Abstract
Recent advances in healthcare domain integrate IoT technology to effectively monitor living conditions in day-to-day life. Monitoring real-time data obtained from IoT sensors enables to predict the risk factors of any chronic diseases. Machine and deep learning algorithms make the job of physicians easier in predicting the seriousness of the diseases. This paper presents an exhaustive overview on the need for intelligent prediction models in IoT health care. It also reviews in detail the merits and demerits of the classification and prediction techniques. The generic framework for IoT healthcare prediction is proposed. This paper also outlines the healthcare sensors and its purposes for intelligent healthcare monitoring. The areas of further research have also been presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
A. Das, P. Rad, K.K.R. Choo, B. Nouhi, J. Lish, J. Martel, Distributed machine learning cloud teleophthalmology IoT for predicting AMD disease progression. Futur. Gener. Comput. Syst. 93, 486–498 (2019)
M.M. Hassan, M.Z. Uddin, A. Mohamed, A. Almogren, A robust human activity recognition system using smartphone sensors and deep learning. Futur. Gener. Comput. Syst. 81, 307–313 (2018)
P.M. Kumar, S. Lokesh, R. Varatharajan, G. Chandra Babu, P. Parthasarathy, Cloud and IoT based disease prediction and diagnosis system for healthcare using fuzzy neural classifier. Futur. Gener. Comput. Syst. 86, 527–534 (2018)
F.-Y. Leu, C.-Y. Ko, I. You, K. Kwang, A smartphone-based wearable sensors for monitoring real-time physiological data. Comput. Electr. Eng. 65, 376–392 (2018)
Q. An, S. Gan, X. Jianan, Y. Bao, A multichannel electrochemical all-solidstate wearable potentiometric sensor for real-time sweat ion monitoring. Electrochem. Commun. 107, 106553 (2019)
S. Veena, A. Monisha, V. Manjula, A survey on sensor tools for healthcare applications. Int. J. Res. Appl. Sci. Eng. Technol. 6 (2018)
S.K. Bhoi, S.K. Panda, B. Patra, B. Pradhan, FallDS-IoT: a fall detection system for elderly healthcare based on IoT data analytics, in International Conference on Information Technology (2018)
M. Nilashi, O. bin Ibrahim, H. Ahmadi, L. Shahmoradi, An analytical method for diseases prediction using machine learning techniques. Comput. Chem. Eng. 106, 212–223 (2017)
P. Naraei, A. Abhari, A. Sadeghian, Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data, in Future Technologies Conference (2016)
M.Z. Alom, M. Alam, T.M. Taha, in Object Recognition Using Cellular Simultaneous Recurrent Networks and Convolutional Neural Network (2017)
M.M. Hassan, M.Z. Uddin, A. Mohamed, A. Almogren, A robust human activity recognition system using smartphone sensors and deep learning. Futur. Gener. Comput. Syst. 81, 307–313 (2017)
Aashay Gondalia, Dhruv Dixit, Shubham Parashar, IoT-based healthcare monitoring system for war solders using machine learning. Proc. Comput. Sci. 133, 1005–1013 (2018)
X. Zhao, X. Zeng, L. Koehl, in An IoT-Based Wearable System Using Accelerometer and Machine Learning for Fetal Movement Monitoring (IEEE, 2019)
C.M.J.M. Dourado, S.P.P. da Silva, R.V.M. da Nóbrega, A.C. Antonio, P.P.R. Filho, V.H.C. de Albuquerque, Deep learning IoT system for online stroke detection in skull computed tomography images. Comput. Netw. 152, 25–39 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Philip, J.M., Durga, S., Esther, D. (2021). Deep Learning Application in IoT Health Care: A Survey. In: Peter, J., Fernandes, S., Alavi, A. (eds) Intelligence in Big Data Technologies—Beyond the Hype. Advances in Intelligent Systems and Computing, vol 1167. Springer, Singapore. https://doi.org/10.1007/978-981-15-5285-4_19
Download citation
DOI: https://doi.org/10.1007/978-981-15-5285-4_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5284-7
Online ISBN: 978-981-15-5285-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)