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.

Designing features for supporting social interactions in online communities

This Master's thesis studies the development of interaction and socialization in online communities. A large number of online communities fade away even before they really get started. In many occasions the reason is that the community does not give anything new, or even if they do, the delivery does not satisfy the users. In this thesis guidelines were developed to help to see important things, which might be forgotten when developing an online community. The thesis goes through the characteristic of an online community and human behaviour related to them and also compares behaviour in the Internet and real life. In addition, usability is an important part of the online communities and thus it is also covered in this thesis. As a result of this thesis an 8-step guideline was developed to ease the design of an online community. Guidelines were also applied to two real life cases which are described as one part of this work.

Thermoplastic bioactive composite – with special reference to dissolution behaviour and tissue response

Bioactive glasses are surface-active ceramic materials which support and accelerate bone growth in the body. During the healing of a bone fracture or a large bone defect, fixation is often needed. The aim of this thesis was to determine the dissolution behaviour and biocompatibility of a composite consisting of poly(ε-caprolactone-co-DL-lactide) and bioactive glass (S53P4). In addition the applicability as an injectable material straight to a bone defect was assessed. In in vitro tests the dissolution behaviour of plain copolymer and composites containing bioactive glass granules was evaluated, as well as surface reactivity and the material’s capability to form apatite in simulated body fluid (SBF). The human fibroblast proliferation was tested on materials in cell culture. In in vivo experiments, toxicological tests, material degradation and tissue reactions were tested both in subcutaneous space and in experimental bone defects. The composites containing bioactive glass formed a unified layer of apatite on their surface in SBF. The size and amount of glass granules affected the degradation of polymer matrix, as well the material’s surface reactivity. In cell culture on the test materials the human gingival fibroblasts proliferated and matured faster compared with control materials. In in vitro tests a connective tissue capsule was formed around the specimens, and became thinner in the course of time. Foreign body cell reactions in toxicological tests were mild. In experimental bone defects the specimens with a high concentration of small bioactive glass granules (<45 μm) formed a dense apatite surface layer that restricted the bone ingrowth to material. The range of large glass granules (90-315 μm) with high concentrations formed the best bonding with bone, but slow degradation on the copolymer restricted the bone growth only in the superficial layers. In these studies, the handling properties of the material proved to be good and tissue reactions were mild. The reactivity of bioactive glass was retained inside the copolymer matrix, thus enabling bone conductivity with composites. However, the copolymer was noticed to degradate too slowly compared with the bone healing. Therefore, the porosity of the material should be increased in order to improve tissue healing.

From Fluid Dynamics to Human Psychology.What Drives Financial Markets Towards Extreme Events

For decades researchers have been trying to build models that would help understand price performance in financial markets and, therefore, to be able to forecast future prices. However, any econometric approaches have notoriously failed in predicting extreme events in markets. At the end of 20th century, market specialists started to admit that the reasons for economy meltdowns may originate as much in rational actions of traders as in human psychology. The latter forces have been described as trading biases, also known as animal spirits. This study aims at expressing in mathematical form some of the basic trading biases as well as the idea of market momentum and, therefore, reconstructing the dynamics of prices in financial markets. It is proposed through a novel family of models originating in population and fluid dynamics, applied to an electricity spot price time series. The main goal of this work is to investigate via numerical solutions how well theequations succeed in reproducing the real market time series properties, especially those that seemingly contradict standard assumptions of neoclassical economic theory, in particular the Efficient Market Hypothesis. The results show that the proposed model is able to generate price realizations that closely reproduce the behaviour and statistics of the original electricity spot price. That is achieved in all price levels, from small and medium-range variations to price spikes. The latter were generated from price dynamics and market momentum, without superimposing jump processes in the model. In the light of the presented results, it seems that the latest assumptions about human psychology and market momentum ruling market dynamics may be true. Therefore, other commodity markets should be analyzed with this model as well.