Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Players of Th-cell differentiation and immune dysregulation – focus on GIMAP family genes

The balance of T helper (Th) cell differentiation is the fundamental process that ensures that the immune system functions correctly and effectively. The differentiation is a fine tuned event, the outcome of which is driven by activation of the T-cell in response to recognition of the specific antigen presented. The co-stimulatory signals from the surrounding cytokine milieu help to determine the outcome. An impairment in the differentiation processes may lead to an imbalance in immune responses and lead to immune-mediated pathologies. An over-representation of Th1 type cytokine producing cells leads to tissue-specific inflammation and autoimmunity, and excessive Th2 response is causative for atopy, asthma and allergy. The major factors of Th-cell differentiation and in the related disease mechanisms have been extensively studied, but the fine tuning of these processes by the other factors cannot be discarded. In the work presented in this thesis, the association of T-cell receptor costimulatory molecules CTLA4 and ICOS with autoimmune diabetes were studied. The underlying aspect of the study was to explore the polymorphism in these genes with the different disease rates observed in two geographically close populations. The main focus of this thesis was set on a GTPase of the immunity associated protein (GIMAP) family of small GTPases. GIMAP genes and proteins are differentially regulated during human Th-cell differentiation and have been linked to immune-mediated disorders. GIMAP4 is believed to contribute to the immunological balance via its role in T-cell survival. To elucidate the function of GIMAP4 and GIMAP5 and their role in human immunity, a study combining genetic association in different immunological diseases and complementing functional analyses was conducted. The study revealed interesting connections with the high susceptibility risk genes. In addition, the role of GIMAP4 during Th1-cell differentiation was investigated. A novel function of GIMAP4 in relation to cytokine secretion was discovered. Further assessment of GIMAP4 and GIMAP5 effect for the transcriptomic profile of differentiating Th1-cells revealed new insights for GIMAP4 and GIMAP5 function.