.png){kind=icon}
返回
Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol
.png){kind=icon}
PRE
AI
DOI：10.1109/MCE.2019.2941456.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nowadays, many people are facing the problem of arthritis. Regular monitoring and consultation of joint health from a specialist can help patients with this chronicle disease. The ratio of orthopedic doctors to patients with arthritis is low, worldwide. Use of smart devices can support the healthcare industry a lot. Motivated by these facts, here we propose an architecture to track the hand movements of the patient. For regular monitoring of patients with arthritis, fog and cloud gateways for real-time response generation are used. Thread protocol and Bayesian network classifier have been included in the proposed architecture to achieve reliable communication and anomaly detection, respectively. A dataset of 431 patients with arthritis is taken in real time and simulated on OMNet++ simulator. Observations show that the packet delivery ratio is improved by 15-20%, the response time is reduced by 20-30%, and the packet delivery rate is improved by 25-35%, in comparison to not using the fog and thread protocol.
Keyword:
Arthritis
Protocols
Monitoring
Logic gates
Smart devices
Sensors
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.1
论文数:
1.3K
被引数:
1.8K
机构
引用论文
分享收藏3D Human Gait Reconstruction and Monitoring Using Body-Worn Inertial Sensors and Kinematic Modeling使用身体佩戴的惯性传感器和运动学建模的3D人体步态重建和监测.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏分享收藏
分享收藏 没有更多内容
没有更多内容