Yip, Wai Lee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 119-124). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.ii / Acknowledgement --- p.iii / Table of Content --- p.iv / List of Figure --- p.vi / List of table --- p.viii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Background information --- p.2 / Chapter 1.2.1 --- Overview of positioning technologies --- p.2 / Chapter 1.2.2 --- Comparison between different positioning systems --- p.7 / Chapter 1.2.3 --- Recent works related to INS --- p.9 / Chapter 1.3 --- Objective --- p.11 / Chapter 1.4 --- Organization of thesis --- p.11 / Chapter Chapter 2 --- Literature Study --- p.13 / Chapter 2.1 --- Introduction to INS --- p.13 / Chapter 2.1.1 --- Coordinate Frames --- p.13 / Chapter 2.1.2 --- Gimbaled INS --- p.16 / Chapter 2.1.3 --- Strapdown INS --- p.17 / Chapter 2.1.4 --- Conventional algorithm of strapdown INS --- p.17 / Chapter 2.2 --- Inertial sensors --- p.19 / Chapter 2.2.1 --- Gyroscope --- p.19 / Chapter 2.2.2 --- Accelerometer --- p.20 / Chapter 2.3 --- Previous works --- p.22 / Chapter 2.4 --- GF-INS --- p.23 / Chapter 2.5 --- Summary --- p.25 / Chapter Chapter 3 --- Performance of MEMS accelerometer in position determination --- p.27 / Chapter 3.1 --- Basic principle --- p.27 / Chapter 3.2 --- Numeric integration --- p.28 / Chapter 3.3 --- Experimental setup --- p.30 / Chapter 3.3.1 --- MEMS Accelerometer --- p.30 / Chapter 3.3.2 --- Microcontroller --- p.32 / Chapter 3.3.3 --- System architecture --- p.33 / Chapter 3.3.4 --- Testing platform --- p.34 / Chapter 3.4 --- Initial calibration and filtering --- p.37 / Chapter 3.4.1 --- Convert ADC reading to acceleration --- p.37 / Chapter 3.4.2 --- Identify configuration error --- p.38 / Chapter 3.4.3 --- Implement low pass filter --- p.39 / Chapter 3.5 --- Experimental results --- p.40 / Chapter 3.5.1 --- Results --- p.40 / Chapter 3.5.2 --- Discussion --- p.43 / Chapter 3.6 --- Summary --- p.45 / Chapter Chapter 4 --- Performance Improvement --- p.46 / Chapter 4.1 --- Fuzzy logic based steady state detector --- p.46 / Chapter 4.1.1 --- Principle --- p.46 / Chapter 4.1.2 --- Experimental result --- p.48 / Chapter 4.2 --- Kalman filtering --- p.50 / Chapter 4.2.1 --- Discrete Kalman filter --- p.50 / Chapter 4.2.2 --- Combine with fuzzy logic based steady state detector --- p.52 / Chapter 4.2.3 --- Experimental results --- p.54 / Chapter 4.3 --- Summary --- p.58 / Chapter Chapter 5 --- Construction of GF-INS --- p.59 / Chapter 5.1 --- Principle of GF-INS --- p.59 / Chapter 5.1.1 --- Algorithm --- p.59 / Chapter 5.1.2 --- Comparing error of GF-INS and conventional INS --- p.66 / Chapter 5.1.3 --- Simulation study --- p.67 / Chapter 5.2 --- Experimental setup --- p.73 / Chapter 5.3 --- Experimental Results --- p.75 / Chapter 5.4 --- Summary --- p.81 / Chapter Chapter 6 --- Improvement on the GF-INS --- p.82 / Chapter 6.1 --- Configuration error compensation --- p.82 / Chapter 6.1.1 --- "Identify bias, scale factor and sensing direction error" --- p.83 / Chapter 6.1.2 --- Identify position error --- p.86 / Chapter 6.1.3 --- Compensator design --- p.89 / Chapter 6.1.4 --- Simulation --- p.91 / Chapter 6.2 --- Fuzzy rule based motion state detector --- p.97 / Chapter 6.2.1 --- Relation of data in different motions --- p.97 / Chapter 6.2.2 --- Fuzzy system --- p.99 / Chapter 6.2.3 --- Membership function training with gradient descent --- p.101 / Chapter 6.3 --- Experimental results and discussion --- p.104 / Chapter 6.3.1 --- Configuration errors --- p.104 / Chapter 6.3.2 --- Compensator --- p.106 / Chapter 6.3.3 --- Fuzzy rule based motion state detector --- p.107 / Chapter 6.3.4 --- Comparing the performance of both methods --- p.110 / Chapter 6.3.5 --- Comparing GF-INS and one dimensional INS --- p.112 / Chapter 6.3.6 --- Discussion --- p.113 / Chapter 6.4 --- Summary --- p.115 / Chapter Chapter 7 --- Conclusions and Future works --- p.116 / Reference --- p.119

Les travaux entrepris dans cette thèse s'inscrivent dans le cadre du projet BodyScoring. Ce dernier propose une solution innovante basée sur l'utilisation d'une technologie embarquée (BodyTrack) et des applications web (BodyLink) pour évaluer les habiletés motrices et pour développer la motivation pour l’accomplissement d’une pratique physique régulière. Dans le cadre de ce projet, notre apport a consisté à évaluer et à noter la qualité de la marche des personnes âgées par le biais de capteurs inertiels qui incluent accéléromètre, gyromètre et magnétomètre. Notre apport original a consisté à caractériser le pattern de marche au travers de différentes configurations de capteurs placés sur le corps et de proposer un score global validé et facilement interprétable. Le score permettra de se positionner par rapport à une population de référence jeune et asymptomatique et in fine autoévaluer l’évolution de sa marche. Afin d’atteindre cet objectif plusieurs étapes sont nécessaires. Ainsi, le premier chapitre de ce mémoire décrit en se référant à la littérature les paramètres déterminants de la marche, les facteurs pouvant les influencer et les moyens utilisés pour les quantifier. Le second chapitre porte principalement sur la définition de la meilleure configuration de capteurs pour la détection des événements clés de la marche qui sont la survenue du contact initial et final et la quantification des paramètres temporels. Il en ressort que le gyromètre fixé au bord distal du tibia est la configuration la plus précise aussi bien pour la détection des événements de la marche que pour la quantification des paramètres temporels chez des sujets sains. Le troisième chapitre expose un nouveau protocole expérimental afin de définir les paramètres pertinents pour caractériser la marche et définir l'incidence de la pratique de la marche nordique sur les paramètres biomécaniques. Autrement dit, définir les paramètres biomécaniques qui rendent compte de l'altération du pattern de marche au cours de la sénescence ou encore apprécier l'effet d'une activité physique régulière. Cette étude a révélé 72 paramètres au pouvoir discriminant et rejoint les études qui rapportent un effet bénéfique de lamarche nordique. Pour finir, le quatrième chapitre décrit l'élaboration de nouveaux scores d'évaluation de la marche basé sur les paramètres mis en évidence au chapitre 3 complémentés par ceux qualifiant la symétrie des membres inférieurs et supérieurs. Ces derniers décrivent la qualité de la marche dans son ensemble (score global) et la qualité de chaque aspect (score partiel). Quantifiés pour trois groupes de sujets (âgés sédentaire, âgés sportif et jeune) ces scores ont permis de mettre en évidence l'altération du pattern de marche au cours de la sénescence et l'effet de la pratique d'une activité physique sur les paramètres associés à la marche. / The purpose of this thesis is to asses and scores the gait quality of elderly persons through inertial sensors. The originality of this contribution is to characterize the pattern of walking through different sensor configurations and propose an overall score, valid and easily interpretable. This latter, allows subjects to self-assess to position themselves compared to a young and asymptomatic reference population and ultimately track their evolution.The first chapter, following a review of the literature, identifies the determinant gait parameters, its influent factors and the means used to quantify them.The second chapter, focuses on the definition of the best configuration of sensors to detect gait events and quantify temporal parameters.The third chapter, lists the biomechanical parameters that reflect the changing pattern of walking during senescence or consecutive to a regular physical activity.In the fourth chapter, a new method to compute the score based on the parameters identified in Chapter 3 was developed.

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.

<p>This thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.</p>

This paper presents a method for pedestrian activity classification and gait analysis based on the microelectromechanical-systems inertial measurement unit (IMU). The work targets two groups of applications, including the following: 1) human activity classification and 2) joint human activity and gait-phase classification. In the latter case, the gait phase is defined as a substate of a specific gait cycle, i.e., the states of the body between the stance and swing phases. We model the pedestrian motion with a continuous hidden Markov model (HMM) in which the output density functions are assumed to be Gaussian mixture models. For the joint activity and gait-phase classification, motivated by the cyclical nature of the IMU measurements, each individual activity is modeled by a "circular HMM." For both the proposed classification methods, proper feature vectors are extracted from the IMU measurements. In this paper, we report the results of conducted experiments where the IMU was mounted on the humans' chests. This permits the potential application of the current study in camera-aided inertial navigation for positioning and personal assistance for future research works. Five classes of activity, including walking, running, going upstairs, going downstairs, and standing, are considered in the experiments. The performance of the proposed methods is illustrated in various ways, and as an objective measure, the confusion matrix is computed and reported. The achieved relative figure of merits using the collected data validates the reliability of the proposed methods for the desired applications. / <p>QC 20130114</p>

A method is proposed to fuse the information from two navigation systems whose relative position is unknown, but where there exists an upper limit on how far apart the two systems can be. The proposed information fusion method is applied to a scenario in which a pedestrian is equipped with two foot-mounted zero-velocity-aided inertial navigation systems; one system on each foot. The performance of the method is studied using experimental data. The results show that the method has the capability to significantly improve the navigation performance when compared to using two uncoupled foot-mounted systems. / <p>QC 20121221</p>

Near-fall detection is important for medical research since it can help doctors diagnose fall-related diseases and also help alert both doctors and patients of possible falls. However, in people’s daily life, there are lots of similarities between near-falls and other Activities of Daily Living (ADLs), which makes near-falls particularly difficult to detect. In order to find the subtle difference between ADLs and near-fall and accurately identify the latter, the movement of whole human body needs to be captured and displayed by a computer generated avatar. In this thesis, a wireless inertial motion capture system consisting of a central control host and ten sensor nodes is used to capture human body movements. Each of the ten sensor nodes in the system has a tri-axis accelerometer and a tri-axis gyroscope. They are attached to separate locations of a human body to record both angular and acceleration data with which body movements can be captured by applying Euler angle based algorithms, specifically, single rotation order algorithm and the optimal rotation order algorithm. According to the experiment results of capturing ten ADLs, both the single rotation order algorithm and the optimal rotation order algorithm can track normal human body movements without significantly distortion and the latter shows higher accuracy and lower data shifting. Compared to previous inertial systems with magnetometers, this system reduces hardware complexity and software computation while ensures a reasonable accuracy in capturing human body movements.

This thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.

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 inautomobiles. 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 lightweight micro-machined electromechanical systems (MEMS) inertial sensors have made the application of inertial 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 toprovide an accurate estimate of the true location. The proposed sensors were an accelerometer and a GPS device. This thesis however deals with using accelerometer sensor and using estimation scheme, Kalman filter. The 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.