A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application. (April 2018)

• Record Type: Journal Article
• Authors: Mostafa, Salama A.; Mustapha, Aida; Mohammed, Mazin Abed; Ahmad, Mohd Sharifuddin; Mahmoud, Moamin A.
• Is Part Of: International journal of medical informatics. Volume 112(2018)
• Page Start: 173
• Page End: 184
• Publication Date: 2018-04
• Subjects: Autonomous agent -- Multi-agent system -- Adjustable autonomy -- Fuzzy logic -- Elderly remote care; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2018.02.001
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: A Fuzzy Logic-based Adjustable Autonomy (FLAA) model to manage the autonomy of multi-agent systems. The FLAA model is implemented in an Automated Elderly Movements Monitoring (AEMM-Care) system. The AEMM-Care system monitors elderly people daily activities and implements fall detection and prevention tasks in a complex environment. The results show that the model improves the performance of the agents and the accuracy of the fall detection and prevention tasks. Abstract: Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls.