Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: James G. Richards, College of Health Sciences. Includes bibliographical references.

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Li, Yun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 68-72). / Abstracts in English and Chinese. / Abstract --- p.I / 摘要 --- p.III / Acknowledgments --- p.IV / List of Contents --- p.VI / List of Figures --- p.VII / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Visual Detection --- p.2 / Chapter 1.2 --- Irregularity Detection --- p.4 / Chapter Chapter 2 --- System Overview --- p.7 / Chapter 2.1 --- Definition of Irregularity --- p.7 / Chapter 2.2 --- Contributions --- p.8 / Chapter 2.3 --- Review of previous work --- p.9 / Chapter 2.3.1 --- Model-based Methods --- p.9 / Chapter 2.3.2 --- Statistical Methods --- p.11 / Chapter 2.4 --- System Outline --- p.14 / Chapter Chapter 3 --- Background Subtraction --- p.16 / Chapter 3.1 --- Related Work --- p.17 / Chapter 3.2 --- Adaptive Mixture Model --- p.18 / Chapter 3.2.1 --- Online Model Update --- p.20 / Chapter 3.2.2 --- Background Model Estimation --- p.22 / Chapter 3.2.3 --- Foreground Segmentation --- p.24 / Chapter Chapter 4 --- Feature Extraction --- p.28 / Chapter 4.1 --- Various Feature Descriptors --- p.29 / Chapter 4.2 --- Histogram of Oriented Gradients --- p.30 / Chapter 4.2.1 --- Feature Descriptor --- p.31 / Chapter 4.2.2 --- Feature Merits --- p.33 / Chapter 4.3 --- Subspace Analysis --- p.35 / Chapter 4.3.1 --- Principal Component Analysis --- p.35 / Chapter 4.3.2 --- Subspace Projection --- p.37 / Chapter Chapter 5 --- Bayesian Probabilistic Inference --- p.39 / Chapter 5.1 --- Estimation of PDFs --- p.40 / Chapter 5.1.1 --- K-Means Clustering --- p.40 / Chapter 5.1.2 --- Kernel Density Estimation --- p.42 / Chapter 5.2 --- MAP Estimation --- p.44 / Chapter 5.2.1 --- ML Estimation & MAP Estimation --- p.44 / Chapter 5.2.2 --- Detection through MAP --- p.46 / Chapter 5.3 --- Efficient Implementation --- p.47 / Chapter 5.3.1 --- K-D Trees --- p.48 / Chapter 5.3.2 --- Nearest Neighbor (NN) Algorithm --- p.49 / Chapter Chapter 6 --- Experiments and Conclusion --- p.51 / Chapter 6.1 --- Experiments --- p.51 / Chapter 6.1.1 --- Outdoor Video Surveillance - Exp. 1 --- p.52 / Chapter 6.1.2 --- Outdoor Video Surveillance - Exp. 2 --- p.54 / Chapter 6.1.3 --- Outdoor Video Surveillance - Exp. 3 --- p.56 / Chapter 6.1.4 --- Classroom Monitoring - Exp.4 --- p.61 / Chapter 6.2 --- Algorithm Evaluation --- p.64 / Chapter 6.3 --- Conclusion --- p.66 / Bibliography --- p.68

We analyze videos in which a hand interacts with a basketball. In this work, we present a computational system which detects and classifies hand-ball events, given the trajectories of a hand and ball. Our approach is to determine non-gravitational parts of the ball's motion using only the motion of the hand as a reliable cue for hand-ball events. This thesis makes three contributions. First, we show that hand motion can be segmented using piecewise fifth-order polynomials inspired by work in motor control. We make the remarkable experimental observation that hand-ball events have a phenomenal correspondence to the segmentation breakpoints. Second, by fitting a context-dependent gravitational model to the ball over an adaptive window, we can isolate places where the hand is causing non-gravitational motion of the ball. Finally, given a precise segmentation, we use the measured velocity steps (force impulses) on the ball to detect and classify various event types.

We analyze videos in which a hand interacts with a basketball. In this work, we present a computational system which detects and classifies hand-ball events, given the trajectories of a hand and ball. Our approach is to determine non-gravitational parts of the ball's motion using only the motion of the hand as a reliable cue for hand-ball events. This thesis makes three contributions. First, we show that hand motion can be segmented using piecewise fifth-order polynomials inspired by work in motor control. We make the remarkable experimental observation that hand-ball events have a phenomenal correspondence to the segmentation breakpoints. Second, by fitting a context-dependent gravitational model to the ball over an adaptive window, we can isolate places where the hand is causing non-gravitational motion of the ball. Finally, given a precise segmentation, we use the measured velocity steps (force impulses) on the ball to detect and classify various event types.

Thesis (Ph. D.)--University of Washington, 2008. / Vita. Includes bibliographical references (p. 105-112).

Fundamental movement skills (FMSs) are considered to be one of the essential phases of motor skill development. The proper development of FMSs allows children to participate in more advanced forms of movements and sports. To be able to perform an FMS correctly, children need to learn the right way of performing it. By making use of technology, a system can be developed that can help facilitate the learning of FMSs. The objective of the research was to propose an effective natural user interface (NUI) architecture for detecting FMSs using the Kinect. In order to achieve the stated objective, an investigation into FMSs and the challenges faced when teaching them was presented. An investigation into NUIs was also presented including the merits of the Kinect as the most appropriate device to be used to facilitate the detection of an FMS. An NUI architecture was proposed that uses the Kinect to facilitate the detection of an FMS. A framework was implemented from the design of the architecture. The successful implementation of the framework provides evidence that the design of the proposed architecture is feasible. An instance of the framework incorporating the jump FMS was used as a case study in the development of a prototype that detects the correct and incorrect performance of a jump. The evaluation of the prototype proved the following: - The developed prototype was effective in detecting the correct and incorrect performance of the jump FMS; and - The implemented framework was robust for the incorporation of an FMS. The successful implementation of the prototype shows that an effective NUI architecture using the Kinect can be used to facilitate the detection of FMSs. The proposed architecture provides a structured way of developing a system using the Kinect to facilitate the detection of FMSs. This allows developers to add future FMSs to the system. This dissertation therefore makes the following contributions: - An experimental design to evaluate the effectiveness of a prototype that detects FMSs - A robust framework that incorporates FMSs; and - An effective NUI architecture to facilitate the detection of fundamental movement skills using the Kinect.

Building smart home environments which automatically or semi-automatically assist and comfort occupants is an important topic in the pervasive computing field, especially with the coming of cheap, easy-to-install sensors. This has given rise to the indispensable need for human activity recognition from ubiquitous sensors whose purpose is to observe and understand what occupants are trying to do from sensory data. The main approach to the problem of human activity recognition is a probabilistic one so as to handle the complication of uncertainty, the overlapping of human behaviours and environmental noise. This thesis develops a probabilistic model as a framework for human activity recognition using multiple multi-modal sensors in complex pervasive environments. The probabilistic model to be developed is adapted and based on the abstract hidden Markov model (AHMM) with one layer to fuse multiple sensors. The concept of factored state representation is employed in the model to parsimoniously represent the state transitions for reducing the number of required parameters. The exact method is used in learning the model’s parameters and performing inference. To be able to incorporate a large number of sensors, several more parsimonious representations including the mixtures of smaller multinomials and sigmoid functions are investigated to model the state transitions, resulting in a reduction of the number of parameters and time required for training. / We examine the approximate variational method to significantly reduce the time required for training the model instead of using the exact method. A system of fixed point equations is derived to iteratively update the free variational parameters. We also present the factored model in the case where all variables are continuous with the use of the conditional Gaussian distribution to model state transitions. The variational method is still employed in this case to speed up the model’s training process. The developed model is implemented and applied in recognizing daily activity in our smart home and the Nokia lab from multiple sensors. The experimental results show that the model is appropriate for fusing multiple sensors in activity recognition with a reasonable recognition performance.

Thesis (Ph. D.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 129-138). Also available on the World Wide Web.

The elderly living in smart homes can have their daily movement recorded and analyzed. Given the fact that different elders can have their own living habits, a methodology that can automatically identify their daily activities and discover their daily routines will be useful for better elderly care and support. In this thesis research, we focus on developing data mining algorithms for automatic detection of behavioral patterns from the trajectory data of an individual for activity identification, daily routine discovery, and activity prediction. The key challenges for the human activity analysis include the need to consider longer-range dependency of the sensor triggering events for activity modeling and to capture the spatio-temporal variations of the behavioral patterns exhibited by human. We propose to represent the trajectory data using a behavior-aware flow graph which is a probabilistic finite state automaton with its nodes and edges attributed with some local behavior-aware features. Subflows can then be extracted from the flow graph using the kernel k-means as the underlying behavioral patterns for activity identification. Given the identified activities, we propose a novel nominal matrix factorization method under a Bayesian framework with Lasso to extract highly interpretable daily routines. To better take care of the variations of activity durations within each daily routine, we further extend the Bayesian framework with a Markov jump process as the prior to incorporate the shift-invariant property into the model. For empirical evaluation, the proposed methodologies have been compared with a number of existing activity identification and daily routine discovery methods based on both synthetic and publicly available real smart home data sets with promising results obtained. In the thesis, we also illustrate how the proposed unsupervised methodology could be used to support exploratory behavior analysis for elderly care.