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Big Data and Signal Processing in mHealth

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m_Health Current and Future Applications

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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.

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References

  1. Istepanian, R., Laxminarayan, S., Pattichis, C.S. (eds.): Emerging Mobile Health Systems. Springer US (2006)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  4. United Nations: World Population Ageing. Technical report, Department of Economic and Social A airs, Population Division, 2015. ST/ESA/SER.A/390

    Google Scholar 

  5. Mukhopadhyay, S.C.: Wearable sensors for human activity monitoring: a review. IEEE Sens. J. 15(3), 1321–1330 (2015)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  8. Deyell, M.W., Krahn, A.D., Goldberger, J.J.: Sudden cardiac death risk stratification. Circ. Res. 116(12),1907–1918 (2015)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  16. Nordin E, Lindelöf N, Rosendahl E, Jensen J, Lundin-Olsson L.: Prognostic validity of the Timed Up-and-Go test, a modified Get-Up-and-Go test, staff’s global judgement and fall history in evaluating fall risk in residential care facilities. Pubmed ref: https://www.ncbi.nlm.nih.gov/pubmed/18515291

  17. World Health Organization (ed.): WHO global report on falls prevention in older age. World Health Organization (2008)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  24. Rivolta, M.W., Aktaruzzaman, Md., Rizzo, G., Lafortuna, C., Ferrarin, M., Bovi, G., Bonardi, D. R., Sassi, R.: Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis. Pubmed ref: https://www.ncbi.nlm.nih.gov/pubmed/30195985

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  33. Moe-Nilssen, R., Helbostad, J.L.: Estimation of gait cycle characteristics by trunk accelerometry. J. Biomech. 37(1), 121–126 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Milan, Via Celoria 18, 20133, Milan, Italy

    Massimo W. Rivolta & Roberto Sassi

Authors
  1. Massimo W. Rivolta
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  2. Roberto Sassi
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Corresponding author

Correspondence to Massimo W. Rivolta .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giuseppe Andreoni

  2. Politecnico di Milano, Milan, Italy

    Paolo Perego

  3. Cefriel Scarl, Politecnico di Milano, Milan, Italy

    Enrico Frumento

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Rivolta, M.W., Sassi, R. (2019). Big Data and Signal Processing in mHealth. In: Andreoni, G., Perego, P., Frumento, E. (eds) m_Health Current and Future Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-02182-5_7

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02181-8

  • Online ISBN: 978-3-030-02182-5

  • eBook Packages: EngineeringEngineering (R0)

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