Remote Healthcare Monitoring using Adaptive Data Transmission for Anomaly Detection and Risk Assessment in a Wireless Sensor Network
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Abstract
An integrated system has been developed to remotely track health status using Wireless Medical Sensor Networks (WMSNs) for continuous patient monitoring. Physiological information is collected in remote locations and then sent to cloud-based repositories, where it is further analyzed, allowing real-time telemedical diagnostics and therapeutics conducted by urban specialists in cooperation with local healthcare providers. Sensors provide isolation and constant surveillance of patients infected with infectious diseases in their homes. This system employs multi-stage processing, beginning with the collection of baseline data, including heart rate, blood oxygen saturation, and temperature. This data then undergoes several processes to improve its quality, such as collecting raw measurements, reducing noise, and compensating for missing values. Next, each patient's risk score is assessed, and an abnormality score is calculated. To eliminate false alarms, an alarm system with a confirmation mechanism is activated in high-risk situations. Finally, the transmission rate is adjusted based on the risk level using adaptive sampling/event-based transmission to ensure a rapid response. The aim of this approach is to reduce power consumption, increase network efficiency, and accelerate transmission during emergencies. The data transmission rates are applied according to risk stratification. Such records undergo descriptive and inferential statistical analysis, and visualizations are created using Python-based data analytics libraries. It is centered on early disease diagnosis, faster treatment adjustments by physicians, cost reduction, and reducing unnecessary encounters in hospitals.
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