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Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. / Microfiche copy available in Archives and Aero. / Includes bibliographical references. / M.S.

Accurate risk assessment tools and methods are necessary to understand the relationship between occupational exposure to physical risk factors and musculoskeletal disorders. Ergonomists typically consider direct measurement methods to be the most objective and accurate of the available tools. However, direct measurement methods are often not used due to cost, practicality, and worker/workplace disruption. Inertial measurement units (IMUs), a relatively new direct measurement technology used to assess worker kinematics, are attractive to ergonomists due to their small size, low cost, and ability to reliably capture information across full working shifts. IMUs are often touted as a field-capable alternative to optical motion capture systems (OMCs). The error magnitudes of IMUs, however, can vary significantly (>15°) both within and across studies. The overall goals of this thesis were to (i) provide knowledge about the capabilities and limitations of IMUs in order to explain the inconsistencies observed in previous studies that assessed IMU accuracy, and (ii) provide guidance for the ergonomics community to leverage this technology. All three studies in this dissertation systematically evaluated IMUs using a repetitive material transfer task performed by thirteen participants with varying movement speeds (15, 30, 45 cycles/minute) and magnetic disturbance (absent, present). An OMC system was used as the reference device. This first study systematically evaluated the effects of motion speed and magnetic disturbance on the spatial orientation accuracy of an inertial measurement unit (IMU) worn on the hand. Root-mean-square differences (RMSD) exceeded 20° when inclination measurements (pitch and roll) were calculated using the IMU’s accelerometer. A linear Kalman filter and a proprietary, embedded Kalman filter reduced inclination RMSD to < 3° across all movement speeds. The RMSD in the heading direction (i.e., about gravity) increased (from < 5° to 17°) under magnetic disturbance. The linear Kalman filter and the embedded Kalman filter reduced heading RMSD to < 12° and < 7°, respectively. This study indicated that the use of IMUs and Kalman filters can improve inclinometer measurement accuracy. However, magnetic disturbances continue to limit the accuracy of three-dimensional IMU motion capture. The goal of the second study was to understand the capability of IMU inclinometers to improve estimates of angular displacements and velocities of the upper arm. RMSD and peak displacement error exceeded 11° and 28° at the fastest transfer rate (45 cycles/min) when upper arm elevation was calculated using the IMU accelerometer. The implementation of a Kalman filter reduced RMS and peak errors to < 1.5° and < 2.3°, respectively. Similarly, the RMS and peak error for accelerometer-derived velocities exceeded 81°/s and 221.3°/s, respectively, at the fastest transfer rate. The Kalman filter reduced RMS and peak errors to < 9.2°/s and < 25.1°/s, respectively. The third study was conducted to evaluate the relationship between magnetic field strength variation and magnetic heading deviation. In this study, the presence of the metal plate increased magnetic heading deviations from < 12° (90th-10th percentile) to approximately 30°. As expected, the magnetic field strength standard deviation increased from 1.0uT to 2.4uT. While this relationship may differ across other sources of magnetic disturbance, the results reinforce the notion that local magnetic field disturbances should be minimized when using IMUs for human motion capture. Overall, the findings from this thesis contribute to the ergonomics community’s understanding of the current capabilities and limitations of IMUs. These studies suggest that while the touted capabilities of the IMUs (full-body motion capture in workplace settings) may be unattainable based on current sensor technology, these sensors are still significantly more accurate than the accelerometer-based inclinometers commonly used by ergonomists to measure motions of the upper arms.

<p>Hasomed GmbH, a German company in the Field of medicine technology, intends to introduce a gait analysis system on the market. The system includes an inertial sensor which collects data used for generating movement patterns of the feet. This thesis describes the development and evaluation of a new version of the sensor, aimed at minimizing costs, maximizing performance and facilitating production.</p><p>Algorithms used in the gait analysis system are sensitive to noise. Noise sources and precautions taken in order to minimize noise levels are described and discussed. By minimizing the physical size of analogue electronics blocks, static noise and occasional high frequency components were substantially reduced.</p><p>New features including internal temperature sensors, firmware update via serial interface, self-test functions and a wireless link were implemented. Additional improvements are e.g. lower power consumption and an extension of the interface from 2 to 256 (theoretical limit) attached devices. By reducing the number of included components and PCB (Printed Circuit Board) layers, together with use of components that do not require advanced soldering techniques, easier and cheaper production was obtained.</p><p>Research and development presented in this thesis resulted in a sensor with overall good performance and new features.</p>

Today the area of high-cost and high-performance navigation for vehicles is a well-developed field. The challenge now is to develop high-performance navigation systems using low-cost sensortechnology. This development involves problems spanning from signal processing of the dirty measurements produced by low-costsensors via fusion and synchronization of information produced by a large set of diverse sensors, to reducing the size and energyconsumption of the systems. This thesis examines and proposessolutions to four of these problems. The first problem examined is the time synchronizing of the sensordata in a global positioning system aided inertial navigationsystem in which no hardware clock synchronization is possible. A poor time synchronization results in an increased mean squareerror of the navigation solution and expressions for calculating this mean square error are presented. A method to solve the timesynchronization issue in the data integration software is proposed. The potential of the method is illustrated with tests onreal-world data that are subjected to timing errors. The second problem examined is the achievable clocksynchronization accuracy in a sensor network employing a two-waymessage exchange model. The Cramer-Rao bound for the estimation of the clock parameters is derived and transformed in to a lower bound on the mean square error of the clock offset.Further, an approximate maximum likelihood estimator for the clockparameters is proposed. The estimator is shown to be of low complexity and to have a mean square error in the vicinity of the Cramer-Rao bound. The third problem examined is the detection of the time epochswhen zero-velocity updates can be applied in a foot-mountedpedestrian navigation system. Four general likelihood ratio testsfor detecting when the navigation system is stationary based onthe inertial measurement data are studied. The performance of thefour detectors is evaluated using levelled ground, forward-gaitdata. The results show that the signals from the gyroscopes holdthe most reliable information for the zero-velocity detection. The fourth problem examined is the calibration of a low-costinertial measurement unit. A calibration procedure that relaxesthe accuracy requirements of the orientation angles the inertialmeasurement unit must be placed in during the calibration isstudied. The proposed calibration method is compared with theCramer-Rao bound for the case when the inertial measurementunit is rotated into precisely controlled orientations. Simulationresults show that the mean square error of the estimated sensormodel parameters reaches the Cramer-Rao bound within fewdecibels. Thus, the proposed method may be acceptable for a widerange of low-cost applications. / QC 20100810

Hasomed GmbH, a German company in the Field of medicine technology, intends to introduce a gait analysis system on the market. The system includes an inertial sensor which collects data used for generating movement patterns of the feet. This thesis describes the development and evaluation of a new version of the sensor, aimed at minimizing costs, maximizing performance and facilitating production. Algorithms used in the gait analysis system are sensitive to noise. Noise sources and precautions taken in order to minimize noise levels are described and discussed. By minimizing the physical size of analogue electronics blocks, static noise and occasional high frequency components were substantially reduced. New features including internal temperature sensors, firmware update via serial interface, self-test functions and a wireless link were implemented. Additional improvements are e.g. lower power consumption and an extension of the interface from 2 to 256 (theoretical limit) attached devices. By reducing the number of included components and PCB (Printed Circuit Board) layers, together with use of components that do not require advanced soldering techniques, easier and cheaper production was obtained. Research and development presented in this thesis resulted in a sensor with overall good performance and new features.

Due to their complementary characteristics, Global Positioning System (GPS) is usually integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low cost. In this study, a reduced inertial sensor system (RISS) is considered. It comprises a MEMS grade single axis gyroscope, the vehicle built-in odometer, and two optional MEMS grade accelerometers. Estimation technique is needed to allow the data fusion of RISS and GPS. With adequate accuracy, Kalman filter (KF) fulfills this requirement if high-end inertial sensors are used. However, due to the inherent error characteristics of MEMS devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be suppressed by KF alone. To solve the problem, Fast Orthogonal Search (FOS), a nonlinear system identification algorithm, is suggested in this research for modeling higher order RISS errors. FOS algorithm has the ability to figure out the system nonlinearity with a tolerance of arbitrary stochastic system noise. Its modeling results can then be used to predict the system dynamics. Motivated by the above merits, a KF/FOS module is proposed. By handling both linear and nonlinear RISS errors, this module targets substantial enhancement of positioning accuracy. To examine the effectiveness of the proposed technique, KF/FOS module is applied on RISS with GPS in a land vehicle for several road test trajectories. Its performance is compared to KF-only method, both assessed with respect to a high-end reference. To evaluate navigation algorithm in real-time vehicle application, a multi-sensor data logger is designed in this research to collect online RISS/GPS data. KF/FOS module is transplanted on an embedded digital signal processor as well. Both the off-line and online results confirm that KF/FOS module outperforms KF-only approach in positioning accuracy. They also demonstrate reliable real-time performance. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2012-01-22 01:26:11.477

This study investigated the predictive value of time on-task and error scores on tests administered through Control Data Corporation PLATO interactive computer graphics simulation as predictors of errors and time on-task for inertial navigation system equipment operation. In addition, the correlation between simulated pass/fail error and time on-task scores, and subsequent pass/fail criteria using actual equipment was investigated.

M.Ing. / The Graphics Processor Unit (GPU) has been in the gaming industry for several years now. Of late though programmers and scientists have started to use the parallel processing or stream processing capabilities of the GPU in general numerical applications. The Monte Carlo method is a processing intensive methods, as it evaluates systems with stochastic components. The stochastic components require several iterations of the systems to develop an idea of how the systems reacts to the stochastic inputs. The stream processing capabilities of GPUs are used for the analysis of such systems. Evaluating low-cost Inertial Measurement Units (IMU) for utilisation in Inertial Navigation Systems (INS) is a processing intensive process. The non-deterministic or stochastic error components of the IMUs output signal requires multiple simulation runs to properly evaluate the IMUs performance when applied as input to an INS. The GPU makes use of stream processing, which allows simultaneous execution of the same algorithm on multiple data sets. Accordingly Monte Carlo techniques are applied to create trajectories for multiple possible outputs of the INS based on stochastically varying inputs from the IMU. The processing power of the GPU allows simultaneous Monte Carlo analysis of several IMUs. Each IMU requires a sensor error model, which entails calibration of each IMU to obtain numerical values for the main error sources of lowcost IMUs namely scale factor, non-orthogonality, bias, random walk and white noise. Three low-cost MEMS IMUs was calibrated to obtain numerical values for their sensor error models. Simultaneous Monte Carlo analysis of each of the IMUs is then done on the GPU with a resulting circular error probability plot. The circular error probability indicates the accuracy and precision of each IMU relative to a reference trajectory and the other IMUs trajectories. Results obtained indicate the GPU to be an alternative processing platform, for large amounts of data, to that of the CPU. Monte Carlo simulations on the GPU was performed 200 % faster than Monte Carlo simulations on the CPU. Results obtained from the Monte Carlo simulations, indicated the Random Walk error to be the main source of error in low-cost IMUs. The CEP results was used to determine the e ect of the various error sources on the INS output.

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