Repetitive tasks, awkward hand/wrist postures and forceful exertions are known risk factors for work-related musculoskeletal disorders (WMSDs) of the hand and wrist. WMSD is a major cause of long work absence, productivity loss, loss in wages and individual suffering. Currently available assessment methods of the hand/wrist motion have the limitations of being inaccurate, e.g. when using self-reports or observations, or expensive and resource-demanding for following analyses, e.g. when using the electrogoniometers. Therefore, there is a need for a risk assessment method that is easy-to-use and can be applied by both researchers and practitioners for measuring wrist angular velocity during an 8-hour working day. Wearable Inertial Measurement Units (IMU) in combination with mobile phone applications provide the possibility for such a method. In order to apply the IMU in the field for assessing the wrist velocity of different work tasks, the accuracy of the method need to be examined. Therefore, this laboratory experiment was conducted to compare a new IMU-based method with the traditional goniometer and standard optical motion capture system. The laboratory experiment was performed on twelve participants. Three standard hand movements, including hand/wrist motion of Flexion-extension (FE), Deviation, and Pronationsupination (PS) at 30, 60, 90 beat-per-minute (bpm), and three simulated work tasks were performed. The angular velocity of the three methods at 50th and 90th percentile were calculated and compared. The mean absolute error and correlation coefficient were analysed for comparing the methods. Increase in error was observed with increase in speed/bpm during the standard hand movements. For standard hand movements, comparison between IMUbyaxis and Goniometer had the smallest difference and highest correlation coefficient. For simulated work tasks, the difference between goniometer and optical system was the smallest. However, for simulated work tasks, the differences between the compared methods were in general much larger than the standard hand movements. The IMU-based method is seen to have potential when compared with the traditional measurement methods. Still, it needs further improvement to be used for risk assessment in the field. / Upprepade uppgifter, besvärliga hand- / handledsställningar och kraftfulla ansträngningar är kända riskfaktorer för arbetsrelaterade muskuloskeletala störningar (WMSD) i hand och handled. WMSD är en viktig orsak till lång frånvaro, produktivitetsförlust, löneförlust och individuellt lidande. För närvarande tillgängliga bedömningsmetoder för hand / handledsrörelser har begränsningarna att vara felaktiga, t.ex. när du använder självrapporter eller observationer, eller dyra och resurskrävande för följande analyser, t.ex. när du använder elektrogoniometrarna. Därför finns det ett behov av en riskbedömningsmetod som är enkel att använda och som kan användas av både forskare och utövare för att mäta handledens vinkelhastighet under en 8-timmars arbetsdag. Wearable Inertial Measuring Units (IMU) i kombination med mobiltelefonapplikationer ger möjlighet till en sådan metod. För att kunna använda IMU i fältet för att bedöma handledens hastighet för olika arbetsuppgifter måste metodens noggrannhet undersökas. Därför genomfördes detta laboratorieexperiment för att jämföra en ny IMU-baserad metod med den traditionella goniometern och det vanliga optiska rörelsefångningssystemet. Laboratorieexperimentet utfördes på tolv deltagare. Tre standardhandrörelser, inklusive hand / handledsrörelse av Flexion-extension (FE), Deviation och Pronation-supination (PS) vid 30, 60, 90 beat-per-minut (bpm) och tre simulerade arbetsuppgifter utfördes. Vinkelhastigheten för de tre metoderna vid 50: e och 90: e percentilen beräknades och jämfördes. Det genomsnittliga absoluta felet och korrelationskoefficienten analyserades för att jämföra metoderna. Ökning av fel observerades med ökning av hastighet/bpm under standardhandrörelserna. För standardhandrörelser hade jämförelsen mellan IMUbyaxis och Goniometer den minsta skillnaden och högsta korrelationskoefficienten. För simulerade arbetsuppgifter var skillnaden mellan goniometer och optiskt system den minsta. För simulerade arbetsuppgifter var dock skillnaderna mellan de jämförda metoderna i allmänhet mycket större än de vanliga handrörelserna. Den IMUbaserade metoden anses ha potential jämfört med traditionella mätmetoder. Ändå behöver det förbättras för att kunna användas för riskbedömning på fältet.

Um den Einfluss des Sitzpolsters in Radhosen auf den mechanischen Komfort besser zu verstehen, wurden der Satteldruck und die Beckenbewegung in Abhängigkeit der Belastungsintensität sowie -dauer beim Radfahren analysiert. Die Ergebnisse der initialen Datenanalyse deuten darauf hin, dass weniger die Stabilitätsparameter, sondern eher die Druckwahrnehmung für die Beurteilung des mechanischen Komforts maßgeblich ist. / In order to better understand the influence of the seat pad in cycling shorts on mechanical comfort, saddle pressure and pelvic movement were analyzed as a function of load intensity as well as duration during cycling. The results of the initial data analysis indicate that it is not so much the stability parameters but rather the pressure perception that is decisive for the assessment of mechanical comfort.

Diese Arbeit zeigt den explorativen Ansatz ein künstliches neuronales Netz (kNN) und Daten einer auf dem Ski befindlichen inertialen Messeinheit (IMU) zu verwenden, um auf die Fahrgeschwindigkeit zu schließen. Für das Training des kNN wird dabei die 3D Geschwindigkeit einer auf dem Ski befindlichen GNSS-Antenne als Zielwert verwendet. / This work shows the exploratory approach of using an artificial neural network (kNN) and data from an inertial measurement unit (IMU) located on the ski to infer the ski speed. For the training of the kNN, the 3D speed of a GNSS antenna on the ski is used as a target value.

Les systèmes multi-capteurs exploitent les complémentarités des différentes sources sensorielles. Par exemple, le capteur visuo-inertiel permet d’estimer la pose à haute fréquence et avec une grande précision. Les méthodes de vision mesurent la pose à basse fréquence mais limitent la dérive causée par l’intégration des données inertielles. Les centrales inertielles mesurent des incréments du déplacement à haute fréquence, ce que permet d’initialiser la vision et de compenser la perte momentanée de celle-ci. Cette thèse analyse deux aspects du problème. Premièrement, nous étudions les méthodes visuelles directes pour l’estimation de pose, et proposons une nouvelle technique basée sur la corrélation entre des images et la pondération des régions et des pixels, avec une optimisation inspirée de la méthode de Newton. Notre technique estime la pose même en présence des changements d’illumination extrêmes. Deuxièmement, nous étudions la fusion des données a partir de la théorie de la commande. Nos résultats principaux concernent le développement d’observateurs pour l’estimation de pose, biais IMU et l’autocalibrage. Nous analysons la dynamique de rotation d’un point de vue non linéaire, et fournissons des observateurs stables dans le groupe des matrices de rotation. Par ailleurs, nous analysons la dynamique de translation en tant que système linéaire variant dans le temps, et proposons des conditions d’observabilité uniforme. Les analyses d’observabilité nous permettent de démontrer la stabilité uniforme des observateurs proposés. La méthode visuelle et les observateurs sont testés et comparés aux méthodes classiques avec des simulations et de vraies données visuo-inertielles. / Systems with multiple sensors can provide information unavailable from a single source, and complementary sensory characteristics can improve accuracy and robustness to many vulnerabilities as well. Explicit pose measurements are often performed either with high frequency or precision, however visuo-inertial sensors present both features. Vision algorithms accurately measure pose at low frequencies, but limit the drift due to integration of inertial data. Inertial measurement units yield incremental displacements at high frequencies that initialize vision algorithms and compensate for momentary loss of sight. This thesis analyzes two aspects of that problem. First, we survey direct visual tracking methods for pose estimation, and propose a new technique based on the normalized crosscorrelation, region and pixel-wise weighting together with a Newton-like optimization. This method can accurately estimate pose under severe illumination changes. Secondly, we investigate the data fusion problem from a control point of view. Main results consist in novel observers for concurrent estimation of pose, IMU bias and self-calibration. We analyze the rotational dynamics using tools from nonlinear control, and provide stable observers on the group of rotation matrices. Additionally, we analyze the translational dynamics using tools from linear time-varying systems, and propose sufficient conditions for uniform observability. The observability analyses allow us to prove uniform stability of the observers proposed. The proposed visual method and nonlinear observers are tested and compared to classical methods using several simulations and experiments with real visuo-inertial data.

Inertial Measurement Unit (IMU) and Global Positioning System (GPS) receivers are sensors that are widely used for land vehicle navigation. GPS receivers provide position and/or velocity data to any user on the Earth&rsquo / s surface independent of his position. Yet, there are some conditions that the receiver encounters difficulties, such as weather conditions and some blockage problems due to buildings, trees etc. Due to these difficulties, GPS receivers&rsquo / errors increase. On the other hand, IMU works with respect to Newton&rsquo / s laws. Thus, in stark contrast with other navigation sensors (i.e. radar, ultrasonic sensors etc.), it is not corrupted by external signals. Owing to this feature, IMU is used in almost all navigation applications. However, it has some disadvantages such as possible alignment errors, computational errors and instrumentation errors (e.g., bias, scale factor, random noise, nonlinearity etc.). Therefore, a fusion or integration of GPS and IMU provides a more accurate navigation data compared to only GPS or only IMU navigation data. v In this thesis, loosely coupled GPS/IMU integration systems are implemented using feed forward and feedback configurations. The mechanization equations, which convert the IMU navigation data (i.e. acceleration and angular velocity components) with respect to an inertial reference frame to position, velocity and orientation data with respect to any desired frame, are derived for the geographical frame. In other words, the mechanization equations convert the IMU data to the Inertial Navigation System (INS) data. Concerning this conversion, error model of INS is developed using the perturbation of the mechanization equations and adding the IMU&rsquo / s sensor&rsquo / s error model to the perturbed mechanization equation. Based on this error model, a Kalman filter is constructed. Finally, current navigation data is calculated using IMU data with the help of the mechanization equations. GPS receiver supplies external measurement data to Kalman filter. Kalman filter estimates the error of INS using the error mathematical model and current navigation data is updated using Kalman filter error estimates. Within the scope of this study, some real experimental tests are carried out using the software developed as a part of this study. The test results verify that feedback GPS/INS integration is more accurate and reliable than feed forward GPS/INS. In addition, some tests are carried out to observe the results when the GPS receiver&rsquo / s data lost. In these tests also, the feedback GPS/INS integration is observed to have better performance than the feed forward GPS/INS integration.

The thesis work evaluates a method to estimate the volume of stone and gravelpiles using only a cellphone to collect video and sensor data from the gyroscopesand accelerometers. The project is commissioned by Escenda Engineering withthe motivation to replace more complex and resource demanding systems with acheaper and easy to use handheld device. The implementation features popularcomputer vision methods such as KLT-tracking, Structure-from-Motion, SpaceCarving together with some Sensor Fusion. The results imply that it is possible toestimate volumes up to a certain accuracy which is limited by the sensor qualityand with a bias. / I rapporten framgår hur volymen av storskaliga objekt, nämligen grus-och stenhögar,kan bestämmas i utomhusmiljö med hjälp av en mobiltelefons kamerasamt interna sensorer som gyroskop och accelerometer. Projektet är beställt avEscenda Engineering med motivering att ersätta mer komplexa och resurskrävandesystem med ett enkelt handhållet instrument. Implementationen använderbland annat de vanligt förekommande datorseendemetoderna Kanade-Lucas-Tommasi-punktspårning, Struktur-från-rörelse och 3D-karvning tillsammans medenklare sensorfusion. I rapporten framgår att volymestimering är möjligt mennoggrannheten begränsas av sensorkvalitet och en bias.

Goal of this thesis is localization of mobile robot in OpenStreet map segment. Robot IMU, odometry and compass sensors are used for trajectory reconstruction, which is compared to reference GPS trajectory. Extended Monte Carlo localization and clusterization are used for robot localization. Software is implemented in C++ with ROS middleware.

This thesis deals with multi-axis position sensors, their nature and individual parts and types of these sensors. It is outlined their historical origin and the most used modern types, such as micro-electro-mechanical sensors. Further, the properties of these sensors, error sources and their compensation are discussed. Output data formats are also discussed here, such as Euler angles, their calculation and applications. The selected sensor is implemented in the simulator electronics, and the graphical application demonstrates its functions.

The ability to position yourself and map the surroundings is an important aspect for both civilian and military applications. Global navigation satellite systems are very popular and are widely used for positioning. This kind of system is however quite easy to disturb and therefore lacks robustness. The introduction of autonomous vehicles has accelerated the development of local positioning systems. This thesis work is done in collaboration with FOI in Linköping, using a positioning system with LIDAR and IMU sensors in a EKF-SLAM system using the GTSAM framework. The goal was to evaluate the system in different conditions and also investigate the possibility of using the road surface for positioning. Data available at FOI was used for evaluation. These data sets have a known sensor setup and matches the intended hardware. The data sets used have been gathered on three different occasions in a residential area, a country road and a forest road in sunny spring weather on two occasions and one occasion in winter conditions. To evaluate the performance several different measures were used, common ones such as looking at positioning error and RMSE, but also the number of found landmarks, the estimated distance between landmarks and the drift of the vehicle. All results pointed towards the forest road providing the best positioning, the country road the worst and the residential area in between. When comparing different weather conditions the data set from winter conditions performed the best. The difference between the two spring data sets was quite different which indicates that there may be other factors at play than just weather. A road edge detector was implemented to improve mapping and positioning. Vectors, denoted road vectors, with position and orientation were adapted to the edge points and the change between these road vectors were used in the system using GTSAM in areas with few landmarks. The clearest improvements to the drift in the vehicle direction was in the longer country area where the error was lowered with 6.4 % with increase in the error sideways and in orientation as side effects. The implemented method has a significant impact on the computational cost of the system as well as requiring precise adjustment of uncertainty to have a noticeable improvement and not worsen the overall results.

Indiana University-Purdue University Indianapolis (IUPUI) / Soft tissue manipulation is a widely used practice by manual therapists from a variety of healthcare disciplines to evaluate and treat neuromusculoskeletal impairments using mechanical stimulation either by hand massage or specially-designed tools. The practice of a specific approach of targeted pressure application using distinguished rigid mechanical tools to breakdown adhesions, scar tissues and improve range of motion for affected joints is called Instrument-Assisted Soft Tissue Manipulation (IASTM). The efficacy of IASTM has been demonstrated as a means to improve mobility of joints, reduce pain, enhance flexibility and restore function. However, unlike the techniques of ultrasound, traction, electrical stimulation, etc. the practice of IASTM doesn't involve any standard to objectively characterize massage with physical parameters. Thus, most IASTM treatments are subjective to practitioner or patient subjective feedback, which essentially addresses a need to quantify therapeutic massage or IASTM treatment with adequate treatment parameters to document, better analyze, compare and validate STM treatment as an established, state-of-the-art practice. This thesis focuses on the development and implementation of Quantifiable Soft Tissue Manipulation (QSTM™) Technology by designing an ergonomic, portable and miniaturized wired localized pressure applicator medical device (Q1), for characterizing soft tissue manipulation. Dose-load response in terms of forces in Newtons; pitch angle of the device ; stroke frequency of massage measured within stipulated time of treatment; all in real-time has been captured to characterize a QSTM session. A QSTM PC software (Q-WARE©) featuring a Treatment Record System subjective to individual patients to save and retrieve treatment diagnostics and a real-time graphical visual monitoring system has been developed from scratch on WINDOWS platform to successfully implement the technology. This quantitative analysis of STM treatment without visual monitoring has demonstrated inter-reliability and intra-reliability inconsistencies by clinicians in STM force application. While improved consistency of treatment application has been found when using visual monitoring from the QSTM feedback system. This system has also discriminated variabilities in application of high, medium and low dose-loads and stroke frequency analysis during targeted treatment sessions. / 2023-04-26