Abstract
Health is very important to each one and for the early discovery of different diseases to get timely treatment Wireless Body Sensor Networks (WBSNs) a subgroup of wireless sensor networks can be used. WBSNs works for collecting the health related data such as ECG, EEG, Glucose values, temperature etc. from human body and route the data towards the destination so that the patients information can be sent to the concerned person. In WBSN the sensor node can either be appropriately located on the body or inserted inside the body. In this work firstly the EEG signals characterization is done with the help of neural networks. EEG signal processing is done with the help of DWT. Then in the secondly routing mechanism used to send the data to the destination. In the health monitoring system routing has an important role. The facts sensed by the sensors need to be routed to the destination effectively. In this work a routing algorithm proposed which can be used to transfer the data to destination efficiently. To route the data to the sink multi hop process is used and the cost function is used to find the node next closest node in the route from source to destination. The experiments are done in MATLAB environment and simulation result shows that efficient classification of EEG signal is done and the proposed scheme provides the less end-to-end as well as good throughput.
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
References
Gonzalez, E., Peña, R., Vargas-Rosales, C., Avila, A., deCerio, D.P.-D.: Survey of WBSNs for pre-hospital assistance: trends to maximize the network lifetime and video transmission techniques. Sensors 15, 11993–12021 (2015). https://doi.org/10.3390/s150511993
Vedavathi, B.S., Biradar, S., Hiremath, S.G., Thippeswamy, G.: Wavelet transform based neural network model to detect and characterize ECG and EEG signals simultaneously. In: IEEE International Advance Computing Conference Number #35547 Xplore Compliant, BMS College of Engineering Bangalore (2015). www.iacc2015.com. ISBN 978-1-4799-8047-5
Sharma, S., Vyas, A.L., Thakke, B., Mulvaney, D., Datta, S.: Wireless body area network for health monitoring. In: IEEE 4th International Conference on Biomedical Engineering and Informatics (BMEI) 978-1-4244-9352-4/11/$26.00 ©2011. IEEE (2011)
Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Zunaidi, I., Hazry, D.: EEG feature extraction for classifying emotions using FCM and FKM. Int. J. Comput. Commun. 1, 21–25 (2007)
Jafari, R., Lotfian, R.: A low power wake-up circuitry based on dynamic time warping for body sensor networks. In: Proceedings of the 2011 International Conference on Body Sensor Network (BSN), Dallas, TX, USA, pp. 83–88 (2011)
Luo, X., Dong, M., Huang, Y.: On distributed fault-tolerant detection in wireless sensor networks. IEEE Trans. Comput. 55, 58–70 (2006)
Kwong, J., Ramadass, Y.K., Verma, N., Chandrakasan, A.P.: A 65 nm sub-Vt microcontroller with integrated SRAM and switched capacitor DC-DC converter. IEEE J. Solid State Circ. 44, 115–126 (2009)
Atallah, L., Lo, B., King, R., Yang, G.Z.: Sensor placement for activity detection using wearable accelerometers. In: Proceedings of the 2010 International Conference on Body Sensor Networks (BSN), Biopolis, Singapore, pp. 24–29 (2010)
Song, K.T., Wang, Y.Q.: Remote activity monitoring of the elderly using a two-axis accelerometer. In: Proceedings of the CACS Automatic Control Conference, Tainan, Taiwan (2005)
Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensors—a review of classification techniques. Phys. Meas. 30, 353–363 (2009)
Akin, A., Stephan, B., Mihai, M.P., Paluca, M.P., Paul, H.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: Proceedings of the 23rd International Conference on Architecture of Computing Systems (ARCS), Hannover, Germany, pp. 1–10 (2010)
Natarajan, A., Motani, M., de Silva, B., Yap, K., Chua, K.: Investigating network architectures for body sensor networks. In: Proceedings of the 1st ACM SIGMOBILE. In: international Workshop on Systems and Networking Support For Healthcare and Assisted Living Environments (HealthNet 2007), San Juan, Puerto Rico, pp. 19–24 (2007)
Tachtatzis, C., Graham, B., Tracey, D., Timmons, N.F., Morrison, J.: On-body to on-body channel characterization. In: Proceedings of 2011 IEEE Sensors Conference, Limerick, Ireland, pp. 908–911 (2011)
Ullah, S., Shen, B., Riazul, I.S., Khan, P., Saleem, S., Kwak, K.: A study of MAC protocols for WBANs. Sensors 10, 128–145 (2010)
Seo, S.H., Gopalan, S.A., Chun, S.M., Seok, K.J., Nah, J.W., Park, J.T.: An energy-efficient configuration management for multi-hop wireless body area networks. In: Proceedings of 3rd IEEE International Conference on Broadband Network and Multimedia Technology, Beijing, China, pp. 1235–1239 (2010)
Latré, B., De Poorter, E., Moerman, I., Demeester, P.: MOFBAN: a lightweight modular framework for body area networks. In: Kuo, T.W., Sha, E., Guo, M., Yang, L.T., Shao, Z. (eds.) EUC 2007. LNCS, vol. 4808, pp. 610–622. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77092-3_53
Yang, J.Y., Wang, J.S., Chen, Y.P.: Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifier. Pattern Recognit. Lett. 29, 2213–2220 (2008)
Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., Jamalipour, A.: Wireless body area networks: a survey. IEEE Trans. 6(3) (2014). third quarter
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shilpa, B., Hiremath, S.G., Thippeswamy, G. (2018). Neural Network Based Characterization and Reliable Routing of Data in Wireless Body Sensor Networks. In: Nagabhushan, T., Aradhya, V.N.M., Jagadeesh, P., Shukla, S., M.L., C. (eds) Cognitive Computing and Information Processing. CCIP 2017. Communications in Computer and Information Science, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-10-9059-2_22
Download citation
DOI: https://doi.org/10.1007/978-981-10-9059-2_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-9058-5
Online ISBN: 978-981-10-9059-2
eBook Packages: Computer ScienceComputer Science (R0)