Abstract
Nowadays, designing and developing wearable devices that could detect many types of diseases has become inevitable for E-health field. The decision-making of those wearable devices is done by various levels of analysis of enormous databases of human health records. Systems that demand a huge number of input data to decide to require real-time data collected from devices, processes, and analyzing the data. Many researchers utilize the Internet of Things (IoT) in medical wearable devices to detect different diseases by using different sensors together for one goal. The IoT promises to revolutionize the lifestyle using a wealth of new services, based on interactions between large numbers of devices data. The proposed work is human monitor system to track the human body troubles. Smart wearable devices can provide users with overall health data, and alerts from sensors to notify them on their mobile phones accordingly. The proposed system developed a technique using Internet of Things technique to decrease the load on IOT network and decrease the overall cost of the users. The simulation results proved that the proposed system could provide identical communication for IOT devices even if many nodes are used.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Joonyoung, J., Kiryong, H., Jeonwoo, L., Youngsung, K., Daeyoung, K.: Wireless body area network in a ubiquitous healthcare system for physiological signal monitoring and health consulting. J. Image Process. Pattern Recogn. 1, 47–54 (2008)
Ngoc, T., Phan, D.: Human activities recognition in android smartphone using support vector machine. In: 7th International Conference on Intelligent Systems, Modelling and Simulation. IEEE (2016)
Yang, L., Yike, G.: Wiki-health: from quantified self to self-understanding. Future Gener. Comput. Syst. 56, 333–359 (2016)
Mileo, A., Merico, D., Bisiani, R.: Support for context-aware monitoring in home healthcare. J. Ambient Intell. Smart Environ. 2, 49–66 (2010)
Wood, A., Virone, G., Stankovic, J.: Context-aware wireless sensor networks for assisted-living and residential monitoring. IEEE Netw. 22, 26–33 (2008)
Bennebroek, M., Barroso, A., Atallah, L., Lo, B., Yang, G.: Deployment of wireless sensors for remote elderly monitoring. In: The 12th IEEE International Conference on e-Health Networking, Application and Services, pp. 1–5 (2010)
Mart´ın, H., Bernardos, A.M., Bergesio, L., Tarr´ıo, P.: Analysis of key aspects to manage wireless sensor networks in ambient assisted living environments. In: The 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies, pp. 1–8 (2009)
Qixin, W., Wook, S., Xue, L.: I-living: an open system architecture for assisted living. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 4268–4275 (2006)
Kyoung, C., Bongshin, L.: Characterizing visualization insights from quantified selfers’ personal data presentations. IEEE Comput. Graph. Appl. 35, 28–37 (2015)
Deborah, L.: The diverse domains of quantified selves: self-tracking modes and dataveillance. Econ. Soc. 45, 101–122 (2016)
Kenneth, L., Donghyeon, R., Lee, M.: Bio-inspired sensors for structural health monitoring bio-inspired sensors for structural health monitoring, pp. 255–274 (2015)
Jung, S., Ahn, J., Hwang, D., Kim, S.: An optimization scheme for M2 M-based patient monitoring in ubiquitous healthcare domain. Int. J. Distrib. Sens. Netw. 8(4), 708762 (2012)
Gil, G., Berlanga, A., Molina, J.: In context to multisensor architecture to obtain people context from smartphones. Int. J. Distrib. Sens. Netw. 8(4), 758789 (2012)
Gara, F., Saad, L., Ayed, R.: RPL protocol adapted for healthcare and medical applications. In: International Wireless Communications and Mobile Computing Conference, pp. 690–695 (2015)
Russell, B.: Extended self and the digital world. Elsevier 10, 50–54 (2016)
Castillejo, P., Martínez, J.F., López, L., Rubio, G.: An Internet of Things approach for managing smart services provided by wearable devices. Int. J. Distrib. Sens. Netw. (2013)
Shehab, A., Elhoseny, M., Hassanein, A.: A hybrid scheme for automated essay grading based on LVQ and NLP techniques. In: 12th IEEE International Computer Engineering Conference (ICENCO) (2016). doi:10.1109/ICENCO.2016.7856447
Elhoseny, H., Elhoseny, M., Abdelrazek, S., Bakry, H., Riad, A.: Utilizing Service Oriented Architecture (SOA) in smart cities. Int. J. Adv. Comput. Technol. (IJACT) 8(3), 77–84 (2016)
Elhoseny, M., Yuan, X., Yu, Z., Mao, C., El-Minir, H., Riad, A.: Balancing energy consumption in heterogeneous wireless sensor networks using a genetic algorithm. IEEE Commun. Lett. 19(2), 2194–2197 (2015). doi:10.1109/LCOMM.2014.2381226
Yuan, X., Elhoseny, M., Minir, H., Riad, A.: A genetic algorithm-based, dynamic clustering method towards improved WSN longevity. J. Netw. Syst. Manag. 25(1), 21–46 (2017). doi:10.1007/s10922-016-9379-7. Springer, US
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Shehab, A., Ismail, A., Osman, L., Elhoseny, M., El-Henawy, I.M. (2018). Quantified Self Using IoT Wearable Devices. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_77
Download citation
DOI: https://doi.org/10.1007/978-3-319-64861-3_77
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64860-6
Online ISBN: 978-3-319-64861-3
eBook Packages: EngineeringEngineering (R0)