International Journal on Artificial Intelligence Tools

Any physical space can be augmented with digital information, associated to various locations or artefacts contained in it. In this paper, an abstract architecture is presented, supporting interaction with mobile context aware applications in public digitally augmented spaces. We focus on how this architecture supports personalization of interaction and adaptation. The main goal of the architecture is to allow users to benefit of a uniform and personalized experience across different contexts and spaces. Among the concerns are: to support high interoperability and flexibility and to address issues of privacy and user control. The framework has been tested in typical augmented spaces: a library and a museum. The paper concludes with a set of examples of use of the defined framework that cover typical situations for intra-space and across spaces usage.

In the present work, we investigate the performance of a number of traditional and recent speech enhancement algorithms in the adverse non-stationary conditions, which are distinctive for motorcycles on the move. The performance of these algorithms is ranked in terms of the improvement they contribute to the speech recognition accuracy, when compared to the baseline performance, i.e. without speech enhancement. The experiments on the MoveOn motorcycle speech and noise database indicated that there is no equivalence between the ranking of algorithms based on the human perception of speech quality and the speech recognition performance. The Multi-band spectral subtraction method was observed to lead to the highest speech recognition performance.

The monitoring of human physiological data, in both normal and abnormal situations of activity, is interesting for the purpose of emergency event detection, especially in the case of elderly people living on their own. Several techniques have been proposed for identifying such distress situations using either motion, audio or video data from the monitored subject and the surrounding environment. This paper aims to present an integrated patient fall detection system that may be used for patient activity recognition and emergency treatment. Visual data captured from the user's environment, using overhead cameras among with motion and physiological data collected from the subject's body are utilized. Appropriate tracking techniques are applied to the aforementioned visual perceptual component enabling the trajectory tracking of the subjects, while acceleration data from the sensors can indicate a fall incident. Trajectory information and subject's visual location can verify fall and indicate an emergency event, whereas the interpretation of biosignals like electrocardiogram (ECG) and blood oxygen saturation (SPO2) can indicate the severity of the incident with the help of rules-based evaluation. The paper includes also the assessment of several classifiers and meta-classifiers in terms of accuracy in detecting falls and a user based evaluation.

We present a system for recognising human behaviour given a symbolic representation of surveillance videos. The input of our system is a set of time-stamped short-term behaviours, that is, behaviours taking place in a short period of time — walking, running, standing still, etc — detected on video frames. The output of our system is a set of recognised long-term behaviours — fighting, meeting, leaving an object, collapsing, walking, etc — which are pre-defined temporal combinations of short-term behaviours. The definition of a long-term behaviour, including the temporal constraints on the short-term behaviours that, if satisfied, lead to the recognition of the long-term behaviour, is expressed in the Event Calculus. We present experimental results concerning videos with several humans and objects, temporally overlapping and repetitive behaviours. Moreover, we present how machine learning techniques may be employed in order to automatically develop long-term behaviour definitions.

The paper addresses the development of a hybrid technology for operational decision support in a pervasive environment. The technology encompasses the idea of implementing a decision support system as a set of Web-services. The Web-services are intended to form an ad-hoc collaborative environment, whose members cooperate with an objective of serving the current needs according to the decision situation. The collaborative environment is made up of resources of a pervasive environment. The technology is focused on three types of resources to be organized: information, problem-solving, and acting, and is supported by a service-based architecture of the decision support system providing types of Web-services needed for this technology implementation. The hybrid technology integrates technologies of ontology management, context management, constraint satisfaction, Web-services, profiling, and intelligent agents. The technology application is illustrated by the decision support for dynamic logistics.