Journal article Open Access
Hayder Jassim Hassaballah; Rashid Ali; Bushra Abdulkareem Abdulazeez AlHayali
In this research paper, a modern framework is presented to detect anomaly in medical wireless body sensor network systems that are incorporated in distant observation of patient’s vital signs. The suggested framework effects analysis of data in a sequential manner using a mini gateway utilized as a root station to discover abnormal alterations and to deal with inaccurate computations in gathered medical information minus advance awareness of irregular occurrences or consistent data patterns. The suggested perspective relies on Principal Component Analysis (PCA) utilized in spatial analysis and dimension reduction for gathered medical details. The key goal is distinguishing defective calculations from clinical dangers for reduction of false alarms prompted by incorrect computations or ill-behaved sensors. The result from the experiments on real medical datasets reveal that the suggested PCA perspective is able to attain good discovery perfection with lesser falt alarm rate when contrasted with other perspectives that fail to minimize the excessive dimension of gathered more information so in multivariate Wireless Body Sensor Networks (WBSN) implementations, and dynamic streaming nature of sensor data, mostly in medical implementations.