Skip to main content
SpringerLink
Log in

Accelerometer Data Based Cyber-Physical System for Training Intensity Estimation

  • Chapter
  • First Online:
Cyber-Physical Systems: Advances in Design & Modelling

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 259))

Abstract

The correctness of athlete’s behavior can be controlled by health care cyber-physical system containing distributed (mobile) sensors and intelligent data processing. Such cyber-physical systems determine a concrete set of events, such as jumps or falls and identify events parameters, e.g. height and duration. The proposed accelerometer data based cyber-physical system differs from existed ones by an original method for detection of various types of athlete’s behavior. A proposed cyber-physical system contains on three modules: the data acquisition module, the data processing module and the processed data visualization module. A method for jump recognition is based on high frequency accelerometer data. The system is developed using Android Studio, R Studio development environments. The results provided by accelerometer data based cyber-physical system might be used for coaches and doctor in sports medicine for decisions regarding the optimal load in future training sessions. Use cases including different experimental setup shows the efficiency of the proposed system.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zanni, A.: Cyber-physical systems and smart cities. IBM Developer. https://developer.ibm.com/chapters/ba-cyber-physical-systems-and-smart-cities-iot/. Last Accessed 05 Apr 2019

  2. Khakhanov, V.I., Khakhanov, V.I., Obrizan, V.I., Mishchenko, A.S., Filippenko, I.V.: Cyber-physical systems as technologies of cyber-administration (analytical review). Electr. Comput. Sci. 1, 39–45 (2014)

    Google Scholar 

  3. Kuj, S.A, Tsvetkov, V.Y.: Network-centric management and cyber-physical systems. Educ. Resour. Technol. 2(19), 86–92 (2017)

    Google Scholar 

  4. Schöder, T.: Cyber-physical production management system SEPIA. Electrotechnical Comput. Syst. 13(89), 197–202 (2014)

    Google Scholar 

  5. Tsvetkov, V.Y.: Management with the use of cyber-physical systems. Prospect. Sci. Educ. 3(27), 55–60 (2017)

    Google Scholar 

  6. Abid, H., Phuong, L.T.T., Wang, J., Lee, S., Qaisar, S: V-Cloud: Vehicular cyber-physical systems and cloud computing. In: 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL, Barcelona (2011). https://doi.org/10.1145/2093698.2093863

  7. Lee, I., Sokolsky, O.: Medical cyber-physical systems. In: 47th Design Automation Conference, DAC, pp. 743–748. Electronic Design Automation Consortium (EDAC), ACM Special Interest Group on Design Automation (SIGDA), IEEE-CEDA, Anaheim, CA (2010)

    Google Scholar 

  8. Hackmann, G., Guo, W., Lu, C., Yan, G., Dyke, S.: Cyber-physical codesign of distributed structural health monitoring with wireless sensor networks. In: 1st ACM/IEEE International Conference on Cyber-Physical Systems, pp. 119–128. ACM Special Interest Group on Embedded Systems (SIGBED), IEEE Technical Committee on Real-Time Systems (TCRTS), Stockholm (2010)

    Google Scholar 

  9. Jezewski, J., Horoba, K., Wrobel, J., Pawlak, A., Czabanski, R., Jezewski, M.: Selected design issues of the medical cyber-physical system for telemonitoring pregnancy at home. Microprocess. Microsyst. 46(Part A), 35–43 (2016)

    Article  Google Scholar 

  10. Pawlak, A., Jezewski, J., Horoba, K.: Dependable medical cyber-physical system for home telecare of high-risk pregnancy. Ada User J. 36(4), 254–258 (2015)

    Google Scholar 

  11. Gerget, O., Devyatykh, D., Shcherbakov, M.: Data-driven approach for modeling of control action impact on anemia dynamics based on energy-informational health state criteria. In: Kravets A., Shcherbakov M., Kultsova M., Groumpos P. (eds.) Creativity in Intelligent Technologies and Data Science. CIT&DS 2017. Communications in Computer and Information Science, vol. 754. Springer, Cham (2017)

    Google Scholar 

  12. Stepanov, V.S.: Asymmetry of motor actions of athletes in three-dimensional space. SPb (2001)

    Google Scholar 

  13. Pestov, E.A.: Mobile device motion recognition. Int. J. Open Inf. Technol. 1, 10–35 (2013)

    Google Scholar 

  14. Butakov, N.A: The applicability of inertial navigation systems in mobile devices. Int. J. Open Inf. Technol. 2(5), 24–31 (2014)

    Google Scholar 

  15. Loginov, S.I.: The possibility of assessing the physical activity of a person using accelerometers motion sensors. (literature review). Bull. N. Med. Technol. XIV(1), 149–151 (2007)

    Google Scholar 

  16. Kazantsev, A.G., Lavrov, D.N.: Personality identification by gait based on wavelet-parameterization of accelerometer readings. Math. Struct. Model. 23, 31–37 (2011)

    Google Scholar 

  17. Syretsky, G.: A: Mems-sensors of orientation and motion parameters. Interexpo Geo-Siberia 2(2), 27–32 (2012)

    Google Scholar 

  18. del Rosario, Michael B., Redmond, Stephen J., Nigel, H.: Lovell tracking the evolution of smartphone sensing for monitoring human movement. Sensors 15, 18901–18933 (2015)

    Article  Google Scholar 

  19. Gaggle. https://play.google.com/store/apps/details?id=com.geeksville.gaggle&hl=en_US. Last Accessed 05 Apr 2019

  20. SyPressure Pro (Barometer). https://play.google.com/store/apps/details?id=sy.android.sypressurepro#?t=W251bGwsMSwxLDIxMiwic3kuYW5kcm9pZC5zeXByZXNzdXJlcHJvIl0. Last Accessed 05 Apr 2019

  21. Height indicator—altimeter. https://play.google.com/store/apps/details?id=com.exatools.altimeter, last accessed 2019/04/05

  22. Accurate altimeter. https://apkpure.com/ru/accurate-altimeter/com.arlabsmobile.altimeterfree. Last Accessed 05 Apr 2019

  23. WOO-tracker. http://shop.woosports.ru/. Last Accessed 05 Apr 2019

  24. Vertical jump parameter meter Vert. https://medgadgets.ru/shop/izmeritel-parametrov-vertikal-nogo-pryzhka-vert.html. Last Accessed 05 Apr 2019

  25. Tran, V.P., Shcherbakov, M., Nguyen, T.A. Yet another method for heterogeneous data fusion and preprocessing in proactive decision support systems: distributed architecture approach. In: Vishnevskiy V., Samouylov K., Kozyrev D. (eds.) Distributed Computer and Communication Networks. DCCN 2017. Communications in Computer and Information Science, vol. 700. Springer, Cham (2017)

    Google Scholar 

  26. Tra, V.P., Shcherbakov, M., Sai, V.C.: On-the-fly multiple sources data analysis in AR-based decision support systems. In: Vishnevskiy V., Kozyrev D. (eds.) Distributed Computer and Communication Networks. DCCN 2018. Communications in Computer and Information Science, vol. 919. Springer, Cham (2018)

    Google Scholar 

  27. Shcherbakov, M., Brebels, A., Shcherbakova, N., Kamaev, V., Gerget, O., Devyatykh, D.: Outlier detection and classification in sensor data streams for proactive decision support systems. In: Conference on Information Technologies in Business and Industry 2016, Journal of Physics: Conference Series, vol. 803 (1), Tomsk (2017). http://dx.doi.org/10.1088/1742-6596/803/1/012143

    Google Scholar 

Download references

Acknowledgements

The reported study was supported by RFBR research project 19-47-340010.

Author information

Authors and Affiliations

  1. Volgograd State Technical University, Volgograd, 400005, Russia

    Igor D. Kazakov, Nataliya L. Shcherbakova & Maxim V. Shcherbakov

  2. Katholieke Universiteit Leuven, Geel, 2440, Belgium

    Adriaan Brebels

Authors
  1. Igor D. Kazakov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nataliya L. Shcherbakova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adriaan Brebels
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maxim V. Shcherbakov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maxim V. Shcherbakov .

Editor information

Editors and Affiliations

  1. Volgograd State Technical University, Volgograd, Russia

    Alla G. Kravets

  2. Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    Alexander A. Bolshakov

  3. Volgograd State Technical University, Volgograd, Russia

    Maxim V. Shcherbakov

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kazakov, I.D., Shcherbakova, N.L., Brebels, A., Shcherbakov, M.V. (2020). Accelerometer Data Based Cyber-Physical System for Training Intensity Estimation. In: Kravets, A., Bolshakov, A., Shcherbakov, M. (eds) Cyber-Physical Systems: Advances in Design & Modelling. Studies in Systems, Decision and Control, vol 259. Springer, Cham. https://doi.org/10.1007/978-3-030-32579-4_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-32579-4_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32578-7

  • Online ISBN: 978-3-030-32579-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation