A Low-Cost Portable Health Platform for Monitoring of Human Physiological Signals

  • Keiran BrownEmail author
  • Emanuele Lindo Secco
  • Atulya Kumar Nagar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 532)


This work reports the integration and preliminary testing of a miniature commercial health platform based on the combination of a set of platforms that can be merged in hardware and software to measure and monitor many physiological parameters of the human body. The system is very portable, has a clear economic benefit in terms of cost and it has been well integrated with a customized and intuitive graphical user interface. Detailed about the materials used for preparation of this platform and the methods used for data collection are reported. Preliminary data has been collected and reported. Explanations are shown about the data in relation to the sensors behaviors and performance.


Wearable device Low-cost device Monitoring of physiological parameters Open source hardware and software 



We thank you all staff of the Department of Mathematics and Computer Science for their valuable support and in particular: Mr. M. Barrett-Baxendale and Prof. D. Reid, Ms. J. Burnett, and Miss S. Benson.

This work was presented in report form in fulfillment of the requirements for the course in Electronic Engineering for the student K. Brown under the supervision of E.L. Secco from the Robotics Laboratory, Department of Mathematics & Computer Science, Liverpool Hope University.


  1. 1.
    G. Magenes, D. Curone, E.L. Secco, A. Bonfiglio, The ProeTEX prototype: a wearable integrated system for physiological & environmental monitoring of emergency operators. 1st IEEE EMBS Unconference on Wearable & Ubiquitous Technology for Health & Wellness, Boston 2011Google Scholar
  2. 2.
    D. Curone, E.L. Secco, L. Caldani, A. Lanatà, R. Paradiso, A. Tognetti, G. Magenes, Assessment of sensing fire fighters uniforms for physiological parameter measurement in harsh environment. IEEE Trans. Inf. Technol. Biomed. 16(3), 501–511 (2012)CrossRefGoogle Scholar
  3. 3.
    Oxehealth, Technology - Oxehealth (Oxehealth, Oxford, 2017)Google Scholar
  4. 4.
    H. Fan, L. Xaiobin, Z. Qian, Ehealthguard: A Personal Mobile Health Monitoring Platform (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), Brussels, 2011), pp. 122–123Google Scholar
  5. 5.
    K.M. Diaz, D.J. Krupka, M.J. Chang, J. Peacock, Y. Ma, J. Goldsmith, J.E. Schwartz, K.W. Davidson, Fitbit®: An accurate and reliable device for wireless physical activity tracking. Int. J. Cardiol. 185, 138–140 (2015)CrossRefGoogle Scholar
  6. 6.
    FitBit Inc Corp. website,
  7. 7.
    A. Pantelopoulos, N.G. Bourbakis, A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern C Appl Rev 40(1), 1–12 (2010)CrossRefGoogle Scholar
  8. 8.
    R.M.D. Omer, N.K. Al-Salihi, HealthMate: Smart wearable system for health monitoring (SWSHM), in 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), Calabria, Italy, (IEEE, New York, 2017), pp. 755–760CrossRefGoogle Scholar
  9. 9.
    C. Otto, A. Milenkovic, C. Sanders, E. Jovanov, System architecture of a wireless body area sensor network for ubiquitous health monitoring. J. Mob. Multimed. 1(4), 307–326 (2006)Google Scholar
  10. 10.
    Cooking Hacks website,
  11. 11.
    Spacelabs Cardiocall VS20 DualMode ECG Event Recorder,
  12. 12.
    Y.M. Garcia, Glucose Meter Fundamentals And Design, 1st edn. (Freescale Semiconductor, Austin, 2017)Google Scholar
  13. 13.
    Atmel Corp. website,
  14. 14.
  15. 15.
    C. Reas, F. Ben, Processing: A Programming Handbook for Visual Designers and Artists, 2nd edn. (MIT press, Cambridge, 2017)Google Scholar
  16. 16.
    G. Bateman, Processing/processing GitHub (2017),
  17. 17.
    E.L. Secco, D. Curone, A. Tognetti, A. Bonfiglio, G. Magenes, Validation of smart garments for physiological and activity-related monitoring of humans in harsh environment. Am. J. Biomed. Eng. 2(4), 189–196 (2012)CrossRefGoogle Scholar
  18. 18.
    D. Curone, E.L. Secco, A. Tognetti, G. Magenes, An activity classifier based on heart rate and accelerometer data fusion, in 7th International Workshop on Biosignal Interpretation, (Politecnico di Milano, Italy, 2012), pp. 169–172Google Scholar
  19. 19.
    A.S. Mahajan, R.Y. Pattar, R. Khamitkar, M. Akalawadi, K. Bhat, Doctor’s innovative clinic: An application of artificial intelligence and physiological sensors, in 2016 International Conference on Advances in Human Machine Interaction (HMI), Doddaballapur, (IEEE, New York, 2016), pp. 1–5Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Keiran Brown
    • 1
    Email author
  • Emanuele Lindo Secco
    • 1
  • Atulya Kumar Nagar
    • 1
  1. 1.Robotics Laboratory, Department of Mathematics and Computer ScienceLiverpool Hope UniversityLiverpoolUK

Personalised recommendations