An Ambient Multi-Agent System for Healthcare Monitoring of Patients With Chronic Diseases

2019 ◽  
pp. 61-71
Author(s):  
Imane Boussebough ◽  
Issam Eddine Chaib ◽  
Billel Boudjit
Keyword(s):  
Chronic Diseases ◽  
Health Monitoring ◽  
Context Awareness ◽  
Ambient Intelligence ◽  
Detection Systems ◽  
Routine Tasks ◽  
Healthcare Monitoring ◽  
Multi Agent ◽  
Promising Solution ◽  
Mobile Health Monitoring

Chronic diseases are a major cause of death in the world. Thus, many guidelines have been proposed to prevent these diseases. In addition, various systems have been developed to ease health monitoring. However, they are generally behaving as reminders or as anomaly detection systems. After giving an overview of the existed solutions and discussing their drawbacks, the authors present their system which is called ambient healthcare monitoring system (AHMS). It provides a continuous, unobtrusive, and mobile health monitoring of patients with chronic diseases. It is based on the multi-agent paradigm that allows devices to be distributed and autonomous. In addition, it benefits from the characteristics of ambient intelligence (AmI) such as ubiquity and context-awareness. So, AHMS is a promising solution for unobtrusive healthcare monitoring, in which it offers efficient medical services, with less energy consumption, that can significantly reduce the healthcare cost by automating some routine tasks. Consequently, it reduces the latency as well it minimizes the overload on the caregiver.

CAs-Based QoS Scheme for Remote Health Monitoring

2013 ◽  
Vol 5 (4) ◽  
pp. 44-67 ◽  
Author(s):  
B. Sathish Babu ◽  
K. Bhargavi ◽  
Pallapa Venkatarm
Keyword(s):  
Health Monitoring ◽  
High Reliability ◽  
Computer Assisted ◽  
Remote Health Monitoring ◽  
Agent Based ◽  
Behavior Observation ◽  
Multi Agent ◽  
And Control ◽  
Mobile Health Monitoring ◽  
Remote Health

Integrated Wireless Networks (IWN) is an important area of today's research because of its application in comprehensive services like battlefield surveillance, traffic avoidance and control systems, mobile health monitoring, biological detection and agricultural fields, structural health monitoring, Computer-assisted rehabilitation and therapy, tele-robotic surgery, etc. IWNs are employed to collect voluminous data from different types of networks and correlate them to provide critical medical services with high reliability and efficiency. This paper proposes an architecture which uses Cognitive Agents (CAs) along with Behavior-Observation-Belief (BOB) model in the area of Remote Health Monitoring (RHM), in order to provide better QoS by reducing the latency. The analytical modeling and simulation of the proposed system shows that, there is a considerable reduction in latency compared to the existing Multi-Agent based m-Health Care system.

scholarly journals How Do Patients with Chronic Diseases Make Usage Decisions regarding Mobile Health Monitoring Service?

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Fanbo Meng ◽  
Xiaofei Zhang ◽  
Xitong Guo ◽  
Kee-hung Lai ◽  
Xinli Zhao
Keyword(s):  
Chronic Disease ◽  
Chronic Diseases ◽  
Mobile Health ◽  
Health Monitoring ◽  
Critical Role ◽  
Measurement Model ◽  
Uncertainty Avoidance ◽  
Related Factors ◽  
Monitoring Service ◽  
Mobile Health Monitoring

Objectives. The increasing population of patients with chronic diseases generates great challenge of chronic disease management. The occurrence of mobile health monitoring service (MHMS) is beneficial to chronic disease prevention and health promotion. The objective of this study is to investigate how patients with chronic diseases make usage decisions on MHMS. Study Design. A survey. Methods. 213 respondents with chronic diseases were asked to rate their level of health severity, negative health emotions, and health uncertainty avoidance. SmartPLS was used to test the measurement model. Results. Of 213 research respondents, 159 of them have one chronic disease, while 54 have more than one such disease. Perceived health severity of patients with chronic diseases positively influences MHMS usage intentions, while negative health emotions do not. Health uncertainty avoidance strengthens the effect of health severity but weakens the effect of negative health emotions on MHMS usage intentions. Conclusion. Patients with chronic diseases have a unique decision-making process regarding MHMS usage in which their special health-related factors and tendencies play a critical role in determining behavioral intentions.

scholarly journals Smart Wearable Sensors Based on Triboelectric Nanogenerator for Personal Healthcare Monitoring

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 352
Author(s):  
Ruonan Li ◽  
Xuelian Wei ◽  
Jiahui Xu ◽  
Junhuan Chen ◽  
Bin Li ◽  
...  
Keyword(s):  
Medical Care ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
The Elderly ◽  
The Body ◽  
Assessment System ◽  
Triboelectric Nanogenerator ◽  
Personal Healthcare ◽  
Healthcare Monitoring ◽  
Smart Wearable

Accurate monitoring of motion and sleep states is critical for human health assessment, especially for a healthy life, early diagnosis of diseases, and medical care. In this work, a smart wearable sensor (SWS) based on a dual-channel triboelectric nanogenerator was presented for a real-time health monitoring system. The SWS can be worn on wrists, ankles, shoes, or other parts of the body and cloth, converting mechanical triggers into electrical output. By analyzing these signals, the SWS can precisely and constantly monitor and distinguish various motion states, including stepping, walking, running, and jumping. Based on the SWS, a fall-down alarm system and a sleep quality assessment system were constructed to provide personal healthcare monitoring and alert family members or doctors via communication devices. It is important for the healthy growth of the young and special patient groups, as well as for the health monitoring and medical care of the elderly and recovered patients. This work aimed to broaden the paths for remote biological movement status analysis and provide diversified perspectives for true-time and long-term health monitoring, simultaneously.

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