STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Microcontroller Engineering and Applications Original Research

IoT-based Patient Fall Detection and Alerting System for Patient Safety

  1. Sruthi S Madhavan Department of Artificial Intelligence and Data Science, Nehru Institute of Engineering and Technology, Anna University, Coimbatore
  2. Hamsavarthan Department of Artificial Intelligence and Data Science, Nehru Institute of Engineering and Technology, Anna University, Coimbatore
  3. Ragul V Department of Artificial Intelligence and Data Science, Nehru Institute of Engineering and Technology, Anna University, Coimbatore
  4. Thangavel M Department of Artificial Intelligence and Data Science, Nehru Institute of Engineering and Technology, Anna University, Coimbatore

Abstract

This paper presents an Internet of Things (IoT) based patient fall detection and alerting system designed to enhance patient safety in healthcare settings. Falls among patients, especially in hospitals or care facilities, can lead to severe injuries and complications. The proposed system utilizes wearable sensors integrated with IoT technology to continuously monitor the movements and activities of patients. Machine learning algorithms are employed to analyze sensor data in real time, enabling the system to accurately detect fall events. Upon detection of a fall, the system triggers immediate alerts to healthcare providers or caregivers, facilitating prompt intervention and reducing response time. Through its proactive approach, the IoT-based system aims to mitigate the risks associated with patient falls, thereby enhancing overall patient safety and well-being in healthcare environments. Since deep learning and machine learning tend to be used interchangeably, it is worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, neural networks are a sub-field of machine learning, and deep learning is a sub-field of neural networks.

Keywords

References (15)

  1. Majumder S, Aghayi E, Noferesti M, Memarzadeh-Tehran H, Mondal T, Pang Z, et al. Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges. Sensors. 2017;17(11):2496. doi:10.3390/s17112496
  2. Abdulmalek S, Nasir A, Jabbar WA, Almuhaya MAM, Bairagi AK, Khan MAM, et al. IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review. Healthcare. 2022;10(10):1993. doi:10.3390/healthcare10101993
  3. Saguna S, Åhlund C, Larsson A. Experiences and challenges of providing IoT-based care for elderly in real-life smart home environments. Handbook of Integration of Cloud Computing, Cyber Physical Systems and Internet of Things. 2020;255–71.
  4. El Zouka HA, Hosni MM. Secure IoT communications for smart healthcare monitoring system. Internet of Things. 2021 Mar 1;13:100036.
  5. Karar ME, Shehata HI, Reyad O. A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends. Applied Sciences. 2022;12(7):3276. doi:10.3390/app12073276
  6. Hercog D, Sedonja D, Recek B, Truntič M, Gergič B. Smart home solutions using Wi-Fi-based hardware. Tehnički Vjesnik. 2020;27:1351–8.
  7. Behera SK, Saisudha G, Vasundhara L, Nasreen J. Wear-an integrated watch for patients. In: 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE; 2022. p. 1–4.
  8. Javed AR, Fahad LG, Farhan AA, Abbas S, Srivastava G, Parizi RM, et al. Automated cognitive health assessment in smart homes using machine learning. Sustainable Cities and Society. 2021;65:102572. doi:10.1016/j.scs.2020.102572
  9. Ashraf Z, Sohail A, Hameed A, Farhan M, Alotaibi FA, Alnfiai MM. Robust and Lightweight Remote User Authentication Mechanism for Next-Generation IoT-Based Smart Home. IEEE Access. 2023;11:137899-137910. doi:10.1109/access.2023.3336763
  10. Rajaei H. An IoT-based smart monitoring system detecting patient falls. In: Annual Modeling and Simulation Conference (ANNSIM). IEEE; 2022. p. 839–50. doi:10.23919/ANNSIM55834.
  11. 2022.9859346.
  12. Gupta A, Srivastava R, Gupta H, Kumar B. IoT-based fall detection monitoring and alarm system for elderly. IEEE 7th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). Prayagraj, India: IEEE; 2020. p. 1–5. doi:10.1109/UP
  13. CON50219.2020.9376569.
  14. Mahesh G, Kalidas M. A Real-Time IoT Based Fall Detection and Alert System for Elderly. 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). 2023:327-331. doi:10.1109/icaiccit60255.2023.10465914
  15. Edeib SRM, Dziyauddin RA, Amir NIM. Fall detection and monitoring using machine learning: A comparative study. Int J Adv Comput Sci Appl. 2023;14. doi:10.14569/IJACSA.2023.
Support