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首页> 外文期刊>Consumer Electronics Magazine, IEEE >Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol
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Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol

机译：贝叶斯网络分类器与线程协议的雾计算环境中的人类关节炎分析

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摘要

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.
机译：如今，许多人面临关节炎问题。经常监测和协商专家联合卫生的咨询可以帮助患者患有该编年症疾病。全球性关节炎患者对关节炎患者的比例很低。使用智能设备可以支持医疗保健行业。这些事实的激励，这里我们提出了一种跟踪患者的手动运动的架构。用于定期监测有关节炎患者，使用用于实时响应生成的雾和云网关。线程协议和贝叶斯网络分类器已包含在所提出的架构中，以实现可靠的通信和异常检测。 431例关节炎患者的数据集实时采用并在OMNET ++模拟器上模拟。观察结果表明，分组输送比率提高了15-20％，响应时间减少了20-30％，并且除非不使用雾和线程协议相比，分组输送率提高了25-35％。

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