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In recent times, healthcare IoT has proven to be highly effective in reducing medical and hospital resource stress produced by an ageing population. The health-care system's quick reply is critical because it is a safety-critical process. Fog computing, which deploys healthcare IoT devices on the edges of clouds, is a strategic solution for meeting the low bandwidth criterion. Such fog devices, on the other hand, consume a huge amount of data sources. Developing a specific system for fog devices in order to make sure secure data transmission and fast data analysis has become a critical issue. In this research, Random Forest Feature Selection (RFFS) algorithm is used to improve dependability of data transmission and processing prompt. Functionalities of RFFS comprise fault-tolerant data transmission, self-adaptive cleaning and data-load-deduction processing. The assessment of heart rate (HR) depends on wear devices is of attention in suitability. Photoplethysmography (PPG) is an auspicious method to evaluate HR owing to its lower cost; nevertheless, it is simply tainted by motion artifacts (MA). In our study, a robust method depend on random forest is projected for precisely assessing HR from the photoplethysmography sign soiled by intense movement artifacts exactly, a dependable transmission mechanism, achieved by a self-adaptive sieve, will recall lost or imprecise data inevitably. Over extensive simulations, we display that our anticipated scheme expands network dependability, and delivers a quicker processing rapidity. The suggested method is more accurate and resistant to extreme motion artefacts, suggesting that it could be used in smart technologies for fitness and health monitoring.
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