This thesis proposes a solution in which Inertial Measurement Units(IMU) are placed along the thoracic section of the spine and measures the movement and rotation of the spine and subsequently also the back. The proposed solution should be able to help the user reduce the risk of injuries related to posture or lifting. Four IMU sensor devices has been constructed and they communicate with an Arduino Uno by means of I2C. Due to the project being at thesis level the amount of time available is limited and the practical parts of the project are scaled down to creating a basic proof of concept system to test the feasibility of the proposed solution. The proposed system is intended to one day become part of a wireless body area network(WBAN).

Human body motion capture systems based on inertial sensors (gyroscopes andaccelerometers) are able to track the relative motions in the body precisely, oftenwith the aid of supplementary sensors. The sensor measurements are combinedthrough a sensor fusion algorithm to create estimates of, among other parame-ters, position, velocity and orientation for each body segment. As this algorithmrequires integration of noisy measurements, some drift, especially in the positionestimate, is expected. Taking advantage of the knowledge about the tracked sub-ject, a human body, models have been developed that improve the estimates, butposition still displays drift over time.In this thesis, a GNSS receiver is added to the motion capture system to givea drift-free measurement of the position as well as a velocity measurement. Theinertial data and the GNSS data complements each other well, particularly interms of observability of global and relative motions. To enable the models of thehuman body at an early stage of the fusion of sensor data, an optimization basedmaximum a posteriori algorithm was used, which is also better suited for thenonlinear system tracked compared to the conventional method of using Kalmanfilters.One of the models that improves the position estimate greatly, without addingadditional sensing, is the contact detection, with which the velocity of a segmentis set to zero whenever it is considered stationary in comparison to the surround-ing environment, e.g. when a foot touches the ground. This thesis looks at botha scenario when this contact detection can be applied and a scenario where itcannot be applied, to see what possibilities an addition of GNSS sensor couldbring to the human body motion tracking case. The results display a notable im-provement in position, both with and without contact detection. Furthermore,the heading estimate is improved at a full-body scale and the solution makes theestimates depend less on acceleration bias estimation.These results show great potential for more accurate estimates outdoors andcould prove valuable for enabling motion tracking of scenarios where the contactdetection model cannot be used, such as e.g. biking.

The purpose of this paper is to investigate the possibility of replacing data from highspeed filming (Qualisys motion capture) with data from Inertial Measurement Units (X-io technologies), when used to run a model of torso and pelvis in OpenSim. Qualisys motion capture data is used as the golden standard to validate the result visually and with Bland-Altman plots. In order to obtain comparable data experiments are conducted where both methods of collecting data are used simultaneously. Data from the IMU's then need to be processed in Matlab before it can be used to run the OpenSim modell. Several Matlab programs rotate the IMU data to a static reference frame, filter and integrate it, then create viritual markers that correspond to Qualisys' optical markers. The conclusion is that using IMU as a method for collecting data can replace Qualisys in some applications, but not in ones that require high precision. However, this paper only begins the examination of IMU's and there are most likely improvements to be made.

There exist numerous commercial six-degree-ofreedom inertial measurement units capable of measuring low-range accelerations and rotation rates. A commercially available multi-range IMU capable of measuring low and high-range motions does not exist. An IMU with this capability was developed for measuring trajectory data of projectiles such as high-powered rockets. This data can be used to provide performance feedback to projectile designers and users. A small footprint printed circuit board was designed to minimize the overall size of the unit, compared to “perf-board” prototypes. Several PCB design guidelines were closely followed to reduce electrical interference in digital/analog components and traces. Embedded C code was developed to control the IMU. The unit features a wireless user interface, providing several control options, including an option to download data sampled at 1KHz per sweep of all twelve sensor channels. Preliminary testing reveals good consistency among the high and low-range sensors and acceptably low noise levels.

La localisation précise d’un train sur la voie ferrée est une information vitale pour la gestion du trafic et la sécurité des passagers. Le système européen de contrôle des trains (ETCS) embarque ainsi un accéléromètre mono axe mesurant l’accélération longitudinale du train. Ce capteur est l’un des nombreux capteurs présents à bord permettant une odométrie précise. Cependant, sa mesure est faussée par la projection de la gravité sur l’axe sensible en fonction de l’inclinaison de la voie. L’objectif de ce mémoire est donc d’augmenter l’intérêt de ce capteur en développant une solution basée sur une centrale inertielle complète dans le but de fournir une accélération longitudinale fiable. Pour cela, un estimateur d’état a été développé à partir d’un filtre de Kalman étendu et de la prise en compte de contraintes sur le vecteur d’état. L’utilisation d’une équation de réactualisation du gain de Kalman force ainsi l’estimation d’état à évoluer dans un espace contraint. De plus, le vecteur d’observation du système a été augmenté par les informations fournies par un estimateur de vitesse et un estimateur d’attitude du train. L’estimateur de vitesse utilise une analyse fréquentielle des mesures accélérométriques et l’estimateur d’attitude exploite la complémentarité fréquentielle des mesures gyrométriques et accélérométriques pour estimer les angles de roulis et de tangage. Ces informations sont ensuite fusionnées avec les mesures de la centrale. Enfin, des expérimentations ont été réalisées en Turquie dans un train et les performances de l’estimateur ont été validées en comparant les résultats obtenus aux données fournies par une centrale de navigation haut de gamme. / The precise location of a train on the rail network is vital information for traffic management and passenger safety. The European Train Control System (ETCS) features a single-axis accelerometer which measures the longitudinal acceleration of the train. This sensor is one of many sensors onboard providing a precise odometry. However, its measurement is corrupted by the projection of the gravity on the sensitive axis as a function of the inclination of the track. The purpose of this work is to increase the value of this sensor by developing a solution based on a complete inertial system in order to provide a reliable longitudinal acceleration. For this, a state estimator was developed based on an extended Kalman filter and the consideration of constraints on the state vector. The use of updating equation of the Kalman gain forces the state estimation to evolve in a constrained space. In addition, the observation vector has been increased with the information provided by a velocity estimator and a train attitude estimator. The velocity estimator uses a frequency analysis of the accelerometer measurements and the attitude estimator operates the frequency complementarity of gyro and accelerometer measurements, to estimate the roll and pitch angles. This information is then merged with the measurements of the IMU. Finally, experiments were carried out in Turkey on a train and the estimator's performance was validated by comparing the results with data from a high-performance inertial navigation system.

Indoor positioning is a rapidly growing research area, enabling new innovative location-aware applications and user-oriented services. Location Fingerprinting (LF) is the positioning technique of coupling a physical location with observed radio signal measurements. In the terms of indoor LF using Wireless Local Area Network (WLAN) it refers to the use of network measurements from the WLAN Access Points (APs) to tag known locations. A data set is created containing reference fingerprints for the area of interest and is known as a radio map. A radio map can later be used to find a user's location in the area of interest. WLAN infrastructures are vulnerable to many kinds of faults and malicious attacks, including, an attacker jamming the signal from an AP, or an AP becoming unavailable during positioning due to power outage. These faults can be collectively characterized as an AP-failure. In LF positioning systems, AP-failure faults can significantly degrade the performance of a LF system due to the difference between the current fingerprints and radio map created with all APs being available. It is desirable to detect such faulty APs, in order to take actions towards fault-mitigation and restoration, in case of a malicious attack. In this work, we have developed a fault detection algorithm that uses inertial sensors (i.e., accelerometer, magnetometer) available in smartphones to detect AP-failure faults in LF systems. Inertial Measurement Unit (IMU) has become an integral part of all high-end smartphones. IMU can be used to infer location information on the smartphone. The main idea is to have two parallel position streams, the LF positioning and the IMU positioning, and to compare the mean positioning error between the two. Since IMU positioning is fairly accurate once provided with starting coordinates, we use it to detect abnormal behaviour in LF positioning system, such as highly erroneous estimates signifying an AP-failure fault present in the system. The performance of the proposed detection algorithm is evaluated with several real-life AP-related faults. The proposed algorithm exhibits low probability of false alarms in the detection of faulty APs. The conclusion is that using IMU based positioning is an effective and robust solution in terms of fault detection in LF systems.

The need addressed in this thesis is to provide an Autonomous Ground Vehicle (AGV) with accurate information regarding its position, velocity, and orientation. The system chosen to meet these needs incorporates (1) a differential Global Positioning System, (2) an Inertial Measurement Unit consisting of accelerometers and angular-rate sensors, and (3) a Kalman Filter (KF) to fuse the sensor data. The obstacle avoidance software requires position and orientation to build a global map of obstacles based on the returns of a scanning laser rangefinder. The path control software requires position and velocity. The development of the KF is the major contribution of this thesis. This technology can either be purchased or developed, and, for educational and financial reasons, it was decided to develop instead of purchasing the KF software. This thesis analyzes three different cases of navigation: one-dimensional, two dimensional and three-dimensional (general). Each becomes more complex, and separating them allows a three step progression to reach the general motion solution. Three tests were conducted at the Texas A&M University Riverside campus that demonstrated the accuracy of the solution. Starting from a designated origin, the AGV traveled along the runway and then returned to the same origin within 11 cm along the North axis, 19 cm along the East axis and 8 cm along the Down axis. Also, the vehicle traveled along runway 35R which runs North-South within 0.1°, with the yaw solution consistently within 1° of North or South. The final test was mapping a box onto the origin of the global map, which requires accurate linear and angular position estimates and a correct mapping transformation.

With the advent of wireless sensor technologies becoming more and more common-place in wearable devices and smartphones, indoor localization is becoming a heavily researched topic. One such application for this topic is in the medical field where wireless sensor devices that are capable of monitoring patient vitals and giving accurate location estimations allow for a less intrusive environment for nursing home patients. This project explores the usage of using received signal strength indication (RSSI) in conjunction with an inertial navigation system (INS) to provide location estimations without the use of GPS in a Particle Filter with a small development microcontroller and base station. The paper goes over the topics used in this thesis and the results.

No description available.

Road traffic has many negative socioeconomic impacts on society. A key problem is the risk of deadly accidents. The risk, to a high extend is reduced in developed societies. However, the accidents are still ubiquitous. There are various approaches for reducing accidents such as improving the infrastructure, educating better drivers and incentivizing drivers for driving safe. For the latter, the way is to analyse driving behaviour and this is possible using sensors such as inertial measurement units (IMU) without hindering privacy. Insurance companies approach this issue via Usage Based Insurance (UBI) products, where the premium is dynamically calculated by evaluating the driver based on measuring vehicle dynamics and other contextual data. Due to utilization of devices that use different IMUs, generalization of measured data is an issue for correct evaluation and fairness.The thesis deals with providing tools for filling the evaluation gap of IMUs for this purpose. The study began with a survey involving IMUs in the market. Considering technical and economic aspects, the most suitable ones were selected for evaluation. A modular system called quad-IMU (QIMU) was designed and developed. A selected IMU (BMI160) was incorporated into a QIMU and compared to two widely used IMUs in two scenarios; harsh breaking and static measurement using raw digital linear acceleration measurements. Root mean square errors (RMSE) showed that the BMI160 outperformed the others by approximately one and two orders of magnitude, respectively. The QIMU showed to be a promising framework that needs to be explored further for evaluating IMUs in-house in a rapid, low-cost and reliable manner. / Vägtrafiken har många negativa socioekonomiska effekter på samhället. Ett viktigt problem är risken för dödliga olyckor. Risken, i stor utsträckning, minskar i utvecklade samhällen. Olyckorna är dock fortfarande allestädes närvarande. Det finns olika metoder för att minska olyckor som att förbättra infrastrukturen, utbilda bättre förare och incitament för förare att köra säkert. Det sistnämnda kan göras genom att analysera körbeteendet, och detta är möjligt med hjälp av sensorer som tröghetssensorer (IMU) utan att hindra integriteten. Försäkringsbolag närmar sig denna fråga via användningsbaserade försäkringar (UBI) -produkter, där premien dynamiskt beräknas genom att utvärdera föraren baserat på mätning av fordonsdynamik och annan kontextuell data. På grund av användningen av enheter som använder olika IMU-enheter är generalisering av uppmätta data en öppen fråga för korrekt utvärdering och rättvisa.Avhandlingen handlar om att tillhandahålla verktyg för att fylla utvärderingsgapet för IMUer för detta ändamål. Studien började med en undersökning med IMUer på marknaden. Med tanke på tekniska och ekonomiska aspekter valdes de mest lämpliga för utvärdering. Ett modulärt system kallat quad-IMU (QIMU) designades och utvecklades. En vald IMU (BMI160) inkorporerades i en QIMU och jämfördes med två ofta använda IMUer i två scenarier; hård inbromsning och statisk mätning med hjälp av raka digitala linjära accelerationsmätningar. Det genomsnittliga medelkvadratfelet (RMSE) visade att BMI160 överträffade de andra med ungefär en och två storleksordningar. QIMU visade sig vara en lovande ram som behöver undersökas mer för att utvärdera IMUer internt på ett snabbt, billigt och pålitligt sätt.