Abstract
One of the applications of big data research is to utilize inexpensive and unobtrusive Internet of Things- (IoT) driven devices for monitoring hospitalized patients whose physiological status requires close attention. This type of solution employs sensors to collect physiological information and uses gateways to send the data or warnings to caregivers for further analysis. Unfortunately, real-world applications of health monitoring for mobile users were so far poor mainly due to the energy constraints imposed by the batteries. Edge computing aims to process data produced by devices to be closer to its origin instead of sending it to data centers.
This chapter presents a ZigBee-based gastrointestinal track motility monitor (GTMM), an IoT-driven eHealth device that is specifically designed for constant monitoring of hospitalized patients after major abdominal surgery. GTMM after abdominal surgery is required for preventing unexpected postoperational complications such as intestinal obstruction.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
F.M. Al-Turjman, Towards smart ehealth in the ultra large-scale Internet of Things era, in 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), (2016), pp. 102–105
U.D. Ulusar, F. Al-Turjman, G. Celik, An overview of Internet of things and wireless communications, in 2017 International Conference on Computer Science and Engineering (UBMK), (2017), pp. 506–509
Y. Tang, G. Cao, H. Li, K. Zhu, The design of electronic heart sound stethoscope based on bluetooth, in 2010 4th International Conference on Bioinformatics and Biomedical Engineering, (2010), pp. 1–4
M.M. Rach, H.M. Gomis, O.L. Granado, M.P. Malumbres, A.M. Campoy, J.J.S. Martín, On the design of a bioacoustic sensor for the early detection of the red palm weevil. Sensors 13(2), 1706–1729 (2013)
D.D.K. Patil, R.K. Shastri, Design of wireless electronic stethoscope based on zigbee. Int. J. Distrib. Parallel Syst. 3(1), 351–359 (2012)
L.J. Hadjileontiadis, I.T. Rekanos, Detection of explosive lung and bowel sounds by means of fractal dimension. IEEE Signal Process. Lett. 10(10), 311–314 (2003)
L.J. Hadjileontiadis, C.N. Liatsos, C.C. Mavrogiannis, T.A. Rokkas, S.M. Panas, Enhancement of bowel sounds by wavelet-based filtering. I.E.E.E. Trans. Biomed. Eng. 47(7), 876–886 (2000)
K.-S. Kim, J.-H. Seo, C.-G. Song, Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds. Biomed. Eng. Online 10, 69 (2011)
C. Dimoulas, G. Kalliris, G. Papanikolaou, A. Kalampakas, Long-term signal detection, segmentation and summarization using wavelets and fractal dimension: A bioacoustics application in gastrointestinal-motility monitoring. Comput. Biol. Med. 37(4), 438–462 (2007)
K.S. Kim, J.H. Seo, S.H. Ryu, M.H. Kim, C.G. Song, Estimation algorithm of the bowel motility based on regression analysis of the jitter and shimmer of bowel sounds. Comput. Methods Prog. Biomed. 104(3), 426–434 (2011)
T. Emoto et al., ARMA-based spectral bandwidth for evaluation of bowel motility by the analysis of bowel sounds. Physiol. Meas. 34(8), 925 (2013)
L.J. Hadjileontiadis, Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding-part II: Application results. I.E.E.E. Trans. Biomed. Eng. 52(6), 1050–1064 (2005)
L.J. Hadjileontiadis, Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding-part I: Methodology. I.E.E.E. Trans. Biomed. Eng. 52(6), 1143–1148 (2005)
C. Dimoulas, G. Kalliris, G. Papanikolaou, A. Kalampakas, Novel wavelet domain Wiener filtering de-noising techniques: Application to bowel sounds captured by means of abdominal surface vibrations. Biomed. Signal Process. Control 1(3), 177–218 (2006)
N. Jatupaiboon, S. Pan-ngum, P. Israsena, Electronic stethoscope prototype with adaptive noise cancellation, in 2010 Eighth International Conference on ICT and Knowledge Engineering, (2010), pp. 32–36
Y. Jiao, R.Y.P. Cheung, W.W.Y. Chow, and M.P.C. Mok, A novel gradient adaptive step size LMS algorithm with dual adaptive filters, in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2013), pp. 4803–4806
U.D. Ulusar, Recovery of gastrointestinal tract motility detection using Naive Bayesian and minimum statistics. Comput. Biol. Med. 51, 223–228 (2014)
C. Dimoulas, G. Kalliris, G. Papanikolaou, V. Petridis, A. Kalampakas, Bowel-sound pattern analysis using wavelets and neural networks with application to long-term, unsupervised, gastrointestinal motility monitoring. Expert Syst. Appl. 34(1), 26–41 (2008)
R. Ranta, V. Louis-Dorr, C. Heinrich, D. Wolf, F. Guillemin, Digestive activity evaluation by multichannel abdominal sounds analysis. I.E.E.E. Trans. Biomed. Eng. 57(6), 1507–1519 (2010)
C.A. Dimoulas, G.V. Papanikolaou, V. Petridis, Pattern classification and audiovisual content management techniques using hybrid expert systems: A video-assisted bioacoustics application in abdominal sounds pattern analysis. Expert Syst. Appl. 38(10), 13082–13093 (2011)
N.J. Gordon, D.J. Salmond, A.F.M. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. F Radar Signal Process. 140(2), 107 (1993)
E. Frank, L. Trigg, G. Holmes, I.H. Witten, Technical note: Naive Bayes for regression. Mach. Learn. 41(1), 5–25 (2000)
Acknowledgment
This work is supported by Akdeniz University Scientific Research Projects Coordination Unit (FYL-2015-1043).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ulusar, U.D., Turk, E., Oztas, A.S., Savli, A.E., Ogunc, G., Canpolat, M. (2019). IoT and Edge Computing as a Tool for Bowel Activity Monitoring. In: Al-Turjman, F. (eds) Edge Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-99061-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-99061-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99060-6
Online ISBN: 978-3-319-99061-3
eBook Packages: EngineeringEngineering (R0)