The advancement and marked increase in the use of computing devices in health care for large scale and personal medical use has transformed the field of medicine and health care into a data rich domain. This surge in the availability of data has allowed domain experts to investigate, study and discover inherent patterns in diseases from new perspectives and in turn, further the field of medicine. Storage and analysis of this data in real time aids in enhancing the response time and efficiency of doctors and health care specialists. However, due to the time critical nature of most life- threatening diseases, there is a growing need to make informed decisions prior to the occurrence of any fatal outcome. Alongside time sensitivity, analyzing data specific to diseases and their effects on an individual basis leads to more efficient prognosis and rapid deployment of cures. The primary challenge in addressing both of these issues arises from the time varying and time sensitive nature of the data being studied and in the ability to successfully predict anomalous events using only observed data.This dissertation introduces adaptive machine learning algorithms that aid in the prediction of anomalous situations arising due to abnormalities present in patients diagnosed with certain types of diseases. Emphasis is given to the adaptation and development of algorithms based on an individual basis to further the accuracy of all predictions made. The main objectives are to learn the underlying representation of the data using empirical methods and enhance it using domain knowledge. The learned model is then utilized as a guide for statistical machine learning methods to predict the occurrence of anomalous events in the near future. Further enhancement of the learned model is achieved by means of tuning the objective function of the algorithm to incorporate domain knowledge. Along with anomaly forecasting using multi-modal data, this dissertation also investigates the use of univariate time series data towards the prediction of onset of diseases using Bayesian nonparametrics.

In recent years, brain signals have gained attention as a potential trait for biometric-based security systems and laboratory systems have been designed. A real-world brain-based security system requires to be usable, accurate, and robust. While there have been developments in these aspects, there are still challenges to be met. With regard to usability, users need to provide lengthy amount of data compared to other traits such as fingerprint and face to get authenticated. Furthermore, in the majority of works, medical sensors are used which are more accurate compared to commercial ones but have a tedious setup process and are not mobile. Performance wise, the current state-of-art can provide acceptable accuracy on a small pool of users data collected in few sessions close to each other but still falls behind on a large pool of subjects over a longer time period. Finally, a brain security system should be robust against presentation attacks to prevent adversaries from gaining access to the system. This dissertation proposes E-BIAS (EEG-based Identification and Authentication System), a brain-mobile security system that makes contributions in three directions. First, it provides high performance on signals with shorter lengths collected by commercial sensors and processed with lightweight models to meet the computation/energy capacity of mobile devices. Second, to evaluate the system's robustness a novel presentation attack was designed which challenged the literature's presumption of intrinsic liveness property for brain signals. Third, to bridge the gap, I formulated and studied the brain liveness problem and proposed two solution approaches (model-aware & model agnostic) to ensure liveness and enhance robustness against presentation attacks. Under each of the two solution approaches, several methods were suggested and evaluated against both synthetic and manipulative classes of attacks (a total of 43 different attack vectors). Methods in both model-aware and model-agnostic approaches were successful in achieving an error rate of zero (0%). More importantly, such error rates were reached in face of unseen attacks which provides evidence of the generalization potentials of the proposed solution approaches and methods. I suggested an adversarial workflow to facilitate attack and defense cycles to allow for enhanced generalization capacity for domains in which the decision-making process is non-deterministic such as cyber-physical systems (e.g. biometric/medical monitoring, autonomous machines, etc.). I utilized this workflow for the brain liveness problem and was able to iteratively improve the performance of both the designed attacks and the proposed liveness detection methods.

The advent of commercial inexpensive sensors and the advances in information and communication technology (ICT) have brought forth the era of pervasive Quantified-Self. Automatic diet monitoring is one of the most important aspects for Quantified-Self because it is vital for ensuring the well-being of patients suffering from chronic diseases as well as for providing a low cost means for maintaining the health for everyone else. Automatic dietary monitoring consists of: a) Determining the type and amount of food intake, and b) Monitoring eating behavior, i.e., time, frequency, and speed of eating. Although there are some existing techniques towards these ends, they suffer from issues of low accuracy and low adherence. To overcome these issues, multiple sensors were utilized because the availability of affordable sensors that can capture the different aspect information has the potential for increasing the available knowledge for Quantified-Self. For a), I envision an intelligent dietary monitoring system that automatically identifies food items by using the knowledge obtained from visible spectrum camera and infrared spectrum camera. This system is able to outperform the state-of-the-art systems for cooked food recognition by 25% while also minimizing user intervention. For b), I propose a novel methodology, IDEA that performs accurate eating action identification within eating episodes with an average F1-score of 0.92. This is an improvement of 0.11 for precision and 0.15 for recall for the worst-case users as compared to the state-of-the-art. IDEA uses only a single wrist-band which includes four sensors and provides feedback on eating speed every 2 minutes without obtaining any manual input from the user.

Mobile apps have improved human lifestyle in various aspects ranging from instant messaging to tele-health. In the current app development paradigm, apps are being developed individually and agnostic of each other. The goal of this thesis is to allow a new world where multiple apps communicate with each other to achieve synergistic benefits. To enable integration between apps, manual communication between developers is needed, which can be problematic on many levels. In order to promote app integration, a systematic approach towards data sharing between multiple apps is essential. However, current approaches to app integration require large code modifications to reap the benefits of shared data such as requiring developers to provide APIs or use large, invasive middlewares. In this thesis, a data sharing framework was developed providing a non-invasive interface between mobile apps for data sharing and integration. A separate app acts as a registry to allow apps to register database tables to be shared and query this information. Two health monitoring apps were developed to evaluate the sharing framework and different methods of data integration between apps to promote synergistic feedback. The health monitoring apps have shown non-invasive solutions can provide data sharing functionality without large code modifications and manual communication between developers.

Critical infrastructures in healthcare, power systems, and web services, incorporate cyber-physical systems (CPSes), where the software controlled computing systems interact with the physical environment through actuation and monitoring. Ensuring software safety in CPSes, to avoid hazards to property and human life as a result of un-controlled interactions, is essential and challenging. The principal hurdle in this regard is the characterization of the context driven interactions between software and the physical environment (cyber-physical interactions), which introduce multi-dimensional dynamics in space and time, complex non-linearities, and non-trivial aggregation of interaction in case of networked operations. Traditionally, CPS software is tested for safety either through experimental trials, which can be expensive, incomprehensive, and hazardous, or through static analysis of code, which ignore the cyber-physical interactions. This thesis considers model based engineering, a paradigm widely used in different disciplines of engineering, for safety verification of CPS software and contributes to three fundamental phases: a) modeling, building abstractions or models that characterize cyberphysical interactions in a mathematical framework, b) analysis, reasoning about safety based on properties of the model, and c) synthesis, implementing models on standard testbeds for performing preliminary experimental trials. In this regard, CPS modeling techniques are proposed that can accurately capture the context driven spatio-temporal aggregate cyber-physical interactions. Different levels of abstractions are considered, which result in high level architectural models, or more detailed formal behavioral models of CPSes. The outcomes include, a well defined architectural specification framework called CPS-DAS and a novel spatio-temporal formal model called Spatio-Temporal Hybrid Automata (STHA) for CPSes. Model analysis techniques are proposed for the CPS models, which can simulate the effects of dynamic context changes on non-linear spatio-temporal cyberphysical interactions, and characterize aggregate effects. The outcomes include tractable algorithms for simulation analysis and for theoretically proving safety properties of CPS software. Lastly a software synthesis technique is proposed that can automatically convert high level architectural models of CPSes in the healthcare domain into implementations in high level programming languages. The outcome is a tool called Health-Dev that can synthesize software implementations of CPS models in healthcare for experimental verification of safety properties.