Multimodal Analytics for Healthcare

The ailing healthcare system demands effective autonomous solutions to improve service and provide individualize care, while reducing the burden on the scarce healthcare workforce. Most of these solutions require a multidisciplinary approach that combines healthcare with computational abilities. The work presented in this thesis introduces a multimodal multiview network along with methods and solutions that leverage inexpensive visual sensors and computers to monitor healthcare. One of the most prominent outcomes of this work includes enabling the medical analysis of ICU conditions such as sleep disorders, decubitus ulcerations, and hospital acquired infections, which are preventable and negatively affect patients' health population. The problems tackled include patient pose classification, pose motion analysis and summarization, role representation and identification, and activity and event logging in natural hospital settings. These problems are addressed via a non-intrusive non-disruptive multimodal multiview sensor network (Medical Internet-of-Things). The multimodal data is combined with coupled-optimization to estimate source weights and accurately classify patient poses. Pose patterns such as pose transitions are represented using deep convolutional features and pose duration is modelled via segments. The proposed techniques serve to differentiate between poses and pseudo-poses (transitory poses) and create effective motion summaries. The role representation is tackled using novel appearance and semantic interaction maps to assign generic labels to individuals (doctor, nurse, visitor, etc) without using identifiable information (e.g., facetracking or badges), which is prohibited in healthcare applications. Finally, activity and event analysis is tackled using a new contextual aspect frames where aspect bases and weights are learned and then used to reconstruct activities. The objective of this thesis is to enable the development, evaluation, and optimization of individualized therapies, standards-of-care, room infrastructural designs, and clinical workflows and procedures

Latent Structured Models for Video Understanding

The proliferation of videos in recent years has spurred a surge of interest in developing efficient techniques for automatic video interpretation. The thesis improves the understanding of videos by building structured models that use latent information to detect and recognize instances of actions or abnormalities in videos. The thesis also proposes efficient algorithms for inference in and learning of the proposed latent structured models that are appropriate for learning with weak supervision. An important class of latent variable models is the multiple instance learning where the training labels are provided only for bags of instances, but not for instances themselves. As inference of latent instance labels is performed jointly with training of a classifier on the same data, multiple-instance learning is very susceptible to overfitting. To increase the robustness of popular methods for multiple instance learning, the thesis introduces a novel concept of superbags (ensemble of bags of bags) that allows for decoupling of classifier training and latent label inference steps. In the thesis, a novel latent structured representation is proposed to discover instances of action classes in videos and jointly train an action classifier on them. Action class instances typically occupy only a part of the whole video that is not annotated in weakly labeled training videos. Therefore, multiple instance learning is proposed to find these latent action instances in training videos and jointly train the action classifier. The thesis proposes a sequential method to multiple instance learning to increase the robustness of the training. For the interpretation of crowded scenes, it is important to detect all irregular objects or actions in a video. However, the abnormality detection is hindered by the fact that the training set does not contain any abnormal sample, thus it is necessary to find abnormalities in a test video without actually knowing what they are. To address this problem, the thesis proposes a probabilistic graphical model for video parsing that searches for latent object hypotheses to jointly explain all the foreground pixels, which are, at the same time, well matched to the normal training samples. By inferring all latent normal hypotheses in a video, the model indirectly finds abnormalities as those hypotheses that are not supported by normal samples but still need to be used to explain the foreground. Video parsing is applied sequentially on individual video frames, where hypotheses are jointly inferred by a local search in a graphical model. The thesis then proposes a spatio-temporal extension of the video parsing, where an efficient inference method based on convex optimization is developed to find abnormal/normal spatio-temporal hypotheses in the video

Path integrals, particular kinds, and strange things

This paper describes a path integral formulation of the free energy principle. The ensuing account expresses the paths or trajectories that a particle takes as it evolves over time. The main results are a method or principle of least action that can be used to emulate the behaviour of particles in open exchange with their external milieu. Particles are defined by a particular partition, in which internal states are individuated from external states by active and sensory blanket states. The variational principle at hand allows one to interpret internal dynamics - of certain kinds of particles - as inferring external states that are hidden behind blanket states. We consider different kinds of particles, and to what extent they can be imbued with an elementary form of inference or sentience. Specifically, we consider the distinction between dissipative and conservative particles, inert and active particles and, finally, ordinary and strange particles. Strange particles (look as if they) infer their own actions, endowing them with apparent autonomy or agency. In short - of the kinds of particles afforded by a particular partition - strange kinds may be apt for describing sentient behaviour.Comment: 31 pages (excluding references), 6 figure

Scene Segmentation and Object Classification for Place Recognition

This dissertation tries to solve the place recognition and loop closing problem in a way similar to human visual system. First, a novel image segmentation algorithm is developed. The image segmentation algorithm is based on a Perceptual Organization model, which allows the image segmentation algorithm to ‘perceive’ the special structural relations among the constituent parts of an unknown object and hence to group them together without object-specific knowledge. Then a new object recognition method is developed. Based on the fairly accurate segmentations generated by the image segmentation algorithm, an informative object description that includes not only the appearance (colors and textures), but also the parts layout and shape information is built. Then a novel feature selection algorithm is developed. The feature selection method can select a subset of features that best describes the characteristics of an object class. Classifiers trained with the selected features can classify objects with high accuracy. In next step, a subset of the salient objects in a scene is selected as landmark objects to label the place. The landmark objects are highly distinctive and widely visible. Each landmark object is represented by a list of SIFT descriptors extracted from the object surface. This object representation allows us to reliably recognize an object under certain viewpoint changes. To achieve efficient scene-matching, an indexing structure is developed. Both texture feature and color feature of objects are used as indexing features. The texture feature and the color feature are viewpoint-invariant and hence can be used to effectively find the candidate objects with similar surface characteristics to a query object. Experimental results show that the object-based place recognition and loop detection method can efficiently recognize a place in a large complex outdoor environment

Efficient Belief Propagation for Perception and Manipulation in Clutter

Autonomous service robots are required to perform tasks in common human indoor environments. To achieve goals associated with these tasks, the robot should continually perceive, reason its environment, and plan to manipulate objects, which we term as goal-directed manipulation. Perception remains the most challenging aspect of all stages, as common indoor environments typically pose problems in recognizing objects under inherent occlusions with physical interactions among themselves. Despite recent progress in the field of robot perception, accommodating perceptual uncertainty due to partial observations remains challenging and needs to be addressed to achieve the desired autonomy. In this dissertation, we address the problem of perception under uncertainty for robot manipulation in cluttered environments using generative inference methods. Specifically, we aim to enable robots to perceive partially observable environments by maintaining an approximate probability distribution as a belief over possible scene hypotheses. This belief representation captures uncertainty resulting from inter-object occlusions and physical interactions, which are inherently present in clutterred indoor environments. The research efforts presented in this thesis are towards developing appropriate state representations and inference techniques to generate and maintain such belief over contextually plausible scene states. We focus on providing the following features to generative inference while addressing the challenges due to occlusions: 1) generating and maintaining plausible scene hypotheses, 2) reducing the inference search space that typically grows exponentially with respect to the number of objects in a scene, 3) preserving scene hypotheses over continual observations. To generate and maintain plausible scene hypotheses, we propose physics informed scene estimation methods that combine a Newtonian physics engine within a particle based generative inference framework. The proposed variants of our method with and without a Monte Carlo step showed promising results on generating and maintaining plausible hypotheses under complete occlusions. We show that estimating such scenarios would not be possible by the commonly adopted 3D registration methods without the notion of a physical context that our method provides. To scale up the context informed inference to accommodate a larger number of objects, we describe a factorization of scene state into object and object-parts to perform collaborative particle-based inference. This resulted in the Pull Message Passing for Nonparametric Belief Propagation (PMPNBP) algorithm that caters to the demands of the high-dimensional multimodal nature of cluttered scenes while being computationally tractable. We demonstrate that PMPNBP is orders of magnitude faster than the state-of-the-art Nonparametric Belief Propagation method. Additionally, we show that PMPNBP successfully estimates poses of articulated objects under various simulated occlusion scenarios. To extend our PMPNBP algorithm for tracking object states over continuous observations, we explore ways to propose and preserve hypotheses effectively over time. This resulted in an augmentation-selection method, where hypotheses are drawn from various proposals followed by the selection of a subset using PMPNBP that explained the current state of the objects. We discuss and analyze our augmentation-selection method with its counterparts in belief propagation literature. Furthermore, we develop an inference pipeline for pose estimation and tracking of articulated objects in clutter. In this pipeline, the message passing module with the augmentation-selection method is informed by segmentation heatmaps from a trained neural network. In our experiments, we show that our proposed pipeline can effectively maintain belief and track articulated objects over a sequence of observations under occlusion.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163159/1/kdesingh_1.pd