Detection and prediction problems with applications in personalized health care

The United States health-care system is considered to be unsustainable due to its unbearably high cost. Many of the resources are spent on acute conditions rather than aiming at preventing them. Preventive medicine methods, therefore, are viewed as a potential remedy since they can help reduce the occurrence of acute health episodes. The work in this dissertation tackles two distinct problems related to the prevention of acute disease. Specifically, we consider: (1) early detection of incorrect or abnormal postures of the human body and (2) the prediction of hospitalization due to heart related diseases. The solution to the former problem could be used to prevent people from unexpected injuries or alert caregivers in the event of a fall. The latter study could possibly help improve health outcomes and save considerable costs due to preventable hospitalizations. For body posture detection, we place wireless sensor nodes on different parts of the human body and use the pairwise measurements of signal strength corresponding to all sensor transmitter/receiver pairs to estimate body posture. We develop a composite hypothesis testing approach which uses a Generalized Likelihood Test (GLT) as the decision rule. The GLT distinguishes between a set of probability density function (pdf) families constructed using a custom pdf interpolation technique. The GLT is compared with the simple Likelihood Test and Multiple Support Vector Machines. The measurements from the wireless sensor nodes are highly variable and these methods have different degrees of adaptability to this variability. Besides, these methods also handle multiple observations differently. Our analysis and experimental results suggest that GLT is more accurate and suitable for the problem. For hospitalization prediction, our objective is to explore the possibility of effectively predicting heart-related hospitalizations based on the available medical history of the patients. We extensively explored the ways of extracting information from patients' Electronic Health Records (EHRs) and organizing the information in a uniform way across all patients. We applied various machine learning algorithms including Support Vector Machines, AdaBoost with Trees, and Logistic Regression adapted to the problem at hand. We also developed a new classifier based on a variant of the likelihood ratio test. The new classifier has a classification performance competitive with those more complex alternatives, but has the additional advantage of producing results that are more interpretable. Following this direction of increasing interpretability, which is important in the medical setting, we designed a new method that discovers hidden clusters and, at the same time, makes decisions. This new method introduces an alternating clustering and classification approach with guaranteed convergence and explicit performance bounds. Experimental results with actual EHRs from the Boston Medical Center demonstrate prediction rate of 82% under 30% false alarm rate, which could lead to considerable savings when used in practice

Conception, réalisation et étude d'un esssaim de robots autonome protégeant un groupe de personnes munies de semelle intelligente

Ce projet porte sur le problème de la protection d'un convoi de personnes munies de semelle intelligente par un essaim de robots. De nos jours, il y a beaucoup de flux de population qui nécessite d'être protégés dans des zones à risques (familles syriennes, irakiennes...). L'essaim de robots est une solution qui permettrait de les protéger sans exposer d'autres personnes au danger. Celui-ci devra suivre le groupe de personnes et éviter toutes les perturbations externes dans le but de réduire les erreurs de positionnement des robots. La semelle intelligente portée par les gens, élaborée à partir de plusieurs capteurs, donnera les informations sur leur orientation et leur vitesse de marche. Les robots pourront être munis de capteurs de distance et de centrale inertielle afin de détecter les obstacles environnant et de se déplacer autour du groupe de personnes. Un drone fournira également des informations visuelles sur l'environnement autour des personnes. Le système est entièrement basé sur un réseau de modules WiFi (WBAN : Wireless Body Area Network) qui communiqueront toutes les données recueillies. Un serveur se chargera de collecter toutes les données reçues par les robots et les semelles. Celles-ci seront traitées par différents algorithmes qui dirigeront les robots de manière autonome autour des personnes