Open Access
Research Article
Utilizing behavioral deep learning models to monitoar and alert physicians regarding trauma cases
P. Prabuprabu.p@christuniversity.inDepartment of Computer Science CHRIST (Deemed to be University)Bangalore, Karnataka, 560029, IndiaView full profile → , Musleh Alsulamimhsulami@uqu.edu.saDepartment of Information Systems Umm Al-Qura UniversityMakkah, 21961, Saudi ArabiaView full profile → , Deafallah Alsadiedbsadie@uqu.edu.saDepartment of Information Systems Umm Al-Qura UniversityMakkah, 21961, Saudi ArabiaView full profile → , Abdul Khader Jilani Saudagaraksaudagar@imamu.edu.saDepartment of Information Systems Imam Mohammad Ibn Saud Islamic University (IMSIU)Information Systems Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadh, 11432, Saudi ArabiaView full profile → , Mohammed Alkhathamimaalkhathami@imamu.edu.saDepartment of Information Systems Imam Mohammad Ibn Saud Islamic University (IMSIU)Riyadh, 11432, Saudi ArabiaView full profile → , *Ramesh Chandra PooniaCorresponding authorrameshcpoonia@gmail.comDepartment of Computer Science CHRIST (Deemed to be University) Delhi NCRDepartment of Computer Science CHRIST (Deemed to be University) Ghaziabad, Uttar Pradesh, 201003, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 09 May 2023
- Published Online:
- 05 Jul 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1461
- Pages:
- 983–995
Abstract
IoT-based health monitoring is crucial for addressing the rising number of trauma cases, enabling timely treatment and symptom detection. This research combines IoT and Deep Learning to efficiently detect trauma cases and monitor patient health using data like body temperature and heart rate. A Deep Convolutional Neural Network (DCNN) enhances fall detection accuracy. Results show significant improvements over k-NN, SVM, and DT, with a 4.00% increase in precision, 2.60% in recall, 5.04% in accuracy, and 2.81%. In F-Measure compared to ANN, RNN, and LSTM. This approach revolutionizes healthcare in Smart Cities by leveraging IoT and machine learning to improve patient outcomes and access to remote healthcare resources.
Keywords
Subject Classifications
Primary 93A30Secondary 49K15
References
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