Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Stairs and Pedestrian Crosswalks Detection Using Morphological Image Processing and Analysis in Order to Guide Visually Impaired Persons

Većina slijepih i slabovidnih osoba još ne koristi napredne sustave za pomoć pri kretanju i orijentaciji. Iako još nije vrijeme za potpuno izbacivanje ustaljenih metoda poput bijelog štapa, napredak tehnologije sada omogućava razvijanje i postupno uvođenje digitalnih mobilnih sustava za pomoć slijepima i slabovidnima. U ovoj disertaciji opisana je problematika koju mora riješiti takav sustav s naglaskom na metode navođenja prilikom kretanja korištenjem kamere i računalnom obradom slike. Ovo istraživanje usmjereno je na specifične situacije kada se osoba nalazi ispred ili na stepenicama i pješačkim prijelazima kao potencijalnim kritičnim točkama prilikom kretanja. Osim pregleda postojećih metoda detaljno su opisane tri novorazvijene metode zajedno s njihovom evaluacijom. Razvijene metode uključuju: metodu za detekciju stepenica zasnovanu na vertikalnoj i horizontalnoj analizi, multirezolucijsku metodu za detekciju pješačkih prijelaza zasnovanu na morfološkoj analizi i energiji linija, metodu za zvučno usmjeravanje slijepih i slabovidnih određivanjem prostora za sigurno kretanje. Dodatno je razvijen okvir za evaluaciju metoda usmjeravanja slijepih i slabovidnih osoba na stepenicama i pješačkim prijelazima. Testiranjem razvijenih metoda pokazane su određene prednosti u odnosu na postojeće metode po pitanju uspješnosti detekcije, mogućnosti korištenja širokokutnih ulaznih slika i robusnosti u slučajevima zaklonjenosti traženih objekata. Testiranjem brzine izvođenja razvijenih metoda pokazana je mogućnost izvođenja u realnom vremenu što je iznimno važno za pomoćne sustave koji bi se trebali koristiti u pokretu.Most of the blind and visually impaired persons are still not using advanced navigation and orientation assistance systems. Though it is not yet time to fully expel standard methods such as a white cane, advances in technology now enable the development and gradual introduction of digital mobile systems for helping the blind and visually impaired people. This dissertation describes the issues that need to be solved by such a system, focusing on navigation methods using camera and digital image processing. This research is focused on specific situations when a person is in front of or on stairs and pedestrian crosswalks as potential critical points when walking. In addition to an overview of the existing methods, three newly developed methods are described in detail along with their evaluation. Developed methods include: method for stairs detection using vertical and horizontal analysis, multiresolution method for pedestrian crosswalks detection based on morphological analysis and line energy, method for sound guidance of the blind and visually impaired by determining space for safe movement. There is also an additionally developed framework for evaluating the methods for guidance of the blind and visually impaired on stairs and pedestrian crosswalks. Testing of the developed methods has shown some advantages over existing methods regarding the accuracy, the ability to use with wide-angle input images and the robustness in cases of concealed objects. By testing the processing speed for developed methods, possibility to perform in real-time is proven, which is extremely important for the assistance systems that should be used in the movement

Content-based Digital Video Processing. Digital Videos Segmentation, Retrieval and Interpretation.

Recent research approaches in semantics based video content analysis require shot boundary detection as the first step to divide video sequences into sections. Furthermore, with the advances in networking and computing capability, efficient retrieval of multimedia data has become an important issue. Content-based retrieval technologies have been widely implemented to protect intellectual property rights (IPR). In addition, automatic recognition of highlights from videos is a fundamental and challenging problem for content-based indexing and retrieval applications. In this thesis, a paradigm is proposed to segment, retrieve and interpret digital videos. Five algorithms are presented to solve the video segmentation task. Firstly, a simple shot cut detection algorithm is designed for real-time implementation. Secondly, a systematic method is proposed for shot detection using content-based rules and FSM (finite state machine). Thirdly, the shot detection is implemented using local and global indicators. Fourthly, a context awareness approach is proposed to detect shot boundaries. Fifthly, a fuzzy logic method is implemented for shot detection. Furthermore, a novel analysis approach is presented for the detection of video copies. It is robust to complicated distortions and capable of locating the copy of segments inside original videos. Then, iv objects and events are extracted from MPEG Sequences for Video Highlights Indexing and Retrieval. Finally, a human fighting detection algorithm is proposed for movie annotation

Tracking moving objects in surveillance video

The thesis looks at approaches to the detection and tracking of potential objects of interest in surveillance video. The aim was to investigate and develop methods that might be suitable for eventual application through embedded software, running on a fixed-point processor, in analytics capable cameras. The work considers common approaches to object detection and representation, seeking out those that offer the necessary computational economy and the potential to be able to cope with constraints such as low frame rate due to possible limited processor time, or weak chromatic content that can occur in some typical surveillance contexts. The aim is for probabilistic tracking of objects rather than simple concatenation of frame by frame detections. This involves using recursive Bayesian estimation. The particle filter is a technique for implementing such a recursion and so it is examined in the context of both single target and combined multi-target tracking. A detailed examination of the operation of the single target tracking particle filter shows that objects can be tracked successfully using a relatively simple structured grey-scale histogram representation. It is shown that basic components of the particle filter can be simplified without loss in tracking quality. An analysis brings out the relationships between commonly used target representation distance measures and shows that in the context of the particle filter there is little to choose between them. With the correct choice of parameters, the simplest and computationally economic distance measure performs well. The work shows how to make that correct choice. Similarly, it is shown that a simple measurement likelihood function can be used in place of the more ubiquitous Gaussian. The important step of target state estimation is examined. The standard weighted mean approach is rejected, a recently proposed maximum a posteriori approach is shown to be not suitable in the context of the work, and a practical alternative is developed. Two methods are presented for tracker initialization. One of them is a simplification of an existing published method, the other is a novel approach. The aim is to detect trackable objects as they enter the scene, extract trackable features, then actively follow those features through subsequent frames. The multi-target tracking problem is then posed as one of management of multiple independent trackers