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Introduction to Large-Scale Distributed Computing in Smart Healthcare Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

Authors : Harishchandra Dubey, Admir Monteiro, Nicholas Constant, Mohammadreza Abtahi, Debanjan Borthakur, Leslie Mahler, Yan Sun, Qing Yang, Umer Akbar, Kunal Mankodiya

Published in: Handbook of Large-Scale Distributed Computing in Smart Healthcare

Publisher: Springer International Publishing

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Abstract

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one’s health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: (1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex; (2) The data, when communicated, are vulnerable to security and privacy issues; (3) The communication of the continuously collected data is not only costly but also energy hungry; (4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the overall performance of the system in terms of computing intelligence, transmission, storage, configurable, and security. The case studies on various types of physiological data shows that the proposed Fog architecture could be used for signal enhancement, processing and analysis of various types of bio-signals.

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previous chapter Internet of Things Based E-health Systems: Ideas, Expectations and Concerns
next chapter Technologies and Practices: Mobile Healthcare Based on Medical Image Cloud and Big Data (in China)
Literature
1.
Alzand, B.S., Crijns, H.J.: Diagnostic criteria of broad QRS complex tachycardia: decades of evolution. Europace 13(4), 465–472 (2011)
2.
Asgari, M., Shafran, I., Bayestehtashk, A.: Robust detection of voiced segments in samples of everyday conversations using unsupervised hmms. In: IEEE Spoken Language Technology Workshop (2012)
3.
Banse, R., Scherer, K.R.: Acoustic profiles in vocal emotion expression. Journal of personality and social psychology 70(3), 614 (1996)
4.
Barik, R.K., Dubey, H., Samaddar, A.B., Gupta, R.D., Ray, P.K.: FogGIS: Fog Computing for Geospatial Big Data Analytics. In: 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering, India (2016)
5.
Boersma, P.: Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In: Proceedings of the institute of phonetic sciences. vol. 17, pp. 97–110. Amsterdam (1993)
6.
Brusco, M., Nazeran, H.: Development of an intelligent pda-based wearable digital phonocardiograph. In: Proceedings of the 27th IEEE Annual Conference on Engineering in Medicine and Biology. vol. 4, pp. 3506–3509 (2005)
7.
Cisco: White paper published by cisco. fog computing and the internet of things: Extend the cloud to where the things are. (2015)
8.
Cohen, I.: Noise spectrum estimation in adverse environments: Improved minima controlled recursive averaging. IEEE Transactions on audio, speech and language processing 11(5), 466–475 (2003)
9.
Constant, N., Borthakur, D., Abtahi, M., Dubey, H., Mankodiya, K.: Fog-Assisted wIoT: A Smart Fog Gateway for End-to-End Analytics in Wearable Internet of Things. In: The 23rd IEEE Symposium on High Performance Computer Architecture HPCA, Austin, Texas, USA (2017)
10.
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proceedings of the VLDB Endowment 1(2), 1542–1552 (2008)
11.
Dubey, H., Mehl, M.R., Mankodiya, K.: BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-Based Acoustic Big Data. In: IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington DC, USA (June 2016)
12.
Dubey, H., Golberg, C., Abtahi, M., Mahler, L., Makodiya, K.: EchoWear: Smartwatch Technology for Voice and Speech Treatments of Patients with Parkinson’s Disease. In: Proceedings of the Wireless Health 2015, National Institutes of Health, Baltimore, MD, USA. ACM (2015)
13.
Dubey, H., Goldberg, J.C., Makodiya, K., Mahler, L.: A multi-smartwatch system for assessing speech characteristics of people with dysarthria in group settings. In: Proceedings IEEE 17th International Conference on e-Health Networking, Applications and Services (Healthcom), Boston, USA (2015)
14.
Dubey, H., Kumaresan, R., Mankodiya, K.: Harmonic sum-based method for heart rate estimation using PPG signals affected with motion artifacts. “Journal of Ambient Intelligence and Humanized Computing” pp. 1–14 (2016), doi:10.​1007/​s12652-016-0422-z
15.
Dubey, H., Yang, J., Constant, N., Amiri, A., Yang, Q., Makodiya, K.: Fog Data: Enhancing Telehealth Big Data Through Fog Computing. In: Proceedings of The Fifth ASE International Conference on BigData, Kaohsiung, Taiwan. ACM (2015)
16.
17.
Fastl, H., Zwicker, E.: Psychoacoustics: Facts and models, vol. 22. Springer Science & Business Media (2007)
18.
Gamboa, J., Jiménez-Jiménez, F.J., Nieto, A., Montojo, J., Ortí-Pareja, M., Molina, J.A., García-Albea, E., Cobeta, I.: Acoustic voice analysis in patients with parkinson’s disease treated with dopaminergic drugs. Journal of Voice 11(3), 314–320 (1997)
19.
Gavrila, D., Davis, L., et al.: Towards 3-d model-based tracking and recognition of human movement: a multi-view approach. In: International workshop on automatic face-and gesture-recognition. pp. 272–277 (1995)
20.
Geddes, L.: Birth of the stethoscope. IEEE Engineering in Medicine and Biology Magazine 24(1), 84–86 (2005)
21.
22.
23.
Gonzalez, S., Brookes, M.: Pefac-a pitch estimation algorithm robust to high levels of noise. IEEE Transactions on Audio, Speech, and Language Processing 22(2), 518–530 (2014)
24.
J Holmes, R., M Oates, J., J Phyland, D., J Hughes, A.: Voice characteristics in the progression of parkinson’s disease. International Journal of Language & Communication Disorders 35(3), 407–418 (2000)
25.
26.
Johnston, J.D.: Transform coding of audio signals using perceptual noise criteria. IEEE Journal on Selected Areas in Communications 6(2), 314–323 (1988)
27.
28.
29.
Kaiser, J.F.: Some useful properties of teager’s energy operators. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (1993)
30.
Kayyali, B., Knott, D., Van Kuiken, S.: The big-data revolution in us health care: Accelerating value and innovation. Mc Kinsey & Company (2013)
31.
Kent, R.D., Weismer, G., Kent, J.F., Vorperian, H.K., Duffy, J.R.: Acoustic studies of dysarthric speech: Methods, progress, and potential. Journal of communication disorders 32(3), 141–186 (1999)
32.
Kovacs-Vajna, Z.M.: A fingerprint verification system based on triangular matching and dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1266–1276 (2000)
33.
Kvedalen, E.: Signal processing using the teager energy operator and other nonlinear operators. Master, University of Oslo Department of Informatics 21 (2003)
34.
Lansford, K.L., Liss, J.M.: Vowel acoustics in dysarthria: Speech disorder diagnosis and classification. Journal of Speech, Language, and Hearing Research 57(1), 57–67 (2014)
35.
Li, F., Gao, Y., Cao, Y., Iravani, R.: Improved teager energy operator and improved chirp-z transform for parameter estimation of voltage flicker. IEEE Transactions on Power Delivery 31(1), 245–253 (2016)
36.
Mahler, L., Dubey, H., Goldberg, C., Mankodiya, K.: Use of smartwatch technology for people with dysarthria. In: Motor Speech Conference at. Madonna Rehabilitation Hospital, Newport Beach, CA, USA. (2016)
37.
Martínez-Sánchez, F., Meilán, J., Carro, J., Gómez, Í.C., Millian-Morell, L., Pujante, V.I., López-Alburquerque, T., López, D.: Speech rate in parkinson’s disease: A controlled study. Neurologia (Barcelona, Spain) (2015)
38.
Monteiro, A., Dubey, H., Mahler, L., Yang, Q., Mankodiya, K.: FIT: A Fog Computing Device for Speech TeleTreatments. 2nd IEEE International Conference on Smart Computing (SMARTCOMP), Missouri, USA (2016)
39.
Myers, C., Rabiner, L., Rosenberg, A.: Performance tradeoffs in dynamic time warping algorithms for isolated word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 28(6), 623–635 (1980)
40.
41.
42.
Orfanidis, S.J.: Introduction to signal processing. Prentice-Hall, Inc. (1995)
43.
Paliwal, K.K.: Spectral subband centroid features for speech recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (1998)
44.
Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE transactions on biomedical engineering (3), 230–236 (1985)
45.
Panahiazar, M., Taslimitehrani, V., Jadhav, A., Pathak, J.: Empowering personalized medicine with big data and semantic web technology: Promises, challenges, and use cases. In: IEEE International Conference on Big Data. pp. 790–795 (2014)
46.
47.
Reed, T.R., Reed, N.E., Fritzson, P.: Heart sound analysis for symptom detection and computer-aided diagnosis. Simulation Modelling Practice and Theory 12(2), 129–146 (2004)
48.
Sapir, S., Ramig, L., Fox, C.: Speech and swallowing disorders in parkinson disease. Current opinion in otolaryngology & head and neck surgery 16(3), 205–210 (2008)
49.
Sobell, M.G.: A Practical Guide to Fedora and Red Hat Enterprise Linux. Pearson Education (2013)
50.
Spielman, J., Mahler, L., Halpern, A., Gilley, P., Klepitskaya, O., Ramig, L.: Intensive voice treatment (lsvt® loud) for parkinson’s disease following deep brain stimulation of the subthalamic nucleus. Journal of communication disorders 44(6), 688–700 (2011)
51.
Sun, X.: A pitch determination algorithm based on subharmonic-to-harmonic ratio (2000)
52.
Tan, Z.H., Lindberg, B.: Low-complexity variable frame rate analysis for speech recognition and voice activity detection. IEEE Journal of Selected Topics in Signal Processing 4(5), 798–807 (2010)
53.
Tsanas, A.: Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine learning. Ph.D. thesis, University of Oxford (2012)
54.
Varghees, V.N., Ramachandran, K.: A novel heart sound activity detection framework for automated heart sound analysis. Biomedical Signal Processing and Control 13, 174–188 (2014)
55.
Yang, Y.H., Lin, Y.C., Su, Y.F., Chen, H.H.: A regression approach to music emotion recognition. IEEE Transactions on Audio, Speech, and Language Processing 16(2), 448–457 (2008)
56.
Zwicker, E., Fastl, H.: Psychoacoustics: Facts and models, vol. 22. Springer Science & Business Media (2013)
Metadata
Title
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
Authors
Harishchandra Dubey
Admir Monteiro
Nicholas Constant
Mohammadreza Abtahi
Debanjan Borthakur
Leslie Mahler
Yan Sun
Qing Yang
Umer Akbar
Kunal Mankodiya
Copyright Year
2017
Book
Handbook of Large-Scale Distributed Computing in Smart Healthcare

Print ISBN: 978-3-319-58279-5

Electronic ISBN: 978-3-319-58280-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-58280-1_11

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