Internet of Medical Things (IoMT) for Remote Healthcare Monitoring Using Wearable Sensors

S M Nazmuz Sakib

S M Nazmuz Sakib

Keywords: IoMT (Internet of Medical Things), remote health monitoring (RHM), medical, sensor.

Abstract

In the healthcare industry, the most recent innovations and developments in communication and informational technique play a critical role. These developments paved the mode for the IoMT (Internet of Medical Things), which allows for persistent, distant, and genuine patient monitoring. The capacity, connectivity protocols, big data and volume of data, adaptability, durability, data processing, data acquisition system, data handling, and analytics accessibility, economic viability, privacy and security, and power efficiency are all problems that IoMT architectures confront. The basic aim of his research is to employ remote health monitoring (RHM) and IoMT to identify health monitor easy approach to improve medical residing conditions. Moreover, the use of IoMT and Medical knowledge RHM to strengthen the protection, diagnosis, prognosis, and therapeutic capacities of the Internet of medical things is addressed. The IoMT (Internet of Medical Things) involves a network of health care devices and individuals that exchange medical data via wireless communications. With the rising expansion of the population and the use of advanced technology, medical expenses and medical cost has significantly increased. The coupling of IoMT with medical healthcare has the potential to increase people's lives, deliver effective medical therapy, and establish pocket-friendly approaches. This study discusses the current state of IoT in the medical business, as well as plans for development and innovation strategies and implementations. The adoption of IoT in the medical sector has evolved worldwide, but it still encounters numerous architectural and technological hurdles. To find a solution to the mentioned problems, current research illustrates a basic IoMT design that comprises 3 foundational pillars: data gathering, connection gateways, and servers/cloud. Ultimately, this article examined the possibilities and potential of IoMT in practice, as well as the associated remarkable research concern.

References

Abd-El-Atty, B., A.M. Iliyasu, H. Alaskar, A. El-Latif and A. Ahmed. 2020. A robust quasi-quantum walks-based steganography protocol for secure transmission of images on cloud-based E-healthcare platforms. Sensors. 20:3108.
[2]. AbdulGhaffar, A., S.M. Mostafa, A. Alsaleh, T. Sheltami and E.M. Shakshuki. 2020. Internet of things based multiple disease monitoring and health improvement system. J. Ambient Intell. Humaniz. Comput. 11:1021–1029.
[3]. Ahmed, F. 2017. An Internet of Things (IoT) application for predicting the quantity of future heart attack patients. Int. J. Comput. Appl. 164:36–40.
[4]. Al-Khafajiy, M., T. Baker, C. Chalmers, M. Asim, H. Kolivand, M. Fahim and A. Waraich. 2019. Remote health monitoring of elderly through wearable sensors. Multimed. Tools Appl. 78:24681–24706.
[5]. Al-Makhadmeh, Z. and A. Tolba. 2019. Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach. Measurement. 147:106815.
[6]. Alfian, G., M. Syafrudin, M.F. Ijaz, M.A. Syaekhoni, N.L. Fitriyani and J. Rhee. 2018. A personalized healthcare monitoring system for diabetic patients by utilizing BLE-based sensors and real-time data processing. Sensors. 18:2183.
[7]. Ali, L., A. Rahman, A. Khan, M. Zhou, A. Javeed and J.A. Khan. 2019. An automated diagnostic system for heart disease prediction based on ${\chi^{2}} $ statistical model and optimally configured deep neural network. IEEE Access. 7:34938–34945.
[8]. AlShorman, O., B. AlShorman, M. Alkhassaweneh and F. Alkahtani. 2020. A review of internet of medical things (IoMT)–based remote health monitoring through wearable sensors: A case study for diabetic patients. Indones. J. Electr. Eng. Comput. Sci. 20:414–422.
[9]. AlShorman, O.M. and A.M. Alshorman. 2020. Frontal lobe and long-term memory retrieval analysis during pre-learning stress using EEG signals. Bull. Electr. Eng. Informatics. 9:141–145.
[10]. Ani, R., S. Krishna, N. Anju, M.S. Aslam and O.S. Deepa. 2017. Iot based patient monitoring and diagnostic prediction tool using ensemble classifier. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, 1588–1593.
[11]. Antolín, D., N. Medrano, B. Calvo and F. Pérez. 2017. A wearable wireless sensor network for indoor smart environment monitoring in safety applications. Sensors. 17:365.
[12]. Athira, A., T.D. Devika and K.R. Varsha. 2020. Design and development of IOT based multi-parameter patient monitoring system. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, 862–866.
[13]. Benjemmaa, A., H. Ltifi and M. Ben Ayed. 2019. Design of remote heart monitoring system for cardiac patients. In: International Conference on Advanced Information Networking and Applications, Springer, 963–976.
[14]. Bhardwaj, K.K., A. Khanna, D.K. Sharma and A. Chhabra. 2019. Designing energy-efficient IoT-based intelligent transport system: need, architecture, characteristics, challenges, and applications. In: Energy Conservation for IoT Devices, Springer, 209–233.
[15]. Cappon, G., G. Acciaroli, M. Vettoretti, A. Facchinetti and G. Sparacino. 2017. Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment. Electronics. 6:65.
[16]. Chen, E.T. 2020. Examining the influence of information technology on modern health care. In: Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications, IGI Global, 1943–1962.
[17]. Chen, M., W. Li, Y. Hao, Y. Qian and I. Humar. 2018. Edge cognitive computing based smart healthcare system. Futur. Gener. Comput. Syst. 86:403–411.