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Smartphone Based Real-Time Health Monitoring and Intervention

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Handbook of Large-Scale Distributed Computing in Smart Healthcare

Part of the book series: Scalable Computing and Communications ((SCC))

Abstract

Smartphones are often dubbed as “a doctor in your pocket” as they have in recent years become one of the most notable platforms for health management and monitoring. In this chapter we discuss the potentials for real-time health monitoring of chronic health conditions and data-driven intervention that aim to improve patient care at a lower cost. We outline several challenges that developers, patients, and providers face with respect to this new technology. We then review several commercial platforms for health monitoring and discuss their pros and cons. Furthermore, we present Berkeley Telemonitoring Framework, a recently developed Andorid-based open source solution for development of health-monitoring applications with security and privacy in mind. In particular, our framework offers an easy-to-use API for building client apps, deploying data-hosting servers, fault-tolerant data retrieval and storage, access to event-based Bluetooth and BLE stacks with standards for personal health devices, access to phone sensors, implementation of several vital signs estimators, gait analysis, etc. We demonstrate the use of the framework on an example fitness application MarathonCoach. We further discuss several challenges facing real-time telemonitoring. In particular, we focus on privacy and propose a novel information-theoretic framework called Private Disclosure of Information (PDI). The PDI framework formalizes a scheme for encoding the collected health data in a manner that minimizes the ability of an adversary from gaining knowledge about the patient’s diagnosis (or other information private by implication) through statistical inference, while allowing the authorized provider to use this information with no loss in utility.

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Notes

  1. 1.

    http://www.nike.com/us/en_us/c/nike-plus.

  2. 2.

    http://www.fitbit.com/.

  3. 3.

    Samsung renamed “S Health” to “Samsung Health” in April 2017.

  4. 4.

    https://www.samsungknox.com.

  5. 5.

    https://www.ucsf.edu/news/2014/02/111976/samsung-ucsf-partner-accelerate-new-innovations-preventive-health-technology.

  6. 6.

    https://www.artik.cloud/.

  7. 7.

    https://www.developers.google.com/fit/.

  8. 8.

    https://www.verily.com/.

  9. 9.

    https://www.deepmind.com/applied/deepmind-health/.

  10. 10.

    https://www.underarmour.com/en-us/ua-record.

  11. 11.

    https://www.fitbit.com/.

  12. 12.

    https://www.pebble.com/.

  13. 13.

    https://www.nike.com/us/en_us/c/nike-plus.

  14. 14.

    https://www.jawbone.com/.

  15. 15.

    https://www.telemonitoring.berkeley.edu.

  16. 16.

    https://www.clinicaltrials.gov/ct2/show/NCT02799407.

  17. 17.

    We say that \(g : D_2 \rightarrow D_1\) is a left inverse of a function \(f : D_1 \rightarrow D_2\) if for all \(x \in D_1\) we have \(g\left( f\left( x\right) \right) = x\).

  18. 18.

    BMI is a measure of relative weight based on an individual’s mass and height. Defined as \(BMI \triangleq \frac{mass(kg)}{height(m)^2}\).

  19. 19.

    https://www.n.cdc.gov/nchs/nhanes/search/nhanes11_12.aspx.

  20. 20.

    https://wwwn.cdc.gov/nchs/nhanes/2011-2012/BMX_G.htm.

  21. 21.

    The adopted total accuracy measure is trace(M) / N where M is the confusion matrix and N is the cardinality of the test set. This is the percentage of true classifications over the test set.

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Acknowledgements

Many people have directly and indirectly helped in preparing the manuscript of this chapter, both their support and feedback are greatly appreciated. The authors would like to thank David M. Liebovitz, MD for his continued support in telemonitoring and mHealth research. The Berkeley Telemonitoring Project would not have been possible without the dedication of every member of its team—their contributions are greatly appreciated and valued. Many thanks go to Katherine Driggs-Campbell for the initial conversation that spurred the idea of Private Disclosure of Information (PDI). The authors are also greatly indebted to Yusuf Erol, Michael Carl Tschantz, Arash Nourian and John F. Canny for their fruitful discussions and feedback that significantly improved the quality of the work on PDI. This work was supported in part by TRUST, Team for Research in Ubiquitous Secure Technology, which receives funding support for the National Science Foundation (NSF award number CCF-0424422). The publication of this chapter was made possible by Grant Number HHS 90TR0003/01. Any opinions, findings, conclusions, views or recommendations expressed in this chapter are those of the authors and do not necessarily reflect the views of the National Science Foundation or the official views of the United States Department of Health and Human Services.

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  1. University of California, Berkeley, USA

    Daniel Aranki, Gregorij Kurillo & Ruzena Bajcsy

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  1. Department of Electrical and Computer Engineering, North Dakota State University , Fargo, North Dakota, USA

    Samee U. Khan

  2. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  3. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Assad Abbas

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Aranki, D., Kurillo, G., Bajcsy, R. (2017). Smartphone Based Real-Time Health Monitoring and Intervention. In: Khan, S., Zomaya, A., Abbas, A. (eds) Handbook of Large-Scale Distributed Computing in Smart Healthcare. Scalable Computing and Communications. Springer, Cham. https://doi.org/10.1007/978-3-319-58280-1_18

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