Improving Data Transmission by Efficient Communication Protocol to Control Wearable Sensors with Risk Level Analysis in Smart E-Health
Keywords:
wearable sensor, smart healthcare, message queuing telemetry transport, decision making, risk priorityAbstract
A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. Wearable biosensors are attracting much attention in the medical and physiological therapeutic disciplines due to their ability to offer patients time-sensitive data, non-intrusive assessments of biochemical markers dispersed across the body in the bloodstream, and real-time diagnostic devices. These types of sensors are a new option for evaluating human health and take advantage of some technology that needs to be put in hospitals. Wearable sensors have come a long way, but there are still numerous potentials and problems in substances, sensing efficiency, and practical application. Therefore, we still have a ways to go before human health metrics are continuously monitored over an extended period. This is achievable by using the right methods of communication and patient risk-level decision-making techniques. Since MQTT is an effective communication protocol for data transmission, Smart E-Health (SEH) is designed in this study. In addition, Fuzzy-based Back Propagation Neural Network (FuzzBPNN) is made to determine the risk level of a patient's health state based on the results of their vital signs. A risk variable with a value range of 0 to 1 is a proxy for the risk level. A patient's health is more critically ill and requires more medical care, the higher the risk value. The MIMIC II dataset is taken and compared with the state-of-the-art methods for experimental analysis. It is found that Smart_FuzzBPNN achieves a 98.4% of detection rate, 11% of packet drop rate, 94% of risk level analysis detection, and 97.5% of energy efficiency in 12.5ms.
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