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

Sudeep Tanwar, Jayneel Vora, Shriya Kaneriya, Sudhanshu Tyagi, Neeraj Kumar, Vishal Sharma, Ilsun You

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.