full range of motion. Results show that the proposed approach estimates flexion/extension angle more accurately (RMSE from 7.08 to 7.29 deg) than the existing method (RMSE from 11.64 deg to 14.33 deg), with similar performance for the other anatomical axes. Agreement of each method with optical motion capture is further characterized by considering correlation and regression analyses. Mean ranges of motion for the proposed method are not largely different from those reported by motion capture, and showed similar values to the existing method. Results indicate that this algorithm provides a promising approach for estimating hip joint angles using wearable inertial sensors, and would allow for use outside of constrained research laboratories.

It was demonstrated in recent studies that Complementary Limb Motion Estimation (CLME) is robust approach for controlling active knee prostheses. A wearable sensor system is then needed to provide inputs to the controller in a real-time platform. In the present work, a wearable sensor system based on magnetic and inertial measurement units (MIMU) together with a simple calibration procedure were proposed. This sensor system was intended to substitute and extend the capabilities of a previous device based on potentiometers and gyroscopes. The proposed sensor system and calibration were validated with an Optical Tracking System (OTS) in a standard gait lab and first results showed that the proposed solution had a performance comparable to similar studies in the literature.

A general automotive navigation system is a satellite navigation system designed for use in automobiles. It typically uses GPS to acquire position data to locate the user on a road in the unit's map database. However, due to recent improvements in the performance of small and light weight micro-machined electromechanical systems (MEMS) inertial sensors have made the application ofinertial techniques to such problems, possible. This has resulted in an increased interest in the topic of inertial navigation. In location tracking system, sensors are used either individually or in conjunction like in data fusion.However, still they remain noisy, and so there is a need to measure maximum data and then make an efficient system that can remove the noise from data and provide a better estimate.The task of this thesis work was to take data from two sensors, and use an estimation technique to provide an accurate estimate of the true location. The proposed sensors were an accelerometer and aGPS device. This thesis however deals with using accelerometer sensor and using estimation scheme, Kalman filter. This thesis report presents an insight to both the proposed sensors and different estimation techniques.Within the scope of the work, the task was performed using simulation software Matlab. Kalman filter’s efficiency was examined using different noise levels.

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