Authors: Nasr Musaed S. Almurisi; Srinivasulu Tadisetty
Addresses: Department of Electronics & Communication Engineering, College of Engineering and Technology, Kakatiya University, Warangal, India ' Department of Electronics & Communication Engineering, College of Engineering and Technology, Kakatiya University, Warangal, India
Abstract: The rapid evolution in IoT technology has facilitated the development of smart applications in different sectors. Nowadays, IoT paradigm allows smart objects to be interconnected with medical devices to provide healthcare services. Recently, many healthcare systems (HS) have been proposed for integrating IoT with medical things. However, the performance of existing architecture is decreased when medical data is increased. Therefore, this article proposes a novel internet of medical device (IoMD) architecture to overcome the limitations of conventional HS. Since medical data are reaching the cloud, hence we propose a neural network method called deep LSTM that runs in the cloud platform and capable of processing huge data in a fast and efficient manner. A real dataset from UCL Repository is used to train and validate the proposed method. However, the analysis of the results confirms that our method can classify the activities with an accuracy of 98.60%.
Keywords: internet of things; IoT; internet of medical device; IoMD; artificial intelligence; cloud computing; long short-term memory; LSTM.
International Journal of Cloud Computing, 2023 Vol.12 No.2/3/4, pp.324 - 339
Received: 28 Jun 2020
Accepted: 08 Jan 2021
Published online: 14 May 2023 *