Quantified Self Using IoT Wearable Devices

by | Aug 31, 2017 | Research Publications

Author(s):

  • Ahmed Ismail
  • Lobna Osman
  • Mohamed Elhoseny
  • I. M. El-Henawy
  • Abdulaziz Shehab

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.

Documentation:

https://doi.org/10.1007/978-3-319-64861-3_77

References:
  1. 1.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)Google Scholar
  2. 2.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)‏Google Scholar
  3. 3.Yang, L., Yike, G.: Wiki-health: from quantified self to self-understanding. Future Gener. Comput. Syst. 56, 333–359 (2016)CrossRefGoogle Scholar
  4. 4.Mileo, A., Merico, D., Bisiani, R.: Support for context-aware monitoring in home healthcare. J. Ambient Intell. Smart Environ. 2, 49–66 (2010)Google Scholar
  5. 5.Wood, A., Virone, G., Stankovic, J.: Context-aware wireless sensor networks for assisted-living and residential monitoring. IEEE Netw. 22, 26–33 (2008)CrossRefGoogle Scholar
  6. 6.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)Google Scholar
  7. 7.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)Google Scholar
  8. 8.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)Google Scholar
  9. 9.Kyoung, C., Bongshin, L.: Characterizing visualization insights from quantified selfers’ personal data presentations. IEEE Comput. Graph. Appl. 35, 28–37 (2015)CrossRefGoogle Scholar
  10. 10.Deborah, L.: The diverse domains of quantified selves: self-tracking modes and dataveillance. Econ. Soc. 45, 101–122 (2016)CrossRefGoogle Scholar
  11. 11.Kenneth, L., Donghyeon, R., Lee, M.: Bio-inspired sensors for structural health monitoring bio-inspired sensors for structural health monitoring, pp. 255–274 (2015)Google Scholar
  12. 12.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)CrossRefGoogle Scholar
  13. 13.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)CrossRefGoogle Scholar
  14. 14.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)Google Scholar
  15. 15.Russell, B.: Extended self and the digital world. Elsevier 10, 50–54 (2016)Google Scholar
  16. 16.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)Google Scholar
  17. 17.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
  18. 18.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)Google Scholar
  19. 19.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 CrossRefGoogle Scholar
  20. 20.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, USCrossRefGoogle Scholar