Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol

Abstract

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.