IoT Based Patient Fall Detection and Alerting System for Patient Safety

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2024 | Volume :11 | Issue : 03 | Page : 21-30
By
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Sruthi S Madhavan,

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Hamsavarthan,

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Ragul V,

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Thangavel M,

  1. Assistant Professor, , Department of Artificial Intelligence and Data Science, Nehru Institute of Engineering and Technology, Anna University,, Coimbatore,, India
  2. Student,, Department of Artificial Intelligence and Data Science, Nehru Institute of Engineering and Technology, Anna University,, Coimbatore,, India
  3. Student,, Department of Artificial Intelligence and Data Science, Nehru Institute of Engineering and Technology, Anna University,, Coimbatore,, India
  4. Student,, Department of Artificial Intelligence and Data Science, Nehru Institute of Engineering and Technology, Anna University,, Coimbatore,, India

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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’s worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, neural networks is actually a sub-field of machine learning, and deep learning is a sub-field of neural networks.

Keywords: IoT, Internet of Things, patient safety, fall detection, wearable sensors, healthcare, machine learning, real-time monitoring, alerting system, caregiver intervention.

[This article belongs to Journal of Microcontroller Engineering and Applications (jomea)]

How to cite this article:
Sruthi S Madhavan, Hamsavarthan, Ragul V, Thangavel M. IoT Based Patient Fall Detection and Alerting System for Patient Safety. Journal of Microcontroller Engineering and Applications. 2024; 11(03):21-30.
How to cite this URL:
Sruthi S Madhavan, Hamsavarthan, Ragul V, Thangavel M. IoT Based Patient Fall Detection and Alerting System for Patient Safety. Journal of Microcontroller Engineering and Applications. 2024; 11(03):21-30. Available from: https://journals.stmjournals.com/jomea/article=2024/view=0

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References
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Regular Issue Subscription Original Research
Volume 11
Issue 03
Received 01/07/2024
Accepted 23/10/2024
Published 15/10/2024
343