Skip to main content
SpringerLink
Log in

Real-time running workouts monitoring using Cloud–Edge computing

  • S.I.: IoT-based Health Monitoring System
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In a world of ever-growing technology where smartwatches are becoming more and more widespread, we introduced an application that can attach a personal running coach on one’s wrist. We developed a highly scalable model that takes input from real coaches, conveys it into a running training on a watch, analyzes the running performance, and gives real-time text and haptic feedback based on it. Using cloud technologies, we came up with a solution that provides end-to-end connectivity between a smartwatch and a coach or user. We empower people to track down their workouts and physical profiles easily and to connect with their trainers using a standard, interactive dashboard. The solution is presented in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://www.statista.com/to1743/running-and-jogging/.

  2. https://www.verywellfit.com/best-running-apps-4165816.

  3. https://www.fitbit.com/us/technology/partnership.

  4. https://www.strava.com/subscribe.

  5. https://runkeeper.com.

  6. https://www.trainingpeaks.com/.

  7. Speed = minutes per kilometer.

  8. Infrastructure as a Service.

  9. Hypertext Transfer Protocol Secure.

  10. Secure Sockets Layer.

  11. https://dev.fitbit.com/build/guides/communications/.

  12. https://fetch.spec.whatwg.org/.

  13. https://html.spec.whatwg.org/dev/.

  14. Hypertext Markup Language.

  15. JavaScript Object Notation.

  16. https://dev.fitbit.com/build/reference/device-api/exercise/.

  17. https://dev.fitbit.com/build/reference/device-api/clock/.

  18. https://jerryscript.net/.

  19. https://dev.fitbit.com/build/reference/device-api/.

  20. https://reactjs.org/docs/introducing-jsx.html.

  21. https://dev.fitbit.com/blog/2019-10-29-announcing-fitbit-os-sdk-4.0/.

  22. https://dev.fitbit.com/build/guides/communications/messaging/.

  23. https://dev.fitbit.com/build/guides/settings/.

  24. https://flask.palletsprojects.com/en/0.12.x/.

  25. https://jquery.com/.

  26. https://getbootstrap.com/.

  27. https://www.chartjs.org/.

  28. https://developers.google.com/maps/documentation.

  29. https://firebase.google.com/docs/auth/web/firebaseui.

  30. https://firebase.google.com/docs/firestore/data-model.

  31. https://www.fitbit.com/eu/versa.

  32. https://dev.fitbit.com/build/reference/device-api/system/.

References

  1. Berndsen J, Lawlor A, Smyth B (2017) Running with recommendation. In: HealthRecSys - international workshop on health recommender systems, August 2017, Como, Italy, pp 18–21

  2. Berndsen J, Smyth B, Lawlor A (2019) Pace my race: recommendations for marathon running. In: Proceedings of the 13th ACM conference on recommender systems. pp 246–250

  3. Chen B, Wan J, Celesti A, Li D, Abbas H, Zhang Q (2018) Edge computing in iot-based manufacturing. IEEE Commun Mag 56(9):103–109

    Article  Google Scholar 

  4. Fabrizio RA (2002) Age-based heart rate target zone method and apparatus, February 5 . US Patent 6,345,197

  5. Foster C, Schrager M, Snyder A, Thompson N (1994) Pacing strategy and athletic performance. Sports Med (Auckland, N.Z.) 17:77–85

    Article  Google Scholar 

  6. Howe C (2020) Trainingpeaks performance manager–a new tool for training analysis. Accessed 8 June 2020

  7. Janssen M, Scheerder J, Thibaut E, Brombacher A, Vos S (2017) Who uses running apps and sports watches? determinants and consumer profiles of event runners’ usage of running-related smartphone applications and sports watches. PLoS ONE 12(7)

  8. Kooiman TJM, Dontje ML, Sprenger SR, Krijnen WP, van der Schans CP, de Groot M (2015) Reliability and validity of ten consumer activity trackers. BMC Sports Sci Med Rehabil 7(1):24

    Article  Google Scholar 

  9. Krishnan SPT, Jose L (2015) Ugia Gonzalez. Building your next big thing with google cloud platform: a guide for developers and enterprise architects. Apress

  10. Magdalinski T (2009) Sport, technology and the body: the nature of performance. Routledge, Milton Park

    Book  Google Scholar 

  11. Manogaran G, Mumtaz S, Mavromoustakis CX, Pallis E, Mastorakis G (2021) Artificial intelligence and blockchain-assisted offloading approach for data availability maximization in edge nodes. IEEE Trans Veh Technol 70(3):2404–2412

    Article  Google Scholar 

  12. Mavromoustakis CX, Mastorakis G, Batalla JM (2019) A mobile edge computing model enabling efficient computation offload-aware energy conservation. IEEE Access 7:102295–102303

    Article  Google Scholar 

  13. Moroney L (2017) The definitive guide to firebase. Build android apps on Google’s mobile platform. Springer, Apress, Berkeley, CA

    Book  Google Scholar 

  14. Mukherjee M, Kumar V, Maity D, Matam R, Mavromoustakis CX, Zhang Q, Mastorakis G (2020) Delay-sensitive and priority-aware task offloading for edge computing-assisted healthcare services. In: GLOBECOM 2020–2020 IEEE global communications conference. IEEE, pp 1–5

  15. Rawassizadeh R, Price BA, Petre M (2014) Wearables: Has the age of smartwatches finally arrived? Commun ACM 58(1):45–47

    Article  Google Scholar 

  16. Richardson L, Ruby S (2008) RESTful web services. O’Reilly Media, Inc., Newton

  17. Sigrist R, Rauter G, Marchal-Crespo L, Riener R, Wolf P (2015) Sonification and haptic feedback in addition to visual feedback enhances complex motor task learning. Exp Brain Res 233(3):909–925

    Article  Google Scholar 

  18. Wei J (2014) How wearables intersect with the cloud and the internet of things: considerations for the developers of wearables. IEEE Consum Electron Mag 3(3):53–56

    Article  Google Scholar 

  19. Zhang B, Guo JB, Xu DD (2018) Wearable pace speed monitoring and reminding system. In: 2018 International conference on intelligent transportation, big data & smart city (ICITBS). IEEE, pp 553–556

Download references

Acknowledgements

The research presented in this paper was supported by the project FARGO: Federated leARninG for human moBility (PN-III-P4-ID-PCE-2020-2204). We would like to thank our advisor from Fitbit, Andrei Ene, who was very supportive and brought many impactful observations to this paper, especially during challenging pandemic times. We would also like to thank the reviewers for their time and expertise, constructive comments, and valuable insight.

Author information

Authors and Affiliations

  1. University Politehnica of Bucharest, Bucharest, Romania

    Maria-Ruxandra Avram & Florin Pop

  2. National Institute for Research and Development in Informatics – ICI Bucharest, Bucharest, Romania

    Florin Pop

Authors
  1. Maria-Ruxandra Avram
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Florin Pop
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Florin Pop.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Avram, MR., Pop, F. Real-time running workouts monitoring using Cloud–Edge computing. Neural Comput & Applic 35, 13803–13822 (2023). https://doi.org/10.1007/s00521-021-06675-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06675-3

Keywords

Search

Navigation