Advanced Analysis Methods for Large-Scale Structured Data

In the era of ’big data’, advanced storage and computing technologies allow people to build and process large-scale datasets, which promote the development of many fields such as speech recognition, natural language processing and computer vision. Traditional approaches can not handle the heterogeneity and complexity of some novel data structures. In this dissertation, we want to explore how to combine different tools to develop new methodologies in analyzing certain kinds of structured data, motivated by real-world problems. Multi-group design, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI), has been undertaken by recruiting subjects based on their multi-class primary disease status, while some extensive secondary outcomes are also collected. Analysis by standard approaches is usually distorted because of the unequal sampling rates of different classes. In the first part of the dissertation, we develop a general regression framework for the analysis of secondary phenotypes collected in multi-group association studies. Our regression framework is built on a conditional model for the secondary outcome given the multi-group status and covariates and its relationship with the population regression of interest of the secondary outcome given the covariates. Then, we develop generalized estimation equations to estimate the parameters of interest. We use simulations and a large-scale imaging genetic data analysis of the ADNI data to evaluate the effect of the multi-group sampling scheme on standard genome-wide association analyses based on linear regression methods, while comparing it with our statistical methods that appropriately adjust for the multi-group sampling scheme. In the past few decades, network data has been increasingly collected and studied in diverse areas, including neuroimaging, social networks and knowledge graphs. In the second part of the dissertation, we investigate the graph-based semi-supervised learning problem with nonignorable nonresponses. We propose a Graph-based joint model with Nonignorable Missingness (GNM) and develop an imputation and inverse probability weighting estimation approach. We further use graph neural networks (GNN) to model nonlinear link functions and then use a gradient descent (GD) algorithm to estimate all the parameters of GNM. We propose a novel identifiability for the GNM model with neural network structures, and validate its predictive performance in both simulations and real data analysis through comparing with models ignoring or misspecifying the missingness mechanism. Our method can achieve up to 7.5% improvement than the baseline model for the document classification task on the Cora dataset. Predictions of Origin-Destination (OD) flow data is an important instrument in transportation studies. However, most existing methods ignore the network structure of OD flow data. In the last part of the dissertation, we propose a spatial-temporal origin-destination (STOD) model, with a novel CNN filter to learn the spatial features from the perspective of graphs and an attention mechanism to capture the long term periodicity. Experiments on a real customer request dataset with available OD information from a ride-sharing platform demonstrates the advantage of STOD in achieving a more accurate and stable prediction performance compared to some state-of-the-art methods.Doctor of Philosoph

Development of Machine Learning Models to Detect Dynamic Disturbances in Human Gait

Machine learning has transformed the medical field by automating tasks and achieving objectives that are closer to human cognitive capabilities. Gait is a series of intricate interactions for humans, and identifying impaired gait is critical for effective decision-making in clinical practice. However, analysing the complex biological system that governs gait can be challenging. This research proposes a novel Criticality Analysis (CA) methodology to extract dynamic interactions in human gait and represent multivariate data in a nonlinear space. The proposed methodology characterises each data sample with a unique orbit, resulting from perturbations of a critical system composed of nonlinear controlled oscillators. The scale-free network of orbits is a quantitative measure of non-scale-free interacting sets of patterns, which reveal organised features of the structure of dynamic properties interconnected with human gait. This thesis focuses on implementing robust machine learning algorithms for effective detection and classification of complex dynamic patterns in human gait. The CA method maps gait features into a nonlinear representation, which is then used for training and testing categorisation algorithms. The proposed models utilise the Kernel property of the Support Vector Machines (SVM) classifier to identify high-order interactions between multiple gait data variables that may be challenging for traditional statistics. The algorithm was applied to three real datasets, and the SVM models designed using the CA method achieved an accuracy of 88.27% on average, compared to the K-Nearest Neighbours (KNN) approach's accuracy of 67.7%. The proposed SVM models use the receiver operating characteristics (ROC) and the area under the ROC metrics to evaluate their overall performance. The results of this research suggest that the proposed SVM models, with the support of the CA method, can perform as a robust and reliable classification tool for detecting dynamic disturbances of biological data patterns. This provides tremendous opportunities for clinical diagnosis and rehabilitation

State of the Art of Audio- and Video-Based Solutions for AAL

It is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters. Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals. Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely lifelogging and self-monitoring, remote monitoring of vital signs, emotional state recognition, food intake monitoring, activity and behaviour recognition, activity and personal assistance, gesture recognition, fall detection and prevention, mobility assessment and frailty recognition, and cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed