Utilizing behavioral deep learning models to monitoar and alert physicians regarding trauma cases

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.