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Agent-oriented activity recognition in the event calculus: An application for diabetic patients

Kafalı, Özgur, Romero, Alfonso E., Stathis, Kostas (2017) Agent-oriented activity recognition in the event calculus: An application for diabetic patients. Computational Intelligence, 33 (4). pp. 899-925. ISSN 0824-7935. (doi:10.1111/coin.12121) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:65852)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1111/coin.12121

Abstract

We present a knowledge representation framework on the basis of the Event Calculus that allows an agent to recognize complex activities from low-level observations received by multiple sensors, reason about the life cycle of such activities, and take action to support their successful completion. Activities are multivalue fluents that change according to events that occur in the environment. The parameters of an activity consist of a unique label, a set of participants involved in the performing of the activity, and a unique goal associated with the activity revealing the activity's desired outcome. Our contribution is the identification of an activity life cycle describing how activities can be started, interrupted, suspended, resumed, or completed over time, as well as how these can be represented. The framework also specifies activity goals, their associated life cycle, and their relation with the activity life cycle. We provide the complete implementation of the framework, which includes an activity generator that automatically creates synthetic sensor data in the form of event streams that represent the everyday lifestyle of a type 1 diabetic patient. Moreover, we test the framework by generating very large activity streams that we use to evaluate the performance of the recognition capability and study its relative merits.

Item Type: Article
DOI/Identification number: 10.1111/coin.12121
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Divisions: Faculties > Sciences > School of Computing
Depositing User: Ozgur Kafali
Date Deposited: 02 Feb 2018 14:13 UTC
Last Modified: 23 Jan 2020 04:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65852 (The current URI for this page, for reference purposes)
Kafalı, Özgur: https://orcid.org/0000-0001-9296-2087
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