A Comprehensive Review on IoT-Enabled AI and Sensor-Based Wearable Systems for Real-Time Health Monitoring, Fall Detection, and Personal Safety

Authors

  • Aditya R. Shinde Undergraduate Student, Department of Computer Engineering, Rajgad Dnyanpeeth Technical Campus, Pune Savitribai Phule Pune University, Pune, Maharashtra, India
  • Vaibhavi C. Mahadik Undergraduate Student, Department of Computer Engineering, Rajgad Dnyanpeeth Technical Campus, Pune Savitribai Phule Pune University, Pune, Maharashtra, India
  • Ranveer R. Patil Undergraduate Student, Department of Computer Engineering, Rajgad Dnyanpeeth Technical Campus, Pune Savitribai Phule Pune University, Pune, Maharashtra, India
  • Varad V. Patwari Undergraduate Student, Department of Computer Engineering, Rajgad Dnyanpeeth Technical Campus, Pune Savitribai Phule Pune University, Pune, Maharashtra, India
  • S. B. Patil Principle, Department of Computer Engineering, Rajgad Dnyanpeeth Technical Campus, Pune Savitribai Phule Pune University, Pune, Maharashtra, India
  • P. S. Kedge Professor, Department of Computer Engineering, Rajgad Dnyanpeeth Technical Campus, Pune Savitribai Phule Pune University, Pune, Maharashtra, India

Keywords:

Artificial Intelligence (AI), Data privacy, Edge computing, Energy optimization, Federated learning, Fall detection, Healthcare monitoring, Internet of Things (IoT), Machine Learning, Sensor fusion, Wearable devices

Abstract

The rapidly evolving landscapes of the Internet of Things (IoT), Artificial Intelligence (AI), and sensor technologies are revolutionizing the healthcare ecosystem through enhanced real-time monitoring, streamlined decision-making, and healthcare delivery across settings beyond traditional clinical environments. More specifically, incidental falls particularly among older adults represent a major public health challenge with significant secondary outcomes, such as physical injuries, hospitalization, and loss of independence. Recognizing and understanding the associated risk factors early is crucial. This review paper summarizes the recent developments in fall detection and prevention technologies, focusing on IoT-enabled wearables, sensor fusion modalities, and intelligent analytic models. It also highlights our IoT prototype design within a wearable pendant device. The evolution of adaptive and federated machine learning frameworks deployed on low-power edge devices has improved detection accuracy and reliability. Nonetheless, challenges persist in ensuring data privacy, optimizing energy consumption, achieving external validation, and maintaining cost-effectiveness.

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Published

2025-12-19

Issue

Vol. 4 No. 3 (2025)

Section

Articles