Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Introduction Read chapter Read first chapter

2019 | OriginalPaper | Chapter

7. Big Data and Signal Processing in mHealth

Authors : Massimo W. Rivolta, Roberto Sassi

Published in: m_Health Current and Future Applications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this chapter, we present and discuss the state-of-the-art technology for the use of mHealth as a relevant source of clinical information. Then, we provide an overview of the signal processing pipelines that, up to date, are most suitable for the processing of data collected from sensors in unsupervised environments, as at home.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Device for mHealth
next chapter mHealth Services: Examples and Future Perspectives
Literature
1.
Istepanian, R., Laxminarayan, S., Pattichis, C.S. (eds.): Emerging Mobile Health Systems. Springer US (2006)
2.
Ekeland, A.G., Bowes, A., Flottorp, S.: Effectiveness of telemedicine: A systematic review of reviews. Int. J. Med. Inform. 79(11), 736–771 (2010)CrossRef
3.
Silva, B.M., Rodrigues, J.J., de la Torre Díez, I., López-Coronado, M., Saleem, K.: Mobile-health: a review of current state in 2015. J. Biomed. Inform. 56, 265–272 (2015)CrossRef
4.
United Nations: World Population Ageing. Technical report, Department of Economic and Social A airs, Population Division, 2015. ST/ESA/SER.A/390
5.
Mukhopadhyay, S.C.: Wearable sensors for human activity monitoring: a review. IEEE Sens. J. 15(3), 1321–1330 (2015)CrossRef
6.
Mendis, S., Puska, P., Norrving, B., World Health Organization, World Heart Federation, and World Stroke Organization (eds.): Global Atlas on Cardiovascular Disease Prevention and Control. World Health Organization in collaboration with the World Heart Federation and the World Stroke Organization, Geneva (2011)
7.
NCD Risk Factor Collaboration (NCD-RisC): Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19 1 million participants. Lancet 389(10064), 37–55 (2017)
8.
Deyell, M.W., Krahn, A.D., Goldberger, J.J.: Sudden cardiac death risk stratification. Circ. Res. 116(12),1907–1918 (2015)CrossRef
9.
Borne, R.T., Katz, D., Betz, J., Peterson, P.N., Masoudi, F.A.: Implantable cardioverter-defibrillators for secondary prevention of sudden cardiac death: a review. J. Am. Heart Assoc. 6, e005515 (2017)
10.
Martinez-Perez, B., de la Torre-Diez, I., Lopez-Coronado, M., Herreros-Gonzalez, J.: Mobile Apps in Cardiology: review. JMIR mhealth and uhealth 1(2), e15 (2013)CrossRef
11.
Gambi, E., Agostinelli, A., Belli, A., Burattini, L., Cippitelli, E., Fioretti, S., Pierleoni, P., Ricciuti, M., Sbrollini, A., Spinsante, S.: Heart rate detection using microsoft kinect: validation and comparison to wearable devices. Sensors 17(8), 1776 (2017)CrossRef
12.
Iozzia, L., Cerina, L., Mainardi, L.: Relationships between heart-rate variability and pulse-rate variability obtained from video-PPG signal using ZCA. Physiol. Meas. 37(11), 1934–1944 (2016)CrossRef
13.
Siddiqui, S.A., Zhang, Y., Feng, Z., Kos, A.: A pulse rate estimation algorithm using PPG and smartphone camera. J. Med. Syst. 40(5), 126 (2016)CrossRef
14.
Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology: Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 93(5), 1043–1065 (1996)CrossRef
15.
Sharma, M., Barbosa, K., Ho, V., Griggs, D., Ghirmai, T., Krishnan, S., Hsiai, T., Chiao, J.-C., Cao, H.: Cu-less and continuous blood pressure monitoring: a methodological review. Technologies 5(2), 21 (2017)CrossRef
16.
17.
World Health Organization (ed.): WHO global report on falls prevention in older age. World Health Organization (2008)
18.
Wren, T.A.L., Gorton, G. E., Ounpuu, S., Tucker, C. A.: Efficacy of clinical gait analysis: a systematic review. Gait Posture 34(2), 149–153 (2011)CrossRef
19.
Giansanti, D., Maccioni, G., Cesinaro, S., Benvenuti, F., Macellari, V.: Assessment of fallrisk by means of a neural network based on parameters assessed by a wearable device during posturography. Med. Eng. Phys. 30(3), 367–372 (2008)CrossRef
20.
O’Sullivan, M., Blake, C., Cunningham, C., Boyle, G., Finucane, C.: Correlation of accelerometry with clinical balance tests in older fallers and non-fallers. Age Ageing 38(3), 308–313 (2009)CrossRef
21.
Senden, R., Savelberg, H.H.C.M., Grimm, B., Heyligers, I.C., Meijer, K.: Accelerometry based gait analysis, an additional objective approach to screen subjects at risk for falling. Gait Posture 36(2), 296–300 (2012)CrossRef
22.
Narayanan, M.R., Redmond, S.J., Scalzi, M.E., Lord, S.R., Celler, B.G., Lovell, N.H.: Longitudinal falls-risk estimation using triaxial accelerometry. IEEE Trans. Biomed. Eng. 57(3), 534–541 (2010)CrossRef
23.
Tinetti, M.E., Williams, T.F., Mayewski, R.: Fall risk index for elderly patients based on number of chronic disabilities. Am. J. Med. 80, 429–434 (1986)CrossRef
24.
25.
Iber, C., Ancoli-Israel, S., Chesson, A. L., Quan, S. F., et al.: The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Technical Specifications, 1st edn. American Academy of Sleep Medicine, Westcherster, IL, U.S.A. (2007)
26.
Kripke, D.F., Mullaney, D.J., Messin, S., Wyborney, V.G.: Wrist actigraphic measures of sleep and rhythms. Electroencephalogr. Clin. Neurophysiol. 44(5), 674–676 (1978)CrossRef
27.
Ancoli-Israel, S., Cole, R., Alessi, C., Chambers, M., Moorcroft, W., Pollak, C.P.: The role of actigraphy in the study of sleep and circadian rhythms. Sleep 26(3), 342–392 (2003)CrossRef
28.
Redmond, S.J., de Chazal, P., O’Brien, C., Ryan, S., McNicholas, W.T., Heneghan, C.: Sleep staging using cardiorespiratory signals. Somnologie 11(4), 245–256 (2007)CrossRef
29.
Aktaruzzaman, Md, Migliorini, M., Tenhunen, M., Himanen, S.L., Bianchi, A.M., Sassi, R.: The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability. Med. Biol. Eng. Comput. 53(5), 415–425 (2015)CrossRef
30.
Aktaruzzaman, Md., Rivolta, M.W., Karmacharya, R., Scarabottolo, N., Pugnetti, L., Garegnani, M., Bovi, G., Ferrarin, M., Sassi, R.: Performance comparison between wrist and chest actigraphy in combination with heart rate variability for sleep classification. Comput. Biol. Med. (2017)
31.
Sassi, R., Cerutti, S., Lombardi, F., Malik, M., Huikuri, H.V., Peng, C.K., Schmidt, G., Yamamoto, Y.: Advances in heart rate variability signal analysis: joint position statement by the e-cardiology ESC working group and the European heart rhythm association co-endorsed by the Asia Pacific heart rhythm society. Europace 17(9), 1341–1353 (2015)CrossRef
32.
Peng, R.-C., Zhou, X.-L., Lin, W.-H., Zhang, Y.-T.: Extraction of heart rate variability from smartphone photoplethysmograms. Comput. Math. Methods Med. 2015, 1–11 (2015)MathSciNetCrossRef
33.
Moe-Nilssen, R., Helbostad, J.L.: Estimation of gait cycle characteristics by trunk accelerometry. J. Biomech. 37(1), 121–126 (2004)CrossRef
34.
Johnson, N.L., Kirchner, H.L., Rosen, C.L., Storfer-Isser, A., Cartar, L.N., Ancoli-Israel, S., Emancipator, J.L., Kibler, A.M., Redline, S.: Sleep estimation using wrist actigraphy in adolescents with and without sleep disordered breathing: a comparison of three data modes. Sleep 30(7), 899–905 (2007)CrossRef
35.
Cole, R.J., Kripke, D.F., Gruen, W., Mullaney, D.J., Gillin, J.C.: Automatic sleep/wake identification from wrist activity. Sleep 15(5), 461–469 (1992)CrossRef
36.
Ong, A.A., Gillespie, M.B.: Overview of smartphone applications for sleep analysis. World J. Otorhinolaryngol. Head Neck Surg. 2(1), 45–49 (2016)CrossRef
Metadata
Title
Big Data and Signal Processing in mHealth
Authors
Massimo W. Rivolta
Roberto Sassi
Copyright Year
2019
Book
m_Health Current and Future Applications

Print ISBN: 978-3-030-02181-8

Electronic ISBN: 978-3-030-02182-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-02182-5_7