Physics-Based Probabilistic Motion Compensation of Elastically Deformable Objects

A predictive tracking approach and a novel method for visual motion compensation are introduced, which accurately reconstruct and compensate the deformation of the elastic object, even in the case of complete measurement information loss. The core of the methods involves a probabilistic physical model of the object, from which all other mathematical models are systematically derived. Due to flexible adaptation of the models, the balance between their complexity and their accuracy is achieved

Modeling Emotional Aspects in Human Locomotion

The study of emotional body language has been the effort of many scientists for more than 200 years, from areas such as psychology, neuroscience, biology, and others. A lot of work has focused on the analysis of the kinematics, while the study of the underlying dynamics is still largely unexplored. In this thesis we model human walking as a nonlinear multi-phase optimal control problem to investigate the dynamics of full-body emotional expressions in human locomotion. Our approach is based on rigid multibody dynamics, a highly parameterized mathematical model of the human locomotion system, and the direct multiple-shooting method to analyze the dynamics of recorded kinematic motion capture data. Modeling the dynamics of a human rigid multibody model results in a set of highly complex differential algebraic equations that require automated methods to derive and evaluate. We created a new rigid multibody dynamics software package to model and numerically evaluate kinematic and dynamic quantities of rigid multibody systems expressed in generalized coordinates, including modeling of external contacts and discontinuities arising from contact events. Our package evaluates components of the equation of motion for multibody systems using recursive algorithms that are based on Featherstone's 6-D spatial algebra notation. Our package is specifically tailored for the use in numerical optimal control and carefully designed to exploit sparsities and reduction of redundant computations by selectively reusing computed values. By doing so we are able to achieve and partially exceed performance that is otherwise only available with source code generation modeling approaches. We created a highly parameterized 3-D meta model for the human locomotion system. This rigid multibody model is based on biomechanical data for kinematic and inertial parameters and enables us to create subject-specific dynamic models by adjusting segment dimensions, joint locations, and inertial parameters. To describe the contact between the human model and the ground, we created a non-holonomic rigid body contact model specifically for human walking movements that approximates the foot geometry using a sphere for the heel and a line segment at the ball of the foot during forefoot contact. Transforming motion capture marker data to rigid multibody motion is a difficult problem due to unknown joint centers, redundant marker movements, and non-rigid movement of markers as a result of skin and tissue movement. In this thesis, we developed and implemented a semi-automatic method in which we manually adjust the model to approximate the recorded subject and then compute joint angles by solving a non-linear least-squares optimization problem. Our approach is independent of the used motion capture marker set and directly maps onto the joint space of the model. We formulate two types of multi-phase optimal control problems for human walking: an inverse reconstruction problem and a gait synthesis problem that both have the differential equations of the rigid multibody dynamics as a constraint and can be used for different purposes. The reconstruction problem computes the unknown joint actuations from purely kinematic motion capture data. Applied to the recorded motion capture data, the reconstructed joint actuations show emotion specific features that are also found in the recorded muscle activity. This validates our model and approach to use optimal control problems as a tool to study emotional body language in a new way. Our gait synthesis formulation allows the generation of walking motions solely based on mathematical and physical principles. It can be applied in computer animation, robotics, and predictive gait analysis. We have generated a wide range of motions by adjusting objective function and gait parameters. A long-term goal of this formulation is to investigate optimality criteria of emotional walking motions. For this, we aim to use hierarchical optimal control problems in our future works

Adaptive construction of surrogate functions for various computational mechanics models

In most science and engineering fields, numerical simulation models are often used to replicate physical systems. An attempt to imitate the true behavior of complex systems results in computationally expensive simulation models. The models are more often than not associated with a number of parameters that may be uncertain or variable. Propagation of variability from the input parameters in a simulation model to the output quantities is important for better understanding the system behavior. Variability propagation of complex systems requires repeated runs of costly simulation models with different inputs, which can be prohibitively expensive. Thus for efficient propagation, the total number of model evaluations needs to be as few as possible. An efficient way to account for the variations in the output of interest with respect to these parameters in such situations is to develop black-box surrogates. It involves replacing the expensive high-fidelity simulation model by a much cheaper model (surrogate) using a limited number of the high-fidelity simulations on a set of points called the design of experiments (DoE). The obvious challenge in surrogate modeling is to efficiently deal with simulation models that are expensive and contains a large number of uncertain parameters. Also, replication of different types of physical systems results in simulation models that vary based on the type of output (discrete or continuous models), extent of model output information (knowledge of output or output gradients or both), and whether the model is stochastic or deterministic in nature. All these variations in information from one model to the other demand development of different surrogate modeling algorithms for maximum efficiency. In this dissertation, simulation models related to application problems in the field of solid mechanics are considered that belong to each one of the above-mentioned classes of models. Different surrogate modeling strategies are proposed to deal with these models and their performance is demonstrated and compared with existing surrogate modeling algorithms. The developed algorithms, because of their non-intrusive nature, can be easily extended to simulation models of similar classes, pertaining to any other field of application

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

Language-Learner Computer Interactions

This book focuses on learner-computer interactions (LCI) in second language learning environments drawing largely on sociocultural theories of language development. It brings together a rich and varied range of theoretical discussions and applications in order to illustrate the way in which LCI can enrich our comprehension of technology-mediated communication, hence enhancing learners’ digital literacy skills. The book is based on the premise that, in order to fully understand the nature of language and literacy development in digital spaces, researchers and practitioners in linguistics, sciences and engineering need to borrow from each others’ theoretical and practical toolkits. In light of this premise, themes include such aspects as educational ergonomics, affordances, complex systems learning, learner personas and corpora, while also describing such data collecting tools as video screen capture devices, eye-tracking or intelligent learning tutoring systems

Dependencies in language: On the causal ontology of linguistic systems

Dependency is a fundamental concept in the analysis of linguistic systems. The many if-then statements offered in typology and grammar-writing imply a causally real notion of dependency that is central to the claim being made—usually with reference to widely varying timescales and types of processes. But despite the importance of the concept of dependency in our work, its nature is seldom defined or made explicit. This book brings together experts on language, representing descriptive linguistics, language typology, functional/cognitive linguistics, cognitive science, research on gesture and other semiotic systems, developmental psychology, psycholinguistics, and linguistic anthropology to address the following question: What kinds of dependencies exist among language-related systems, and how do we define and explain them in natural, causal terms

Dependencies in language: On the causal ontology of linguistic systems

Dependency is a fundamental concept in the analysis of linguistic systems. The many if-then statements offered in typology and grammar-writing imply a causally real notion of dependency that is central to the claim being made—usually with reference to widely varying timescales and types of processes. But despite the importance of the concept of dependency in our work, its nature is seldom defined or made explicit. This book brings together experts on language, representing descriptive linguistics, language typology, functional/cognitive linguistics, cognitive science, research on gesture and other semiotic systems, developmental psychology, psycholinguistics, and linguistic anthropology to address the following question: What kinds of dependencies exist among language-related systems, and how do we define and explain them in natural, causal terms