This study develops a novel remote healthcare system based on Wireless Sensors Network System (WSNs) and Radio Frequency Identification (RFID) technologies. Cloud equipment is used as sensing cloud architecture to create the system database, and Improved Particle Swarm Optimization (IPSO) is applied to build a personal physiological signal sensing system. The collected personal physiological signals are analyzed, and RFID technology is used to create an administrator identity and database. The integrated physiological instrument measures/monitors blood pressure, heart rate, blood oxygen content, body weight, BMI and cardiogram. This system can be applied to, say, employees, nursing-home residents and the elderly. Physiological changes are identified at any time via a self-health examination, promoting early diagnosis and treatment. The current ZigBee technology, which has many advantages, is used in medical institutions, industry, and agriculture, and for automated control and building monitoring. This study uses WSNs technology to transfer physiological data to the cloud for analysis, processing, and storage. The client-side and appropriate medical personnel are notified by e-mail and short messages via the Internet, such that they can provide timely diagnosis and deploy treatment. The IPSO scheme is used to increase the efficiency and accuracy when searching for at-risk groups, searching data, and defining and summing the weights of physiological data. If the first 10% of users with high weight values are a risky population that must be treated immediately, this system informs medical personnel immediately, potentially improving medical service quality and application of medical resources. 
Health parameter monitoring via a novel wireless system