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While prior research has explored wireless monitoring systems for tracking heart rate variability (HRV) using protocols like Zigbee—a low-power, short-range communication technology—these systems often fall short in integrating robust data processing frameworks. For instance, many existing solutions focus primarily on real-time data transmission but neglect advanced analytics, such as noise filtering, anomaly detection, or long-term trend analysis, limiting their clinical utility [13]. Concurrently, advancements in machine learning (ML) and deep learning (DL) have demonstrated significant potential in applications like disease prediction and medical image classification (e.g., identifying tumors in radiology scans). However, current methodologies face challenges in efficiently handling the volume, velocity, and complexity of modern healthcare datasets. Issues such as computational bottlenecks, slow inference times, and scalability constraints hinder their deployment in time-sensitive clinical environments, where rapid analysis of large-scale data (e.g., high-resolution imaging or continuous physiological signals) is critical [14–16] |
IEEE/ICACT20250032 Slide.06
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