The level of automation in our society is ever increasing. Technologies like self-driving cars, virtual reality, and fully autonomous robots, which all were unimaginable a few decades ago, are realizable today, and will become standard consumer products in the future. These technologies depend upon autonomous localization and situation awareness where careful processing of sensory data is required. To increase efficiency, robustness and reliability, appropriate models for these data are needed.In this thesis, such models are analyzed within three different application areas, namely (1) magnetic localization, (2) extended target tracking, and (3) autonomous learning from raw pixel information. Magnetic localization is based on one or more magnetometers measuring the induced magnetic field from magnetic objects. In this thesis we present a model for determining the position and the orientation of small magnets with an accuracy of a few millimeters. This enables three-dimensional interaction with computer programs that cannot be handled with other localization techniques. Further, an additional model is proposed for detecting wrong-way drivers on highways based on sensor data from magnetometers deployed in the vicinity of traffic lanes. Models for mapping complex magnetic environments are also analyzed. Such magnetic maps can be used for indoor localization where other systems, such as GPS, do not work. In the second application area, models for tracking objects from laser range sensor data are analyzed. The target shape is modeled with a Gaussian process and is estimated jointly with target position and orientation. The resulting algorithm is capable of tracking various objects with different shapes within the same surveillance region. In the third application area, autonomous learning based on high-dimensional sensor data is considered. In this thesis, we consider one instance of this challenge, the so-called pixels to torques problem, where an agent must learn a closed-loop control policy from pixel information only. To solve this problem, high-dimensional time series are described using a low-dimensional dynamical model. Techniques from machine learning together with standard tools from control theory are used to autonomously design a controller for the system without any prior knowledge. System models used in the applications above are often provided in continuous time. However, a major part of the applied theory is developed for discrete-time systems. Discretization of continuous-time models is hence fundamental. Therefore, this thesis ends with a method for performing such discretization using Lyapunov equations together with analytical solutions, enabling efficient implementation in software. / Hur kan man få en dator att följa pucken i bordshockey för att sammanställa match-statistik, en pensel att måla virtuella vattenfärger, en skalpell för att digitalisera patologi, eller ett multi-verktyg för att skulptera i 3D?  Detta är fyra applikationer som bygger på den patentsökta algoritm som utvecklats i avhandlingen. Metoden bygger på att man gömmer en liten magnet i verktyget, och placerar ut ett antal tre-axliga magnetometrar - av samma slag som vi har i våra smarta telefoner - i ett nätverk kring vår arbetsyta. Magnetens magnetfält ger upphov till en unik signatur i sensorerna som gör att man kan beräkna magnetens position i tre frihetsgrader, samt två av dess vinklar. Avhandlingen tar fram ett komplett ramverk för dessa beräkningar och tillhörande analys. En annan tillämpning som studerats baserat på denna princip är detektion och klassificering av fordon. I ett samarbete med Luleå tekniska högskola med projektpartners har en algoritm tagits fram för att klassificera i vilken riktning fordonen passerar enbart med hjälp av mätningar från en två-axlig magnetometer. Tester utanför Luleå visar på i princip 100% korrekt klassificering. Att se ett fordon som en struktur av magnetiska dipoler i stället för en enda stor, är ett exempel på ett så kallat utsträckt mål. I klassisk teori för att följa flygplan, båtar mm, beskrivs målen som en punkt, men många av dagens allt noggrannare sensorer genererar flera mätningar från samma mål. Genom att ge målen en geometrisk utsträckning eller andra attribut (som dipols-strukturer) kan man inte enbart förbättra målföljnings-algoritmerna och använda sensordata effektivare, utan också klassificera målen effektivare. I avhandlingen föreslås en modell som beskriver den geometriska formen på ett mer flexibelt sätt och med en högre detaljnivå än tidigare modeller i litteraturen. En helt annan tillämpning som studerats är att använda maskininlärning för att lära en dator att styra en plan pendel till önskad position enbart genom att analysera pixlarna i video-bilder. Metodiken går ut på att låta datorn få studera mängder av bilder på en pendel, i det här fallet 1000-tals, för att förstå dynamiken av hur en känd styrsignal påverkar pendeln, för att sedan kunna agera autonomt när inlärningsfasen är klar. Tekniken skulle i förlängningen kunna användas för att utveckla autonoma robotar. / <p>In the electronic version figure 2.2a is corrected.</p> / COOPLOC

<p dir="ltr">There is growing research in analyzing human gait in the context of various applications. This has been aided by the improvement in sensing technologies and computation power. A complex motor skill that it is, gait has found its use in medicine for diagnosing different neurological ailments and injuries. In sports, gait can be used to provide feedback to the player/athlete to improve his/her skill and to prevent injuries. In biometrics, gait can be used to identify and authenticate individuals. This can be easier to scale to perform biometrics of individuals in large crowds compared to conventional biometric methods. In the field of Human Computer Interaction (HCI), gait can be an additional input that could be provided to be used in applications such as video games. Gait analysis has also been used for Human Activity Recognition (HAR) for purposes such as personal fitness, elderly care and rehabilitation. </p><p dir="ltr">The current state-of-the-art methods for gait analysis involves non-wearable technology due to its superior performance. The sophistication afforded in non-wearable technologies, such as cameras, is better able to capture gait information as compared to wearables. However, non-wearable systems are expensive, not scalable and typically, inaccessible to the general public. These systems sometimes need to be set up in specialized clinical facilities by experts. On the other hand, wearables offer scalability and convenience but are not able to match the performance of non-wearables. So the current work is a step in the direction to bridge the gap between the performance of non-wearable systems and the convenience of wearables. </p><p dir="ltr">A magnetic tracking system is developed to be applied for gait analysis. The system performs position and orientation tracking, i.e. 6 degrees of freedom or 6DoF tracking. One or more tracker modules, called Rx modules, is tracked with respect to a module called the Tx module. The Tx module mainly consists of a magnetic field generating coil, Inertial Measurement Unit (IMU) and magnetometer. The Rx module mainly consists of a tri-axis sensing coil, IMU and magnetometer. The system is minimally intrusive, works with Non-Line-of-Sight (NLoS) condition, low power consuming, compact and light weight. </p><p dir="ltr">The magnetic tracking system has been applied to the task of Human Activity Recognition (HAR) in this work as a proof-of-concept. The tracking system was worn by participants, and 4 activities - walking, walking with weight, marching and jogging - were performed. The Tx module was worn on the waist and the Rx modules were placed on the feet. To compare magnetic tracking with the most commonly used wearable sensors - IMUs + magnetometer - the same system was used to provide IMU and magnetometer data for the same 4 activities. The gait data was processed by 2 commonly used deep learning models - Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The magnetic tracking system shows an overall accuracy of 92\% compared to 86.69\% of the IMU + magnetometer system. Moreover, an accuracy improvement of 8\% is seen with the magnetic tracking system in differentiating between the walking and walking with weight activities, which are very similar in nature. This goes to show the improvement in gait information that 6DoF tracking brings, that manifests as increased classification accuracy. This increase in gait information will have a profound impact in other applications of gait analysis as well.</p>

För att vi ska ha en förståelse av det förflutna måste arkeologer ha möjlighet att tolka arkeologiska fynd (Why Is Archaeology Important?, 2018). En typ av fynd är kilskrift. För att säkerställa bevaringen av denna typ av fynd har etiska principer etablerats som exempelvis inte låter någon utsätta fynden för fara ("Principles of Archaeological Ethics", 2018), till exempel i form av förvittring genom beröring. På grund av detta har digitala arbetssätt börjat etableras vid exempelvis rekonstruktionsarbete av kilskrift så att arbetet kan utföras utan direkt beröring (Fisseler, Müller &amp; Weichert, 2017). Problemet är att denna typ av arbete upplevs som frustrerande av arkeologerna då det nya digitala arbetssättet skiljer sig från deras tidigare analoga arbetssätt (Woolley et al., 2017). För att lösa denna problematik har en konceptdriven metod använts i uppsatsen. Det har skapats ett digitalt koncept kallat ARCC som kan låta arkeologer arbeta på liknande vis som det analoga naturliga arbetssättet utan att beröra kilskriften. Konceptet ARCC är en kombination av teknologierna: augmented reality, magnetic tracking systems och 3D-printing. Detta koncept är ett exempel på möjligheterna som existerar angående ett praktiskt och etiskt arbetssätt i förhållande till rekonstruktionsarbete av kilskrift. ARCC kan vidareutvecklas för att stärka konceptet, samt också agera indikator på att fler möjligheter inom detta område existerar. / In order to have an understanding of the past archaeologists need to be able to interpret archaeological finds (Why Is Archaeology Important?, 2018). One type of find is cuneiform. Ethical principles has been established to secure the conservation of this kind of find by prohibiting handling that could cause weathering ("Principles of Archaeological Ethics", 2018). Because of this, digital work methods has been established in conjunction with reconstruction of cuneiform in order to eliminate the risk of weathering through touch (Fisseler, Müller &amp; Weichert, 2017). The problem is that this kind of work is found to be frustrating by the archaeologists since this way of work differs from their usual analog work method (Woolley et al., 2017). To solve this problem we have used a concept driven method. The digital concept ARCC has been developed which allows archaeologists to use their usual work method without touching the cuneiform. The concept ARCC is a combination of the technologies: augmented reality, magnetic tracking systems and 3D-printing. This concept is an example of the possibilities that exist regarding a practical and ethical work method regarding reconstruction of cunaiform. ARCC can be further developed to improve the concept, it could also act as an indicator of further possibilities within this area