Regular physical activity is essential to maintain or even improve an individual’s health. There exist various guidelines on how much individuals should do. Therefore, it is important to monitor performed physical activities during people’s daily routine in order to tell how far they meet professional recommendations. This thesis follows the goal to develop a mobile, personalized physical activity monitoring system applicable for everyday life scenarios. From the mentioned recommendations, this thesis concentrates on monitoring aerobic physical activity. Two main objectives are defined in this context. On the one hand, the goal is to estimate the intensity of performed activities: To distinguish activities of light, moderate or vigorous effort. On the other hand, to give a more detailed description of an individual’s daily routine, the goal is to recognize basic aerobic activities (such as walk, run or cycle) and basic postures (lie, sit and stand).
With recent progress in wearable sensing and computing the technological tools largely exist nowadays to create the envisioned physical activity monitoring system. Therefore, the focus of this thesis is on the development of new approaches for physical activity recognition and intensity estimation, which extend the applicability of such systems. In order to make physical activity monitoring feasible in everyday life scenarios, the thesis deals with questions such as 1) how to handle a wide range of e.g.
everyday, household or sport activities and 2) how to handle various potential users. Moreover, this thesis deals with the realistic scenario where either the currently performed activity or the current user is unknown during the development and training
phase of activity monitoring applications. To answer these questions, this thesis proposes and developes novel algorithms, models and evaluation techniques, and performs thorough experiments to prove their validity.
The contributions of this thesis are both of theoretical and of practical value. Addressing the challenge of creating robust activity monitoring systems for everyday life the concept of other activities is introduced, various models are proposed and validated. Another key challenge is that complex activity recognition tasks exceed the potential of existing classification algorithms. Therefore, this thesis introduces a confidence-based extension of the well known AdaBoost.M1 algorithm, called ConfAdaBoost.M1. Thorough experiments show its significant performance improvement compared to commonly used boosting methods. A further major theoretical contribution is the introduction and validation of a new general concept for the personalization of physical activity recognition applications, and the development of a novel algorithm (called Dependent Experts) based on this concept. A major contribution of practical value is the introduction of a new evaluation technique (called leave-one-activity-out) to simulate when performing previously unknown activities in a physical activity monitoring system. Furthermore, the creation and benchmarking of publicly available physical activity monitoring datasets within this thesis are directly benefiting the research community. Finally, the thesis deals with issues related to the implementation of the proposed methods, in order to realize the envisioned mobile system and integrate it into a full healthcare application for aerobic activity monitoring and support in daily life.
It has been observed that for understanding the biological function of certain RNA molecules, one has to study joint secondary structures of interacting pairs of RNA. In this thesis, a new approach for predicting the joint structure is proposed and implemented. For this, we introduce the class of m-dimensional context-free grammars --- an extension of stochastic context-free grammars to multiple dimensions --- and present an Earley-style semiring parser for this class. Additionally, we develop and thoroughly discuss an implementation variant of Earley parsers tailored to efficiently handle dense grammars, which embraces the grammars used for structure prediction. A currently proposed partitioning scheme for joint secondary structures is transferred into a two-dimensional context-free grammar, which in turn is used as a stochastic model for RNA-RNA interaction. This model is trained on actual data and then used for predicting most likely joint structures for given RNA molecules. While this technique has been widely used for secondary structure prediction of single molecules, RNA-RNA interaction was hardly approached this way in the past. Although our parser has O(n^3 m^3) time complexity and O(n^2 m^2) space complexity for two RNA molecules of sizes n and m, it remains practically applicable for typical sizes if enough memory is available. Experiments show that our parser is much more efficient for this application than classical Earley parsers. Moreover the predictions of joint structures are comparable in quality to current energy minimization approaches.
Modern digital imaging technologies, such as digital microscopy or micro-computed tomography, deliver such large amounts of 2D and 3D-image data that manual processing becomes infeasible. This leads to a need for robust, flexible and automatic image analysis tools in areas such as histology or materials science, where microstructures are being investigated (e.g. cells, fiber systems). General-purpose image processing methods can be used to analyze such microstructures. These methods usually rely on segmentation, i.e., a separation of areas of interest in digital images. As image segmentation algorithms rarely adapt well to changes in the imaging system or to different analysis problems, there is a demand for solutions that can easily be modified to analyze different microstructures, and that are more accurate than existing ones. To address these challenges, this thesis contributes a novel statistical model for objects in images and novel algorithms for the image-based analysis of microstructures. The first contribution is a novel statistical model for the locations of objects (e.g. tumor cells) in images. This model is fully trainable and can therefore be easily adapted to many different image analysis tasks, which is demonstrated by examples from histology and materials science. Using algorithms for fitting this statistical model to images results in a method for locating multiple objects in images that is more accurate and more robust to noise and background clutter than standard methods. On simulated data at high noise levels (peak signal-to-noise ratio below 10 dB), this method achieves detection rates up to 10% above those of a watershed-based alternative algorithm. While objects like tumor cells can be described well by their coordinates in the plane, the analysis of fiber systems in composite materials, for instance, requires a fully three dimensional treatment. Therefore, the second contribution of this thesis is a novel algorithm to determine the local fiber orientation in micro-tomographic reconstructions of fiber-reinforced polymers and other fibrous materials. Using simulated data, it will be demonstrated that the local orientations obtained from this novel method are more robust to noise and fiber overlap than those computed using an established alternative gradient-based algorithm, both in 2D and 3D. The property of robustness to noise of the proposed algorithm can be explained by the fact that a low-pass filter is used to detect local orientations. But even in the absence of noise, depending on fiber curvature and density, the average local 3D-orientation estimate can be about 9° more accurate compared to that alternative gradient-based method. Implementations of that novel orientation estimation method require repeated image filtering using anisotropic Gaussian convolution filters. These filter operations, which other authors have used for adaptive image smoothing, are computationally expensive when using standard implementations. Therefore, the third contribution of this thesis is a novel optimal non-orthogonal separation of the anisotropic Gaussian convolution kernel. This result generalizes a previous one reported elsewhere, and allows for efficient implementations of the corresponding convolution operation in any dimension. In 2D and 3D, these implementations achieve an average performance gain by factors of 3.8 and 3.5, respectively, compared to a fast Fourier transform-based implementation. The contributions made by this thesis represent improvements over state-of-the-art methods, especially in the 2D-analysis of cells in histological resections, and in the 2D and 3D-analysis of fibrous materials.
Objective: In some surgical specialties, e.g. orthopedics, robots are already used in the operating room for bony milling work. Oto- and otoneurosurgery may also greatly benefit by robotic enhanced precision. Study Design: Experimental study on robotic milling on oak wood and human temporal bone specimen. Methods: A standard industrial robot with a 6 degrees-of-freedom serial kinematics was used with force feedback to proportionally control the robot speed. Different milling modes and characteristic path parameters were evaluated to generate milling paths based on CAD geometry data of a cochlear implant and an implantable hearing system. Results: The best suited strategy proofed to be the spiral horizontal milling mode with the burr held perpendicularly to the temporal bone surface. In order to avoid high grooves, the distance in between paths should equal half the radius of the cutting burr head. Due to the vibration of the robot’s own motors, a rather high oscillation of the standard deviation of forces was encountered. This oscillation dropped drastically to nearly 0 N, when the burr head reached contact with the dura mater due to its damping characteristics. The cutting burr could be moved a long time on the dura without damaging it, because of its rather blunt head. The robot moved the burr very smoothly according to the encountered resistances. Conclusion: This is the first development of an functioning robotic milling procedure for otoneurosurgery with force-based speed control. It is planned to implement ultrasound-based local navigation and to perform robotic mastoidectomy.