Human Activity Recognition using a Semantic Ontology-Based Framework

In the last years, the extensive use of smart objects embedded in the physical world, in order to monitor and record physical or environmental conditions, has increased rapidly. In this scenario, heterogeneous devices are connected together into a network. Data generated from such system are usually stored in a database, which often shows a lack of semantic information and relationship among devices. Moreover, this set can be incomplete, unreliable, incorrect and noisy. So, it turns out to be important both the integration of information and the interoperability of applications. For this reason, ontologies are becoming widely used to describe the domain and achieve efficient interoperability of information system. An example of the described situation could be represented by Ambient Assisted Living context, which intends to enable older or disabled people to remain living independently longer in their own house. In this contest, human activity recognition plays a main role because it could be considered as starting point to facilitate assistance and care for elderly. Due to the nature of human behavior, it is necessary to manage the time and spatial restrictions. So, we propose a framework that implements a novel methodology based on the integration of an ontology for representing contextual knowledge and a Complex Event Processing engine for supporting timed reasoning. Moreover, it is an infrastructure where knowledge, organized in conceptual spaces (based on its meaning) can be semantically queried, discovered, and shared across applications. In our framework, benefits deriving from the implementation of a domain ontology are exploited into different levels of abstrac- tion. Thereafter, reasoning techniques represent a preprocessing method to prepare data for the final temporal analysis. The results, presented in this paper, have been obtained applying the methodology into AALISABETH, an Ambient Assisted Living project aimed to monitor the lifestyle of old people, not suffering from major chronic diseases or severe disabilities

An ontology-based consultation system to support medical care on board seagoing vessels

Background: A realistic possibility to obtain medical care for patients located in remote sites such as seagoing vessels, in which health professionals are not available, is to contact a doctor via telecommunication systems. In general, the medical knowledge of who on board ships is in charge of medical care is quite limited and therefore, in a first level telemedical consultation, the flow of information should be correct and its efficiency should be maximised. This paper describes an application conceived to improve requests of medical assistance from sailing ships. The ultimate objective of this system is a) to standardise as much as possible the requests of medical advice at a distance, b) to overcome language barriers and jammed-related troubles that could make difficult or not understandable a telephone conversation. Materials and methods: The application is based on a software engine extracting data from an ontological knowledgebase built ad hoc using Protégé. Results: Compared to the conventional consultation systems based on telephone and e-mail, the proposed device is more accurate and complete in terms of information contained in the request of assistance. Moreover, data received by the medical centre can be more easily managed, as they can be standardised. Conclusions: The system described here allows people responsible of medical care on board ships to forward detailed requests of assistance containing symptom-guided information on patient clinical conditions. This may represent an innovative tool for medical consultations at distance allowing the remote centre to provide more precise and quicker medical advice.

Using Ontology and Complex Event Processing Engine for Human Activity Recognition in Ambient Assisted Living domain

As a result of the rising older people population, the Ambient Assisted Living (AAL) branch is growing up fast. Generally, the main goal of an AAL system is to help old people to live in their own houses longer and with an improved quality of life. In fact, its functionalities are based on the use of a set of different sensors interconnected by different types of communication systems to get information about the status of patients. The installed sensors network produce a set of data that shows a fine—grained nature, carrying generally their value, originating device, data type, timestamp and so on. For this reason, it is not always possible to have a clear overall view. Since it is often needed an analysis procedure which is able to take into account the semantic of records, ontologies are becoming widely used to describe the domain and to enrich the acquired data with its significance. In our research work, we propose a methodology arranged by two components integrated sequentially: an ontology [1] and a Complex Event Processing (CEP) [2] engine. The ontology has is built following a precise structure and it is able to describe the AAL domain, organize data according to their semantic meaning and select them (pre-processing phase). The main serious expressiveness limitation of OWL ontology is the lack of temporal reasoning, so in the framework it is introduced after ontology a CEP engine that is a technique concerned with timely detection of compound events within streams of simple events. Our challenge is to perform semantic queries on a data repository, whose records originate from a network of heterogeneous sources. The main goal of such queries is the pattern matching process, i.e. recognition of specific temporal sequences in fine—grained data. In our framework, benefits deriving from the implementation of a domain ontology are exploited i11 different levels of abstraction. Thereafter, reasoning techniques represent a pre—processing method that prepares data for the final temporal analysis. Our proposed approach will be applied to the ongoing AALISABETH [3], an Ambient Assisted Living project aimed to discover and manage the behavior of monitored users