Skip to main content
SpringerLink
Log in

Utilizing data from a local positioning system as input into a neural network to determine stride length

  • Original Article
  • Published:
Sports Engineering Aims and scope Submit manuscript

Abstract

Machine learning methods such as a stepwise linear regression and a feedforward neural network were implemented and analyzed for their capability of estimating stride length using wearable sensors. Strides were segmented using acceleration data from a sacrum mounted inertial measurement unit. Stride length was then obtained by calculating the difference in position from a local positioning system (LPS) operating in the ultrawide bandwidth. Using the last stride of the trial, 27 predictor variables from the LPS were analyzed. Position data from motion capture (MOCAP) were used (i.e. the reference system) for determining accuracy of each machine learning method. Variables from the stepwise linear regression model were used as input nodes for the neural network. To avoid overfitting the data in the neural network, a gradient descent optimization algorithm was used. Based on an a priori analysis, 90 participants were recruited and performed three self-selected walk, run and sprint speeds. Upon analyzing, the lowest normalized root mean square error was 0.08 by the feedforward neural network (i.e. the comparator) after receiving inputs from the regression model and learning stride length estimates from MOCAP. Therefore, the neural network accurately estimated stride length when learning from a reference system such as motion capture.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Morin JB, Samozino P, Murata M, Cross MR, Nagahara R (2019) A simple method for computing sprint acceleration kinetics from running velocity data: replication study with improved design. J Biomech 94:82–87. https://doi.org/10.31236/osf.io/vgqzs

    Article  Google Scholar 

  2. Reina Román M, García-Rubio J, Feu S, Ibáñez SJ (2019) Training and competition load monitoring and analysis of women’s amateur basketball by playing position: approach study. Front Psychol 9:2689

    Article  Google Scholar 

  3. Willy RW (2019) Innovations and pitfalls in the use of wearable devices in the prevention and rehabilitation of running related injuries. Phys Ther Sport 29:26–33. https://doi.org/10.1016/j.ptsp.2017.10.003

    Article  Google Scholar 

  4. Linke D, Link D, Lames M (2018) Validation of electronic performance and tracking systems EPTS under field conditions. PLoS ONE 13(7):e0199519. https://doi.org/10.1371/journal.pone.0199519

    Article  Google Scholar 

  5. Alt PS, Baumgart C, Ueberschär O, Freiwald J, Hoppe MW (2020) Validity of a local positioning system during outdoor and indoor conditions for team sports. Sensors 20:5733. https://doi.org/10.3390/s20205733

    Article  Google Scholar 

  6. Wang D, Chen L, Piscarreta D, Tam KW (2015) Classification and regression of ultra wide band signals. Proceedings of 2015 Chinese automation congress (CAC). https://doi.org/10.1109/cac.2015.7382815

  7. Zijlstra W, Hof AL (2003) Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 18:1–10. https://doi.org/10.1016/s0966-6362(02)00190-x

    Article  Google Scholar 

  8. Siciliano B, Khatib O (2016) Inertial Sensors, GPS, and Odometry. Handbook of robotics. Springer International Publishing, New York, pp 477–490

    Chapter  MATH  Google Scholar 

  9. Potter MV, Ojeda LV, Perkins NC, Cain SM (2019) Effect of IMU design on IMU-derived stride metrics for running. Sensors 19:2601. https://doi.org/10.3390/s19112601

    Article  Google Scholar 

  10. Li X, He J, Xu L, Wang Q (2016) The effect of multipath and NLOS on TOA ranging error and energy based on UWB. Proceedings of the international conference on consumer electronics-Taiwan (ICCE-TW)

  11. Singh P, Esposito M, Barrons Z, Clermont CA, Wannop J, Stefanyshyn D (2021) Measuring gait velocity and stride length with an ultrawide bandwidth local positioning system and an inertial measurement unit. Sensors 21:2896. https://doi.org/10.3390/s21092896

    Article  Google Scholar 

  12. Yang S, Li Q (2012) Inertial sensor-based methods in walking speed estimation: a systematic review. Sensors 12:6102–6116. https://doi.org/10.3390/s120506102

    Article  Google Scholar 

  13. Song Y, Shin S, Kim S, Lee D, Lee KH (2007) Speed estimation from a tri-axial accelerometer using neural networks. In: Proceedings of the IEEE engineering in medicine and biology society

  14. Farokhmanesh F, Sadeghi MT (2019) Deep feature selection using an enhanced sparse group lasso algorithm. In: Proceedings of the iranian conference on electrical engineering (ICEE)

  15. Tuttle WM (2018) Relationship of maximal strength and relative stride length in sprinters. dissertation, Indiana State University

  16. Acharya R (2014) Navigation Receiver. Understanding Satellite Navigation. Elsevier, Cambridge, pp 155–215

    Chapter  Google Scholar 

  17. Kawabata M, Goto K, Fukusaki C, Sasaki K, Hihara E, Mizushina T, Ishii N (2013) Acceleration patterns in the lower and upper trunk during running. J Sports Sci 31:1841–1853. https://doi.org/10.1080/02640414.2013.805884

    Article  Google Scholar 

  18. Ng H, Tong HL, Tan WH, Abdullah J (2011) Improved gait classification with different smoothing techniques. Int J Adv Sci Eng Inf Technol 1(3):242–247. https://doi.org/10.18517/ijaseit.1.3.51

    Article  Google Scholar 

  19. Popovic N, Miljkovic N, Djordjevic O, Sekara TB (2016). Artifact cancellation using median filter, moving average filter, and fractional derivatives in biomedical signals. Proceedings of the international conference on fractional differentiation and its applications (ICFDA 2016), Novi Sad, Serbia

  20. Bland JM, Altman D (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327:307–310. https://doi.org/10.1016/s0140-6736(86)90837-8

    Article  Google Scholar 

  21. Annadhorai A, Guenterberg E, Barnes J, Haraga K, Jafari R (2008) Human identification by gait analysis. In: Proceedings of the 2nd International workshop on systems and networking support for health care and assisted living environments

  22. Zebari R, Abdulazeez A, Zeebaree D, Zebari D, Saeed JA (2020) Comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. JASTT 1:56–70. https://doi.org/10.38094/jastt1224

    Article  Google Scholar 

  23. Roelofs R (2019) Measuring generalization and overfitting in machine learning. dissertation, University of California Berkeley.

  24. Martinasek Z, Hajny J, Malina L (2013) Optimization of power analysis using neural network. In: Proceedings of the international conference on smart card research and advanced applications

  25. Ahmadi A, Destelle F, Unzueta L, Monaghan DS, Linaza MT, Moran K, O’Connor NE (2016) 3D human gait reconstruction and monitoring using body-worn inertial sensors and kinematic modeling. IEEE Sens J 16:8823–8831. https://doi.org/10.1109/jsen.2016.2593011

    Article  Google Scholar 

  26. Yang S, Ting TO, Man KL, Guan SU (2013) Investigation of neural networks for function approximation. Procedia Comput Sci 17:586–594

    Article  Google Scholar 

  27. Xu K, Zhang M, Li J, Du SS, Kawarabayashi KI, Jegelka S. (2021). How neural networks extrapolate: from feedforward to graph neural networks. In: Proceedings of ICLR 2021

  28. Singh P, Esposito M, Barrons ZB, Clermont CA, Wannop JW, Stefanyshyn DJ (2020) Determining stride length of runners using an ultrawide bandwidth local positioning system equipped with an inertial measurement unit. In: Proceedings of the 44th meeting of the american society of biomechanics

  29. Ogris G, Leser R, Horsak B, Kornfeind P, Heller M, Baca A (2012) Accuracy of the LPM tracking system considering dynamic position changes. J Sports Sci 30:1503–1511. https://doi.org/10.1080/02640414.2012.712712

    Article  Google Scholar 

Download references

Funding

This research was funded by NSERC Create We-TRAC Grant and MITACS Accelerate, Grant Number IT13453.

Author information

Authors and Affiliations

  1. Biomedical Engineering Graduate Program, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada

    Pratham Singh, Michael J. S. Esposito & Darren J. Stefanyshyn

  2. Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada

    Zach B. Barrons, Christian A. Clermont, John W. Wannop & Darren J. Stefanyshyn

Authors
  1. Pratham Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael J. S. Esposito
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zach B. Barrons
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christian A. Clermont
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John W. Wannop
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Darren J. Stefanyshyn
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

PS, JWW and DJS designed the study PS, MJSE, ZBB and CAC participated in data acquisition and data analysis. PS wrote the manuscript and prepared all the figures PS, MJSE, ZBB, CAC, JWW and DJS reviewed the manuscript.

Corresponding author

Correspondence to Darren J. Stefanyshyn.

Ethics declarations

Conflict of interest

As part of the MITACS Accelerate Grant, Author P.S. has received research support from XCo Tech Inc.

Additional information

Publisher's Note

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

This article is a part of Topical Collection in Sports Engineering on Wearable Sensor Technology in Sports Monitoring, Edited by Dr. Hugo G Espinosa, Dr. Aimee Mears, Prof. Andy Stamm, Prof. Yuji Ohgi and Ms. Christine Coniglio.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, P., Esposito, M.J.S., Barrons, Z.B. et al. Utilizing data from a local positioning system as input into a neural network to determine stride length. Sports Eng 25, 16 (2022). https://doi.org/10.1007/s12283-022-00383-4

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12283-022-00383-4

Keywords

Search

Navigation