Physical Stint Virtual Representation of Biomedical Signals With Wireless Sensors Using Swarm Intelligence Optimization Algorithm

Shitharth, S.; Yonbawi, Saud; Manoharan, Hariprasath; Alahmari, Sultan; Yafoz, Ayman; Mujlid, Hana

doi:10.1109/jsen.2022.3233407

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…It collects, filters, and archives long-term activity data and physiological data using various sensors. In healthcare sector, a type of swarm intelligent algorithm is implemented in the recognition procedure with a modified fitness function and it is termed fruit fly optimization (FFO) ( Shitharth et al, 2023 ). The disease can be identified by evaluating the extracted data and its simulations before it gets grievous ( Selvarajan et al, 2022 ).…”

Section: Iot Founded Health Monitoring Of Patientsmentioning

confidence: 99%

Efficient data transmission on wireless communication through a privacy-enhanced blockchain process

Aluvalu

Thirumalaisamy

et al. 2023

PeerJ Computer Science

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In the medical era, wearables often manage and find the specific data points to check important data like resting heart rate, ECG voltage, SPO2, sleep patterns like length, interruptions, and intensity, and physical activity like kind, duration, and levels. These digital biomarkers are created mainly through passive data collection from various sensors. The critical issues with this method are time and sensitivity. We reviewed the newest wireless communication trends employed in hospitals using wearable technology and privacy and Block chain to solve this problem. Based on sensors, this wireless technology controls the data gathered from numerous locations. In this study, the wearable sensor contains data from the various departments of the system. The gradient boosting method and the hybrid microwave transmission method have been proposed to find the location and convince people. The patient health decision has been submitted to hybrid microwave transmission using gradient boosting. This will help to trace the mobile phones using the calls from the threatening person, and the data is gathered from the database while tracing. From this concern, the data analysis process is based on decision-making. They adapted the data encountered by the detailed data in the statistical modeling of the system to produce exploratory data analysis for satisfying the data from the database. Complete data is classified with a 97% outcome by removing unwanted data and making it a 98% successful data classification.

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Section: Iot Founded Health Monitoring Of Patientsmentioning

confidence: 99%