Multi Agent Application for Chronic Patients: Monitoring and Detection of Remote Anomalous Situations

Abstract

The clinical study of the most basic vital signs of a patient represents the simplest and most effective way to detect and monitor health problems. There are many diseases that can be diagnosed and controlled through regular monitoring of these medical data. The purpose of this study is to develop a monitoring and tracking system for the various vital signs of a patient. In particular, this work focuses on the design of a multi-agent architecture composed of virtual organizations with capabilities to integrate different medical sensors on an open, low-cost hardware platform. This system integrates hardware and software elements needed for the routine measurement of vital signs, performed by the patient or caregiver without having to go to a medical center.

A Goal-Based Framework for Supporting Medical Assistance: The Case of Chronic Diseases

“…In a real-time monitoring system for crisis prevention and/or compliance with treatment plans, all goals are coded starting from the guidelines and the system must prevent the patient from going to put himself in situations at risk -the different goals are to save the patient from the risky situations identified in the guidelines. Our work is in line with the framework used in [3]. However, in addition to what is proposed in that work, which is based on the measurement of all values (obtained from sensors), our framework give us also the possibility to (i) choose the most useful measurement to do, (ii) obtain information from the patient and use it when making a decisions, and to (iii) use background knowledge.…”

“…Many works in the literature propose solutions to help decreasing this number. Most of them propose solutions to assist health care practitioners, thus helping them to take good decisions according to the patient health conditions [2,3] and other works aim at assisting the patients managing themselves -Self-monitoring of health [4,5,6,7,8,9]. However, as it has been underlined in [10], medical assistance combine medical actions performed by health care professionals who observe signs and symptoms and decide on questions, interventions, prescriptions or tests to treat the health problem of a particular patient.…”

Coin Recognition Approach in Social Environments Using Virtual Organizations of Agents

“…Furthermore, dependent elderlies have dramatically increased during the last decade and require new care solutions [3][4][5][6]. Pervasive systems can help dependent or disabled people to perform daily tasks in a non-obstructive manner [7,8]. In this sense, intelligent environments can improve services and can have a high social impact [9].…”

“…Multi-agent systems are computational distributed systems with high capacities for learning and adaptation and can be very appropriate to design pervasive systems [10]. Multi-agent systems have been successfully used to develop pervasive systems in different scenarios [7,11,12] and can contribute to improving the labour and social integration of disabled people [8]. Multi-agent systems not only provide distributed communication and computation capacities, but also reasoning and adaptation abilities, which allow us to design intelligent adaptive environments in a simple and flexible way, for example, to help visually disabled people in common tasks such as recognizing objects like coins in different situations of their daily life.…”

A Self-Organized Learning Model for Anomalies Detection: Application to Elderly People

“…In this paper, we propose a novel approach based on a multiagent system to detect in real-time behavioural anomalies by using feedback from the medical staff. The usage of the multiagent paradigm has shown interesting results in e-health as it naturally fits with the ambient assisted living paradigm, composed of many heterogeneous and distributed devices in a highly dynamical environment in strong interaction with humans [10] [11] [12] [13]. The novelty of our approach is that it transforms medical staff feedback into constraints and thus, approaches the problem of adaptation of the anomaly detection algorithm to a patient and his pathology through a real-time linear optimization process.…”