Optimized health parameters using PSO: A cost effective RFID based wearable gadget with less false alarm rate

Abstract

The motive of contriving the proposed research was originated after recognizing some false alarm in measuring health parameters and practical issues in hospitals for example approachability and presence of patient at designated place for the evaluation of the health parameters like Blood pressure, sugar, body temperature, pulse and some other parameters. The manual entry of the data into the systems have been become a critical problem. To vanquish this problem, a wearable gadget has been designed, so that a patient can carry it feasibly. Various approaches like Bayesian classifier have been applied to minimize the false alarm. Fuzzy logic, Kalman filtering, extended Kalman filtering, support vector machine, multilayer perceptron, adaptive neuro fuzzy inference system and cuckoo search have been applied to eliminate the false alarm and give the best optimum solution. Results have proved that PSO worked betted as its convergence time is less than the others algorithms and produced best optimum value for the body vitals. Moreover, PSO has been applied to achieve the best optimal results and to increase the performance. Monitoring of heart pulse rate and body temperature readings that were recorded in database were then tested and validated by comparing digital thermometer and digital inflator. PSO will converge and give the global best position of data by updating the velocity. Results proved that PSO produced better results by optimizing with better accuracy and precision and can be acknowledged as cost-effective solution.