Visualizing and Predicting the Effects of Rheumatoid Arthritis on Hands

This dissertation was inspired by difficult decisions patients of chronic diseases have to make about about treatment options in light of uncertainty. We look at rheumatoid arthritis (RA), a chronic, autoimmune disease that primarily affects the synovial joints of the hands and causes pain and deformities. In this work, we focus on several parts of a computer-based decision tool that patients can interact with using gestures, ask questions about the disease, and visualize possible futures. We propose a hand gesture based interaction method that is easily setup in a doctor\u27s office and can be trained using a custom set of gestures that are least painful. Our system is versatile and can be used for operations like simple selections to navigating a 3D world. We propose a point distribution model (PDM) that is capable of modeling hand deformities that occur due to RA and a generalized fitting method for use on radiographs of hands. Using our shape model, we show novel visualization of disease progression. Using expertly staged radiographs, we propose a novel distance metric learning and embedding technique that can be used to automatically stage an unlabeled radiograph. Given a large set of expertly labeled radiographs, our data-driven approach can be used to extract different modes of deformation specific to a disease

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

Self-adaptive structure semi-supervised methods for streamed emblematic gestures

Although many researchers try to improve the level of machine intelligence, there is still a long way to achieve intelligence similar to what humans have. Scientists and engineers are continuously trying to increase the level of smartness of the modern technology, i.e. smartphones and robotics. Humans communicate with each other by using the voice and gestures. Hence, gestures are essential to transfer the information to the partner. To reach a higher level of intelligence, the machine should learn from and react to the human gestures, which mean learning from continuously streamed gestures. This task faces serious challenges since processing streamed data suffers from different problems. Besides the stream data being unlabelled, the stream is long. Furthermore, “concept-drift” and “concept evolution” are the main problems of them. The data of the data streams have several other problems that are worth to be mentioned here, e.g. they are: dynamically changed, presented only once, arrived at high speed, and non-linearly distributed. In addition to the general problems of the data streams, gestures have additional problems. For example, different techniques are required to handle the varieties of gesture types. The available methods solve some of these problems individually, while we present a technique to solve these problems altogether. Unlabelled data may have additional information that describes the labelled data more precisely. Hence, semi-supervised learning is used to handle the labelled and unlabelled data. However, the data size increases continuously, which makes training classifiers so hard. Hence, we integrate the incremental learning technique with semi-supervised learning, which enables the model to update itself on new data without the need of the old data. Additionally, we integrate the incremental class learning within the semi-supervised learning, since there is a high possibility of incoming new concepts in the streamed gestures. Moreover, the system should be able to distinguish among different concepts and also should be able to identify random movements. Hence, we integrate the novelty detection to distinguish between the gestures that belong to the known concepts and those that belong to unknown concepts. The extreme value theory is used for this purpose, which overrides the need of additional labelled data to set the novelty threshold and has several other supportive features. Clustering algorithms are used to distinguish among different new concepts and also to identify random movements. Furthermore, the system should be able to update itself on only the trusty assignments, since updating the classifier on wrongly assigned gesture affects the performance of the system. Hence, we propose confidence measures for the assigned labels. We propose six types of semi-supervised algorithms that depend on different techniques to handle different types of gestures. The proposed classifiers are based on the Parzen window classifier, support vector machine classifier, neural network (extreme learning machine), Polynomial classifier, Mahalanobis classifier, and nearest class mean classifier. All of these classifiers are provided with the mentioned features. Additionally, we submit a wrapper method that uses one of the proposed classifiers or ensemble of them to autonomously issue new labels to the new concepts and update the classifiers on the newly incoming information depending on whether they belong to the known classes or new classes. It can recognise the different novel concepts and also identify random movements. To evaluate the system we acquired gesture data with nine different gesture classes. Each of them represents a different order to the machine e.g. come, go, etc. The data are collected using the Microsoft Kinect sensor. The acquired data contain 2878 gestures achieved by ten volunteers. Different sets of features are computed and used in the evaluation of the system. Additionally, we used real data, synthetic data and public data as support to the evaluation process. All the features, incremental learning, incremental class learning, and novelty detection are evaluated individually. The outputs of the classifiers are compared with the original classifier or with the benchmark classifiers. The results show high performances of the proposed algorithms

Final Report to NSF of the Standards for Facial Animation Workshop

The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed

Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods

A PROBABILISTIC APPROACH TO THE CONSTRUCTION OF A MULTIMODAL AFFECT SPACE

Understanding affective signals from others is crucial for both human-human and human-agent interaction. The automatic analysis of emotion is by and large addressed as a pattern recognition problem which grounds in early psychological theories of emotion. Suitable features are first extracted and then used as input to classification (discrete emotion recognition) or regression (continuous affect detection). In this thesis, differently from many computational models in the literature, we draw on a simulationist approach to the analysis of facially displayed emotions - e.g., in the course of a face-to-face interaction between an expresser and an observer. At the heart of such perspective lies the enactment of the perceived emotion in the observer. We propose a probabilistic framework based on a deep latent representation of a continuous affect space, which can be exploited for both the estimation and the enactment of affective states in a multimodal space. Namely, we consider the observed facial expression together with physiological activations driven by internal autonomic activity. The rationale behind the approach lies in the large body of evidence from affective neuroscience showing that when we observe emotional facial expressions, we react with congruent facial mimicry. Further, in more complex situations, affect understanding is likely to rely on a comprehensive representation grounding the reconstruction of the state of the body associated with the displayed emotion. We show that our approach can address such problems in a unified and principled perspective, thus avoiding ad hoc heuristics while minimising learning efforts. Moreover, our model improves the inferred belief through the adoption of an inner loop of measurements and predictions within the central affect state-space, that realise the dynamics of the affect enactment. Results so far achieved have been obtained by adopting two publicly available multimodal corpora

Cluster-analytic classification of facial expressions using infrared measurements of facial thermal features

In previous research, scientists were able to use transient facial thermal features extracted from Thermal Infra-Red Images (TIRIs) for making binary distinction between the affective states. For example, thermal asymmetries localised in facial TIRIs have been used to distinguish anxiety and deceit. Since affective human-computer interaction would require machines to distinguish between the subtle facial expressions of affective states, computers’ able to make such binary distinctions would not suffice a robust human-computer interaction. This work, for the first time, uses affective-state-specific transient facial thermal features extracted from TIRIs to recognise a much wider range of facial expressions under a much wider range of conditions. Using infrared thermal imaging within the 8-14 μm, a database of 324 discrete, time-sequential, visible-spectrum and thermal facial images was acquired, representing different facial expressions from 23 participants in different situations. A facial thermal feature extraction and pattern classification approach was developed, refined and tested on various Gaussian mixture models constructed using the image database. Attempts were made to classify: neutral and pretended happy and sad faces; multiple positive and negative facial expressions; six (pretended) basic facial expressions; partially covered or occluded faces; and faces with evoked happiness, sadness, disgust and anger. The cluster-analytic classification in this work began by segmentation and detection of thermal faces in the acquired TIRIs. The affective-state-specific temperature distributions on the facial skin surface were realised through the pixel grey-level analysis. Examining the affectivestate- specific temperature variations within the selected regions of interest in the TIRIs led to the discovery of some significant Facial Thermal Feature Points (FTFPs) along the major facial muscles. Following a multivariate analysis of the Thermal Intensity values (TIVs) measured at the FTFPs, the TIRIs were represented along the Principal Components (PCs) of a covariance matrix. The resulting PCs were ranked in the order of their effectiveness in the between-cluster separation. Only the most effective PCs were retained to construct an optimised eigenspace. A supervised learning algorithm was invoked for linear subdivision of the optimised eigenspace. The statistical significance levels of the classification results were estimated for validating the discriminant functions. The main contribution of this research has been to show that: the infrared imaging of facial thermal features within the 8-14 μm bandwidth may be used to observe affective-state-specific thermal variations on the face; the pixel-grey level analysis of TIRIs can help localise FTFPs along the major facial muscles of the face; cluster-analytic classification of transient thermal features may help distinguish between the facial expressions of affective states in an optimized eigenspace of input thermal feature vectors. The Gaussian mixture model with one cluster per affect worked better for some facial expressions than others. This made the influence of the Gaussian mixture model structure on the accuracy of the classification results obvious. However, the linear discrimination and confusion patterns observed in this work were consistent with the ones reported in several earlier studies. This investigation also unveiled some important dimensions of the future research on use of facial thermal features in affective human-computer interaction.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion