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<p>The Internet of Medical Things (IoMT) revolutionizes health-care domain by enabling continuous health monitoring through interconnected devices. Yet, high energy demand and communication burden restrict large-scale implementation. This paper introduces an energy-aware data gathering and transmission framework for IoMT networks, focusing on cardiac signal monitoring. The framework employs Hybrid Adaptive Orthogonal Matching Pursuit (HA-OMP) algorithm to optimize sensing operations in Point-of-Care Testing (POCT) devices to achieve lower energy consumption while preserving high signal accuracy. An optimized hardware framework integrates low-power sensing units and ESP32 micro-controller nodes to forward only data that are exceeding threshold level, thereby optimizing excess network traffic.Experimental findings confirm notable energy reduction of 7.4% and alongside a compression rate above 92%, and reliable reconstruction performance <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\((\text {PRD} \le 3\%, \text {MSE} \le 0.03)\)</EquationSource> </InlineEquation>. This research demonstrates a reliable and scalable approach for dynamic cardiac health monitoring suitable for energy limited IoMT settings.</p>

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IoMT-Based Compressive Sensing with Threshold Filtering for Healthcare Monitoring in POCT Device

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • Tamosa Chakraborty,
  • Debobrata Mitra,
  • Nashreen Nesa

摘要

The Internet of Medical Things (IoMT) revolutionizes health-care domain by enabling continuous health monitoring through interconnected devices. Yet, high energy demand and communication burden restrict large-scale implementation. This paper introduces an energy-aware data gathering and transmission framework for IoMT networks, focusing on cardiac signal monitoring. The framework employs Hybrid Adaptive Orthogonal Matching Pursuit (HA-OMP) algorithm to optimize sensing operations in Point-of-Care Testing (POCT) devices to achieve lower energy consumption while preserving high signal accuracy. An optimized hardware framework integrates low-power sensing units and ESP32 micro-controller nodes to forward only data that are exceeding threshold level, thereby optimizing excess network traffic.Experimental findings confirm notable energy reduction of 7.4% and alongside a compression rate above 92%, and reliable reconstruction performance \((\text {PRD} \le 3\%, \text {MSE} \le 0.03)\) . This research demonstrates a reliable and scalable approach for dynamic cardiac health monitoring suitable for energy limited IoMT settings.