Open AccessEnhancing the Accuracy of Health Care Internet of Medical Things in Real Time using CNNets
Author(s) -
Muntadher Khamees,
Israa Mishkhal,
Hassan Hadi Saleh
Publication year - 2021
Publication title -
iraqi journal of science
Language(s) - English
Resource type - Journals
eISSN - 2312-1637
pISSN - 0067-2904
DOI - 10.24996/ijs.2021.62.11.34
Subject(s) - computer science , the internet , convolutional neural network , state (computer science) , sequence (biology) , artificial intelligence , activity recognition , conditional random field , internet of things , machine learning , data mining , pattern recognition (psychology) , algorithm , computer security , biology , world wide web , genetics
This paper presents an efficient system using a deep learning algorithm that recognizes daily activities and investigates the worst falling cases to save elders during daily life. This system is a physical activity recognition system based on the Internet of Medical Things (IoMT) and uses convolutional neural networks (CNNets) that learn features and classifiers automatically. The test data include the elderly who live alone. The performance of CNNets is compared against that of state-of-the-art methods, such as activity windowing, fixed sample windowing, time-weighted windowing, mutual information windowing, dynamic windowing, fixed time windowing, sequence prediction algorithm, and conditional random fields. The results indicate that CNNets are competitive with state-of-the-art methods, exhibiting enhanced IoMT accuracy of 98.37%, which is the highest among the proposed solutions using the same dataset.