A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state
Wireless Sensor Networks (WSNs) play a crucial role in modern healthcare applications by sensing and communicating environmental and patient health data through wireless mediums. These networks enable real-time monitoring of patients, including the elderly and individuals with chronic conditions, of...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825000912 |
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| author | R. Manikandan S. Arunprakash Rakan A. Alsowail Tharani Pandiaraj |
| author_facet | R. Manikandan S. Arunprakash Rakan A. Alsowail Tharani Pandiaraj |
| author_sort | R. Manikandan |
| collection | DOAJ |
| description | Wireless Sensor Networks (WSNs) play a crucial role in modern healthcare applications by sensing and communicating environmental and patient health data through wireless mediums. These networks enable real-time monitoring of patients, including the elderly and individuals with chronic conditions, offering significant benefits in surveillance and healthcare management. However, several challenges persist in implementing WSNs for healthcare. Real-time administration faces obstacles such as managing patients, medical infrastructure, and staff while ensuring timely treatment delivery. Moreover, accurately predicting diseases and providing precise treatment remains complex due to the diverse nature of medical data and the limitations of traditional systems. This study proposes a deep learning-based approach integrated with WSNs to address these challenges. The system employs deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) models to automate healthcare functions such as patient monitoring, infrastructure management, and disease prediction. The proposed model (DCNN-LSTM) analyzes diverse data types, including numeric, alphanumeric, images, signals, and video, to accurately identify abnormal conditions and predict diseases. The system also generates real-time alerts for medical personnel, ensuring timely intervention. The proposed framework was validated using NS2 simulation software to model the WSN architecture and sensor node communication. Experimental results demonstrate that the DCNN-LSTM models achieved a classification accuracy of 96 % with a loss rate of 0.08, showcasing significant improvements over traditional approaches. Integrating WSNs and deep learning enhances real-time healthcare monitoring, improving efficiency and patient outcomes. |
| format | Article |
| id | doaj-art-69acd5df55d9483e9614778a3b62179c |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-69acd5df55d9483e9614778a3b62179c2025-02-06T05:11:11ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119149167A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health stateR. Manikandan0S. Arunprakash1Rakan A. Alsowail2Tharani Pandiaraj3Department of CSE, University College of Engineering, Ramanathapuram, India; Corresponding author.Department of EEE, Anna University, Regional Campus, Madurai, Tamil Nadu, IndiaComputer Skills, Self-Development Skills Department, Deanship of Common First Year, King Saud University, Riyadh 11362, Saudi ArabiaDepartment of Electronics and Communications Engineering, Periyar Maniammai Institute of Science & Technology, Thanjavur, IndiaWireless Sensor Networks (WSNs) play a crucial role in modern healthcare applications by sensing and communicating environmental and patient health data through wireless mediums. These networks enable real-time monitoring of patients, including the elderly and individuals with chronic conditions, offering significant benefits in surveillance and healthcare management. However, several challenges persist in implementing WSNs for healthcare. Real-time administration faces obstacles such as managing patients, medical infrastructure, and staff while ensuring timely treatment delivery. Moreover, accurately predicting diseases and providing precise treatment remains complex due to the diverse nature of medical data and the limitations of traditional systems. This study proposes a deep learning-based approach integrated with WSNs to address these challenges. The system employs deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) models to automate healthcare functions such as patient monitoring, infrastructure management, and disease prediction. The proposed model (DCNN-LSTM) analyzes diverse data types, including numeric, alphanumeric, images, signals, and video, to accurately identify abnormal conditions and predict diseases. The system also generates real-time alerts for medical personnel, ensuring timely intervention. The proposed framework was validated using NS2 simulation software to model the WSN architecture and sensor node communication. Experimental results demonstrate that the DCNN-LSTM models achieved a classification accuracy of 96 % with a loss rate of 0.08, showcasing significant improvements over traditional approaches. Integrating WSNs and deep learning enhances real-time healthcare monitoring, improving efficiency and patient outcomes.http://www.sciencedirect.com/science/article/pii/S1110016825000912Wireless sensor networkMedical industryPatient monitoringCNNLSTMMedical data analytics |
| spellingShingle | R. Manikandan S. Arunprakash Rakan A. Alsowail Tharani Pandiaraj A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state Alexandria Engineering Journal Wireless sensor network Medical industry Patient monitoring CNN LSTM Medical data analytics |
| title | A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state |
| title_full | A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state |
| title_fullStr | A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state |
| title_full_unstemmed | A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state |
| title_short | A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state |
| title_sort | novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state |
| topic | Wireless sensor network Medical industry Patient monitoring CNN LSTM Medical data analytics |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825000912 |
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