Wearable IoT Devices for Chronic Disease Monitoring: Sensors, Data Transmission, and Machine Learning Inference

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@article{1719751,
      author = {Olalekan Owolabi, Alaba Timothy Owoseni},
      title = {Wearable IoT Devices for Chronic Disease Monitoring: Sensors, Data Transmission, and Machine Learning Inference},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {1397-1416},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719751.pdf},
      abstract = {The increasing prevalence of chronic diseases has intensified the demand for healthcare solutions capable of supporting continuous, personalised, and cost-effective patient management beyond traditional clinical settings. Wearable Internet of Things (IoT) devices have emerged as a promising technology by integrating physiological sensors, wireless communication, and intelligent data analytics to enable real-time remote monitoring of patients. This narrative literature review critically examines recent advances in wearable IoT devices for chronic disease monitoring, with a particular focus on sensing technologies, data transmission mechanisms, and machine learning inference. The review synthesises evidence from contemporary peer-reviewed literature to examine how wearable sensors capture physiological signals, how IoT communication technologies facilitate secure and reliable data exchange, and how machine learning algorithms transform raw health data into clinically meaningful insights. The review highlights the roles of key wearable sensors, including electrocardiography (ECG), photoplethysmography (PPG), continuous glucose monitoring, accelerometers, and pulse oximetry, in monitoring chronic conditions such as cardiovascular disease, diabetes mellitus, chronic obstructive pulmonary disease, and neurological disorders. It further compares wireless communication technologies, including Bluetooth Low Energy (BLE), Wi-Fi, Zigbee, Narrowband IoT (NB-IoT), Long Range (LoRa), and fifth-generation (5G) networks, with respect to their suitability for remote healthcare applications. In addition, the review discusses the growing contribution of traditional machine learning, deep learning, edge artificial intelligence, and explainable artificial intelligence in supporting early disease detection, risk prediction, and clinical decision-making. Finally, current challenges—including interoperability, data privacy, cybersecurity, energy efficiency, and clinical validation—are critically discussed alongside emerging research directions. By integrating recent developments across sensing, communication, and intelligent analytics, this review provides a comprehensive overview of wearable IoT technologies and their potential to advance remote patient monitoring and personalised chronic disease management.},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1719751}
  }