Abstract
This paper presents an Intelligent wearable sensor band for underground working people. The security and soundness of laborers are significant for underground individuals. The proposed framework consolidates wearable sensors to quantify physiological and natural parameters. A passage is acquainted with giving information preparing, a neighborhood web server, and a cloud association. A wearable sensor on a laborer and natural sensor on a wanderer that can transmit the information to the client by means of a door for example server, gives offer notice and cautioning component for the clients. Live health examination taken for laborers who work in an underground like Tunnels, Shafts, etc., it has an Individual database of laborers and contrasts it and current essential tangible qualities separate to workplace information. Live update, will screen from the control room and it can direct the specialist if any medical problem occurs and furthermore can maintain a strategic distance from the undesirable passing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Selkar, R.G., Thakare, M.: Brain tumor detection and segmentation by using thresholding and watershed algorithm. Int. J. Adv. Inf. Commun. Technol. 1, 321–324 (2014)
Alam, M.S., Rahman, M.M., Hossai, M.A.: Automatic human brain tumor detection in MRI image using template-based K means and improved fuzzy C means clustering algorithm. Big Data Cogn. Comput. 3(27) (2019)
Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8, 275–283 (2004)
Devkotaa, B., Alsadoona, A., Prasada, P.W.C., Singhb, A.K., Elchouemic, A.: Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction. Procedia Comp. Sci. 125, 115–123 (2018)
Mathur, N., Meena, Y.K., Mathur, S., Mathur, D.: Detection of Brain Tumor in MRI Image Through Fuzzy-Based Approach in High-Resolution Neuroimaging-Basic Physical Principles and Clinical Applications. Rijeka, Croatia, InTech (2018)
Arzoo, M., Prof, A., Rathod, K.: K-means algorithm with different distance metrics in spatial data mining with uses of NetBeans IDE 8.2. Int. Res. J. Eng. Technol. 4, 2363–2368 (2017)
Karthik, R., Menaka, R.: A multi-scale approach for detection of ischemic stroke from brain MR images using discrete curvelet transformation. Measurement 100, 223–232 (2017)
Wu, Y., Zhu, M., Li, D., Zhang, Y., Wang, Y.: Brain stroke localization by using microwave-based signal classification. In: IEEE International Conference on Electromagnetics in Advanced Applications, pp. 828–831 (2016)
Ay, H., Furie, K.L., Singhal, A., Smith, W.S., Sorensen, A.G., Koroshetz, W.J.: An evidence-based causative classification system for acute ischemic stroke. Ann. Neurol. 58(5), 688–697 (2005)
Database, Brainweb: http://www.bic.mni.mcgill.ca/brainweb/. Accessed 10 Sept 2018
Ramesh, G.P., Aravind, C.V., Rajparthiban, R., Soysa, N.: Body area network through wireless technology. Int. J. Comput. Sci. Eng. Commun. 2(1), 129–134 (2014)
Nithya, V., Ramesh, G.P.: Wireless EAR EEG signal analysis with stationary wavelet transform for co channel interference in schizophrenia diagnosis. In: Balas, V., Kumar, R., Srivastava, R. (eds.) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol 172. Springer (2020)
Fiebach, J.B., Schellinger, P.D., Geletneky, K., Wilde, P., Meyer, M., Hacke, W., Sartor, K.: MRI in acute subarachnoid haemorrhage; findings with a standardised stroke protocol. Neuroradiology 46(1), 44–48 (2004)
Fiebach, J.B., Schellinger, P.D., Jansen, O., Meyer, M., Wilde, P., Bender, J., Hähnel, S.: CT and diffusion-weighted MR imaging in randomized order. Stroke 33(9), 2206–2210 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Karthikeyan, S. et al. (2021). Intelligent Wearable Sensor Band for Underground Working People. In: Balas, V.E., Solanki, V.K., Kumar, R. (eds) Further Advances in Internet of Things in Biomedical and Cyber Physical Systems. Intelligent Systems Reference Library, vol 193. Springer, Cham. https://doi.org/10.1007/978-3-030-57835-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-57835-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57834-3
Online ISBN: 978-3-030-57835-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)