Analyzing Mobile Cycling Applications for Monitoring Workouts

  • Fabricio Landero CristobalEmail author
  • Miguel A. Wister
  • Pablo Payro Campos
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 97)


This paper analyzes three mobile bike applications that compare different measurements in this sport. For cyclists, it is crucial to know the power of pedaling, several computer systems estimate or calculate this variable instead of measuring. There are power meters, but several models give different measurements. This paper tries to show that some mobile applications for cycling supply different measurements to each other, as well as the power obtained by estimation. We showed by means three experimental rides that sometimes the power measurements are not proportional to the speed produced by the cyclist, so we propose to build a mobile bike application that integrates data from power meters, speedometers, and wireless sensor network to synchronize power and speed for delivering it to the cyclist in real time.



This paper was supported by Programa de Fortalecimiento de la Calidad Educativa (PFCE) 2019. Number: P/PFCE-2019-27MSU0018V-11. We would also like to express our gratitude to the Universidad Juarez Autonoma de Tabasco for supporting the academic resources needed for this research.


  1. 1.
    Austin, C.: Bike calculator (2019).
  2. 2.
    BikeCitizens: Bike citizens official web (2019).
  3. 3.
    Bkool: Bkool official web (2019).
  4. 4.
    Eisenman, S.B., Miluzzo, E., Lane, N.D., Peterson, R.A., Ahn, G.S., Campbell, A.T.: BikeNet: a mobile sensing system for cyclist experience mapping. ACM Trans. Sen. Netw. 6(1), 6:1–6:39 (2010). Scholar
  5. 5.
    Endomondo: Endomondo official web (2019).
  6. 6.
    Flüchter, K., Wortmann, F.: Implementing the connected e-bike: challenges and requirements of an IoT application for urban transportation (2014).
  7. 7.
    Gribble, S.D.: The computational cyclist (2019).
  8. 8.
    Guerriero, A., Guaragnella, C., Martines, C., Castellaneta, A.: A distributed health navigation system based on opportunistic mobile WSN, pp. 1–6 (2012).
  9. 9.
    iGPSPORT: Igpsport official web (2019).
  10. 10.
    Korff, T., Romer, L., Mayhew, I., Martin, J.C.: Effect of pedaling technique on mechanical effectiveness and efficiency in cyclists (2007). Scholar
  11. 11.
    MapMyRide: Map my ride official web (2019).
  12. 12.
    Marin-Perianu, R., Marin-Perianu, M., Havinga, P., Taylor, S., Begg, R., Palaniswami, M., Rouffet, D.: A performance analysis of a wireless body-area network monitoring system for professional cycling (2013). Scholar
  13. 13.
    Polar: Polar official web (2019).
  14. 14.
    Runtastic: Runtastic official web (2019).
  15. 15.
    Oliveira, D.S., Afonso, J.: Mobile sensing system for georeferenced performance monitoring in cycling. In: World Congress on Engineering 2015, vol. 1 (2015)Google Scholar
  16. 16.
    SIGMA: Sigma official web (2019).
  17. 17.
    Strava: Strava official web (2019).
  18. 18.
    Taniguchi, Y., Nishii, K., Hisamatsu, H.: Evaluation of a bicycle-mounted ultrasonic distance sensor for monitoring road surface condition. In: 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks, pp. 31–34 (2015).
  19. 19.
    Ueberham, M., Schmidt, F., Schlink, U.: Advanced smartphone-based sensing with open-source task automation. Sensors 18(8), 2456 (2018). Scholar
  20. 20.
    WahooFitness: Wahoo fitness official web (2019).
  21. 21.
    Zhao, Y., Su, Y., Chang, Y.: A real-time bicycle record system of ground conditions based on internet of things. IEEE Access 5, 17525–17533 (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Fabricio Landero Cristobal
    • 1
    Email author
  • Miguel A. Wister
    • 1
  • Pablo Payro Campos
    • 1
  1. 1.Academic Division of Information Technology and SystemsJuarez Autonomous University of TabascoCunduacanMexico

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