IoT-Based Humidity Sensing and Control in Smart Hospitals k-Nearest Neighbors for Infection Control and Patient Comfort

A novel method for monitoring and controlling humidity levels in smart hospital settings by means of the Internet of Things (IoT). To maximize patient comfort and infection control measures, IoT device connection allows for real-time humidity level monitoring and modification. Improving the system's responsiveness to humidity data is possible with the use of the k-Nearest Neighbors (KNN) algorithm. Machine learning (ML) method, the system can forecast the ideal relative humidity from past data and present weather. A major factor in preventing the spread of infectious diseases in healthcare facilities is keeping the relative humidity at an optimal level. The general health and success of the healing process depend on the patient's level of comfort. The proposed IoT system for smart hospital humidity management considers medical and patient needs. The system's capacity to dynamically adjust humidity levels based on data improves infection control and patient satisfaction.

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Published
2024-10-24
DOI
https://doi.org/10.1109/icicec62498.2024.10808268
Citations
1
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.35
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article

IoT-Based Humidity Sensing and Control in Smart Hospitals k-Nearest Neighbors for Infection Control and Patient Comfort

Sangeetha Srinivasan, Nilamadhab Mishra, John Benito Jesudasan Peter, K. Dhivya et al.
1 citations
IoT and Edge/Fog Computing
0.35
article

IoT-Based Humidity Sensing and Control in Smart Hospitals k-Nearest Neighbors for Infection Control and Patient Comfort

Sangeetha Srinivasan, Nilamadhab Mishra, John Benito Jesudasan Peter, K. Dhivya, Kuntiyellannagari Bhagya Laxmi, P. Rajalakshmi
article en
1 citations

Abstract

A novel method for monitoring and controlling humidity levels in smart hospital settings by means of the Internet of Things (IoT). To maximize patient comfort and infection control measures, IoT device connection allows for real-time humidity level monitoring and modification. Improving the system's responsiveness to humidity data is possible with the use of the k-Nearest Neighbors (KNN) algorithm. Machine learning (ML) method, the system can forecast the ideal relative humidity from past data and present weather. A major factor in preventing the spread of infectious diseases in healthcare facilities is keeping the relative humidity at an optimal level. The general health and success of the healing process depend on the patient's level of comfort. The proposed IoT system for smart hospital humidity management considers medical and patient needs. The system's capacity to dynamically adjust humidity levels based on data improves infection control and patient satisfaction.

Capital One (United States) (US), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), SRM Institute of Science and Technology (IN), Madhya Pradesh Bhoj Open University (IN), Barkatullah University (IN), Saveetha University (IN)
Openalex Percentile: Top 35%
IoT and Edge/Fog Computing
0.35
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IoT-Based Humidity Sensing and Control in Smart Hospitals k-Nearest Neighbors for Infection Control and Patient Comfort — Sangeetha Srinivasan, Nilamadhab Mishra, et al. · (2024) | TGRS Research Map | TGRS