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<p>Wearable sensors (WS) are transforming personalized health monitoring by providing continuous, real-time tracking of physiological and environmental parameters. This manuscript presents a comprehensive overview of the rapid growth in the wearable technology market and its integration into healthcare systems, driven by advancements in flexible, biocompatible materials and the increasing need for remote monitoring due to aging populations and chronic illnesses. Diverse sensor types, including accelerometers, Electrocardiography, photoplethysmography, and temperature and glucose sensors, are enabling early disease detection, chronic condition management, and emergency interventions. The synergy between artificial intelligence and WS is enhancing data interpretation, predictive analytics, and personalized care through advanced algorithms like machine learning, deep learning, and natural language processing. The paper further explores recent biomedical engineering implementations, such as gait analysis, cardiovascular and body temperature monitoring systems, and non-invasive glucose detection using interstitial fluid and sweat. While highlighting innovations, such as optical coherence tomography and bioimpedance-based continuous glucose monitoring systems, the review also addresses challenges including data security, algorithmic bias, and regulatory concerns. Future trends suggest deeper AI integration, miniaturization, and improved energy efficiency of WS, with promising applications in precision nutrition and implantable monitoring. Ultimately, the convergence of AI, biomedical engineering, and wearable technologies paves the way for a more proactive, patient-centered healthcare paradigm. </p>

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The Potential of Wearable Sensor Technologies in Enhancing Personalized Health Monitoring and Management

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • Durga Prasad Mishra,
  • Prafulla Kumar Sahu

摘要

Wearable sensors (WS) are transforming personalized health monitoring by providing continuous, real-time tracking of physiological and environmental parameters. This manuscript presents a comprehensive overview of the rapid growth in the wearable technology market and its integration into healthcare systems, driven by advancements in flexible, biocompatible materials and the increasing need for remote monitoring due to aging populations and chronic illnesses. Diverse sensor types, including accelerometers, Electrocardiography, photoplethysmography, and temperature and glucose sensors, are enabling early disease detection, chronic condition management, and emergency interventions. The synergy between artificial intelligence and WS is enhancing data interpretation, predictive analytics, and personalized care through advanced algorithms like machine learning, deep learning, and natural language processing. The paper further explores recent biomedical engineering implementations, such as gait analysis, cardiovascular and body temperature monitoring systems, and non-invasive glucose detection using interstitial fluid and sweat. While highlighting innovations, such as optical coherence tomography and bioimpedance-based continuous glucose monitoring systems, the review also addresses challenges including data security, algorithmic bias, and regulatory concerns. Future trends suggest deeper AI integration, miniaturization, and improved energy efficiency of WS, with promising applications in precision nutrition and implantable monitoring. Ultimately, the convergence of AI, biomedical engineering, and wearable technologies paves the way for a more proactive, patient-centered healthcare paradigm.