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The emerging surface computing trend is a key enabler for a wide range of ergonomic interfaces and applications. Surface computing interfaces are considered appropriate toward facilitating elderly interaction with ICT devices and services. In this paper, the authors present the development of an innovative low-cost multi-surface device and its application in elderly cognitive training. The multi-touch device has been designed and implemented as a cost-effective motivating environment for elderly cognitive training. Along with the implementation of the device and the bundled services, this paper also presents a number of cognitive training exercises that have been developed on the device.

The analysis of lectures and meetings inside smart rooms has recently attracted much interest in the literature, being the focus of international projects and technology evaluations. A key enabler for progress in this area is the availability of appropriate multimodal and multi-sensory corpora, annotated with rich human activity information during lectures and meetings. This paper is devoted to exactly such a corpus, developed in the framework of the European project CHIL, “Computers in the Human Interaction Loop”. The resulting data set has the potential to drastically advance the state-of-the-art, by providing numerous synchronized audio and video streams of real lectures and meetings, captured in multiple recording sites over the past 4 years. It particularly overcomes typical shortcomings of other existing databases that may contain limited sensory or monomodal data, exhibit constrained human behavior and interaction patterns, or lack data variability. The CHIL corpus is accompanied by rich manual annotations of both its audio and visual modalities. These provide a detailed multi-channel verbatim orthographic transcription that includes speaker turns and identities, acoustic condition information, and named entities, as well as video labels in multiple camera views that provide multi-person 3D head and 2D facial feature location information. Over the past 3 years, the corpus has been crucial to the evaluation of a multitude of audiovisual perception technologies for human activity analysis in lecture and meeting scenarios, demonstrating its utility during internal evaluations of the CHIL consortium, as well as at the recent international CLEAR and Rich Transcription evaluations. The CHIL corpus is publicly available to the research community.

Identifying people and tracking their locations is a key prerequisite to achieving context awareness in smart spaces. Moreover, in realistic context-aware applications, these tasks have to be carried out in a non-obtrusive fashion. In this paper we present a set of robust person-identification and tracking algorithms, based on audio and visual processing. A main characteristic of these algorithms is that they operate on far-field and un-constrained audio---visual streams, which ensure that they are non-intrusive. We also illustrate that the combination of their outputs can lead to composite multimodal tracking components, which are suitable for supporting a broad range of context-aware services. In combining audio---visual processing results, we exploit a context-modeling approach based on a graph of situations. Accordingly, we discuss the implementation of realistic prototype applications that make use of the full range of audio, visual and multimodal algorithms.

Sensing the environment and the body of care recipients is the cornerstone of any e-care system. In this paper we present the home infrastructure modules necessary for this sensing. We then focus on the sensor groups offering specific context extraction capabilities, the processing of their signals, the description of the resulting metadata and their local storage. Utilising the extracted context description, the caring system is enabled to offer personalised services to the care recipients.

Cognitive care for the elderly can be provided by training their cognitive skills in order to reduce age-related decline of cognitive capabilities. This is typically achieved with cognitive games that can benefit from computer interfaces. In this paper we present the design and use of a multi-touch surface that serves as a front-end for games designed to specifically support the decline in declarative and prospective memory. The selected games are a result of both user studies that identified a set of requirements for the design of the surface and previous research on this issue. The system can be potentially embedded on the surface of a table. It comprises of a modified Thin Film Transistor panel along with an acrylic surface. A video camera is used to capture the Frustrated Total Internal Reflection of fingers when these are subjected to infrared illuminators. Multi-touch functionality is achieved by a Kalman-based multiple target tracking algorithm that runs upon the feed of the camera and detects any number of fingers and their movement. Results indicate that users perceive the experience as a positive and functional process.

The ability to mark-up live sports event content, viewed from multiple camera angles, such that athletes and other objects of interest can be tracked, facilitates an exciting new personalised and interactive viewing experience for spectators, enabling spectators to act as directors of their own customised live sports videos. In this paper, such an approach is described as part of the My-e-Director 2012 project. The design of this platform is described here and a discussion of a prototype system is given.

Broadcasting of athletics is nowadays biased towards running (sprint and longer distances) sports. Personalized content delivery can change that for users that wish to focus on different content. Using a combination of video signal processing algorithms and live information that accompanies the video of large-scale sports like the Olympics, a system can attend to the preferences of users by selecting the most suitable camera view for them.There are two types of camera selection for personalized content delivery. According to the between sport camera selection, the view is changed between two sports, upon the onset of a sport higher up the user preferences than the one currently being delivered. According to the within sport camera selection, the camera is changed to offer a better view of the evolution of the sport, based on the phase it is in. This paper details the video processing algorithms needed for the extraction of the events that trigger both between and within sport camera selection, and describes a system that handles user preferences, live information andvideo-generated events to offer personalized content to the users. Broadcasting of athletics is nowadays biased towards running (sprint and longer distances) sports. Personalized content delivery can change that for users that wish to focus on different content. Using a combination of video signal processing algorithms and live information that accompanies the video of large-scale sports like the Olympics, a system can attend to the preferences of users by selecting the most suitable camera view for them.There are two types of camera selection for personalized content delivery. According to the between sport camera selection, the view is changed between two sports, upon the onset of a sport higher up the user preferences than the one currently being delivered. According to the within sport camera selection, the camera is changed to offer a better view of the evolution of the sport, based on the phase it is in. This paper details the video processing algorithms needed for the extraction of the events that trigger both between and within sport camera selection, and describes a system that handles user preferences, live information andvideo-generated events to offer personalized content to the users.

As pervasive computing technologies are gradually penetrating sport, we are witnessing a proliferating number of research systems that can track athletes during training and/or in competition. Indeed, athlete tracking is a particularly challenging research tasks, which is also a key enabler for a wide range of applications such as ambient personalized broadcasting. In this paper we survey person tracking systems for sport applications and illustrate their limitations for realistic sports environments. As a real-life example we present the robust and high-performance person tracking system developed at the Athens Information Technology, and explain its inability to deal with occlusions, interlacing, adverse and changing light conditions and mostly the strain of the athletes, which are very common in high activity athletic scenes. We also present techniques for dealing with these challenges, along with an architecture for building ambient camera selection environments for broadcasting purposes. These techniques form the basis for the person tracking and ambient camera selection systems that are developed in the scope of the my-e-Director 2012 EC project, which is working towards a realistic prototype system for the London 2012 Olympics.

This paper describes the video-based face recognition evaluation performed under the CHIL project and the systems that participated to it, along with the obtained first year results. The evaluation methodology comprises a specially built database of videos and an evaluation protocol. Two complete automatic face detection and recognition systems from two academic institutions participated to the evaluation. For comparison purposes, a baseline system is also developed using well-known methods for face detection and recognition.

Many face recognition methods have been reported in the literature. Also many face databases and face recognition methodologies are available to test them. Unfortunately most authors test their methods using restricted databases, or random subsets of them. This does not facilitate the comparison of the methods. In this paper we propose an evaluation methodology that utilizes three publicly available databases and an evaluation protocol that offers numerous splits of the images between training and testing images. We also evaluate many different face recognition methods using our methodology, offering a comparison between them.

Identifying people and tracking their locations is a key prerequisite to achieving context-awareness in smart spaces. Moreover, in realistic context-aware applications, these tasks have to be carried out in a non-obtrusive fashion. In this paper we present a set of robust person identification and tracking algorithms, based on audio and visual processing. A main characteristic of these algorithms is that they operate on far-field and unconstraint audio-visual streams, which ensures that they are non-intrusive. We also illustrate that the combination of their outputs can lead to composite multimodal tracking components, which are suitable for supporting a broad range of context-aware services. In combining audio-visual processing results, we exploit a context-modeling approach based on a graph of situations. Accordingly, we discuss the implementation of realistic prototype applications that make use of the full range of audio, visual and multimodal algorithms.