iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/978-3-031-77731-8_17
Preliminary Investigation on Machine Learning and Deep Learning Models for Change of Direction Classification in Running | SpringerLink
Skip to main content
Springer Nature Link
Log in

Preliminary Investigation on Machine Learning and Deep Learning Models for Change of Direction Classification in Running

  • Conference paper
  • First Online:
Intelligent Data Engineering and Automated Learning – IDEAL 2024 (IDEAL 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15346))

  • 201 Accesses

Abstract

The ability to detect, define, and classify Change of Direction (COD) movements during running plays a crucial role in sports science, as it has been widely used to assess athlete performance. Automating the process of COD classification during live games or training can provide real-time feedback. In this study, we evaluated Machine Learning (ML) and Deep Learning (DL) models for the classification of COD using accelerometers and gyroscope sensor data, and speed data were calculated from the Global Positioning System (GPS) sensor data. We hypothesized that DL algorithms classify COD better than ML classification algorithms. Comparative analysis showed that the best-performing DL and ML models showed similar behavior. Similarly, the statistical analysis observed no significant difference. This emphasized the importance of accurate model selection.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 74.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    (Apex Units, STATSport, Newry, Co. Down, Northern Ireland).

  2. 2.

    (Min-Max Scaler function is used).

References

  1. Abbaspour, S., Fotouhi, F., Sedaghatbaf, A., Fotouhi, H., Vahabi, M., Linden, M.: A comparative analysis of hybrid deep learning models for human activity recognition. Sensors 20(19), 5707 (2020)

    Article  Google Scholar 

  2. Akan, A., Cura, O.K.: Time-frequency signal processing: today and future. Digit. Signal Process. 119, 103216 (2021)

    Article  Google Scholar 

  3. Alanen, A., Räisänen, A., Benson, L., Pasanen, K.: The use of inertial measurement units for analyzing change of direction movement in sports: a scoping review. Int. J. Sports Sci. Coach. 16(6), 1332–1353 (2021)

    Article  Google Scholar 

  4. Ann, O.C., Theng, L.B.: Human activity recognition: a review. In: 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), pp. 389–393. IEEE (2014)

    Google Scholar 

  5. Bloomfield, J., Polman, R., O’Donoghue, P.: Physical demands of different positions in fa premier league soccer. J. Sports Sci. Med. 6(1), 63 (2007)

    Google Scholar 

  6. Clarke, R., Mundy, P., Aspe, R., Sargent, D., Hughes, J.: Change of direction technical models: biomechanical principles (2018)

    Google Scholar 

  7. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  Google Scholar 

  8. Dos’ Santos, T., McBurnie, A., Comfort, P., Jones, P.A.: The effects of six-weeks change of direction speed and technique modification training on cutting performance and movement quality in male youth soccer players. Sports 7(9), 205 (2019)

    Article  Google Scholar 

  9. Faude, O., Koch, T., Meyer, T.: Straight sprinting is the most frequent action in goal situations in professional football. J. Sports Sci. 30(7), 625–631 (2012)

    Article  Google Scholar 

  10. Ferraz, A., Duarte-Mendes, P., Sarmento, H., Valente-Dos-Santos, J., Travassos, B.: Tracking devices and physical performance analysis in team sports: a comprehensive framework for research—trends and future directions. Front. Sports Active Living 5 (2023)

    Google Scholar 

  11. George, A.H., Shahul, A., George, A.S.: Wearable sensors: a new way to track health and wellness. Partners Univ. Int. Innov. J. 1(4), 15–34 (2023)

    Google Scholar 

  12. Gjoreski, H., Bizjak, J., Gjoreski, M., Gams, M.: Comparing deep and classical machine learning methods for human activity recognition using wrist accelerometer. In: Proceedings of the IJCAI 2016 Workshop on Deep Learning for Artificial Intelligence, New York, NY, USA, vol. 10, p. 970 (2016)

    Google Scholar 

  13. Hewamalage, H., Bergmeir, C., Bandara, K.: Recurrent neural networks for time series forecasting: current status and future directions. Int. J. Forecast. 37(1), 388–427 (2021)

    Article  Google Scholar 

  14. Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: 2014 Science and Information Conference, pp. 372–378. IEEE (2014)

    Google Scholar 

  15. Khana, H., Qasima, S.Z., Waqarb, A.: COD detection for injury prevention using sports analytics

    Google Scholar 

  16. Marques, J.B., Paul, D.J., Graham-Smith, P., Read, P.J.: Change of direction assessment following anterior cruciate ligament reconstruction: a review of current practice and considerations to enhance practical application. Sports Med. 50, 55–72 (2020)

    Article  Google Scholar 

  17. Meghji, M., et al.: An algorithm for the automatic detection and quantification of athletes’ change of direction incidents using IMU sensor data. IEEE Sens. J. 19(12), 4518–4527 (2019)

    Article  Google Scholar 

  18. Mekruksavanich, S., Jitpattanakul, A.: Lstm networks using smartphone data for sensor-based human activity recognition in smart homes. Sensors 21(5), 1636 (2021)

    Article  Google Scholar 

  19. Müller, P.N., Müller, A.J., Achenbach, P., Göbel, S.: IMU-based fitness activity recognition using CNNs for time series classification. Sensors 24(3), 742 (2024)

    Article  Google Scholar 

  20. Munguia Tapia, E.: Using machine learning for real-time activity recognition and estimation of energy expenditure. Ph.D. thesis, Massachusetts Institute of Technology (2008)

    Google Scholar 

  21. Muralidharan, K., Ramesh, A., Rithvik, G., Prem, S., Reghunaath, A., Gopinath, M.: 1d convolution approach to human activity recognition using sensor data and comparison with machine learning algorithms. International Journal of Cognitive Computing in Engineering 2, 130–143 (2021)

    Article  Google Scholar 

  22. Nayak, S., Panigrahi, C.R., Pati, B., Nanda, S., Hsieh, M.Y.: Comparative analysis of HAR datasets using classification algorithms. Comput. Sci. Inf. Syst. 19(1), 47–63 (2022)

    Article  Google Scholar 

  23. Nedelec, M., McCall, A., Carling, C., Legall, F., Berthoin, S., Dupont, G.: The influence of soccer playing actions on the recovery kinetics after a soccer match. J. Strength Cond. Res. 28(6), 1517–1523 (2014)

    Article  Google Scholar 

  24. Nimphius, S., Callaghan, S.J., Bezodis, N.E., Lockie, R.G.: Change of direction and agility tests: challenging our current measures of performance. Strength Cond. J. 40(1), 26–38 (2018)

    Article  Google Scholar 

  25. Pienaar, S.W., Malekian, R.: Human activity recognition using LSTM-RNN deep neural network architecture. In: 2019 IEEE 2nd Wireless Africa Conference (WAC), pp. 1–5. IEEE (2019)

    Google Scholar 

  26. Ramanujam, E., Perumal, T., Padmavathi, S.: Human activity recognition with smartphone and wearable sensors using deep learning techniques: a review. IEEE Sens. J. 21(12), 13029–13040 (2021)

    Article  Google Scholar 

  27. Raschka, S.: Model evaluation, model selection, and algorithm selection in machine learning. arXiv preprint arXiv:1811.12808 (2018)

  28. Reilly, B., Morgan, O., Czanner, G., Robinson, M.A.: Automated classification of changes of direction in soccer using inertial measurement units. Sensors 21(14), 4625 (2021)

    Article  Google Scholar 

  29. Robinson, G., O’Donoghue, P.: A movement classification for the investigation of agility demands and injury risk in sport. Int. J. Perform. Anal. Sport 8(1), 127–144 (2008)

    Article  Google Scholar 

  30. Saez, Y., Baldominos, A., Isasi, P.: A comparison study of classifier algorithms for cross-person physical activity recognition. Sensors 17(1), 66 (2016)

    Article  Google Scholar 

  31. Shen, Z., Zhang, Y., Lu, J., Xu, J., Xiao, G.: A novel time series forecasting model with deep learning. Neurocomputing 396, 302–313 (2020)

    Article  Google Scholar 

  32. Singh, D., et al.: Human activity recognition using recurrent neural networks. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2017. LNCS, vol. 10410, pp. 267–274. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66808-6_18

    Chapter  Google Scholar 

  33. Tariq, M.U.: Advanced wearable medical devices and their role in transformative remote health monitoring. In: Transformative Approaches to Patient Literacy and Healthcare Innovation, pp. 308–326. IGI Global (2024)

    Google Scholar 

  34. Tous-Fajardo, J., Gonzalo-Skok, O., Arjol-Serrano, J.L., Tesch, P.: Enhancing change-of-direction speed in soccer players by functional inertial eccentric overload and vibration training. Int. J. Sports Physiol. Perform. 11(1), 66–73 (2016)

    Article  Google Scholar 

  35. Wundersitz, D.W., Josman, C., Gupta, R., Netto, K.J., Gastin, P.B., Robertson, S.: Classification of team sport activities using a single wearable tracking device. J. Biomech. 48(15), 3975–3981 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported with the financial support of the Science Foundation Ireland grant 13/RC/2094_P2 and co-funded under the European Regional Development Fund through the Southern and Eastern Regional Operational Programme to Lero - the Science Foundation Ireland Research Centre for Software (www.lero.ie). The authors thank STATSports for continuous support. The authors also thank the Regulated Software Research Centre (RSRC), where this research was conducted.

Author information

Authors and Affiliations

  1. Dundalk Institute of Technology, Dundalk, Ireland

    Pranay Jaiswal, Abhishek Kaushik, Fiona Lawless & Fergal McCaffery

  2. STATSports, Newry, Northern Ireland

    Tiago Malaquias

Authors
  1. Pranay Jaiswal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhishek Kaushik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fiona Lawless
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tiago Malaquias
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fergal McCaffery
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhishek Kaushik .

Editor information

Editors and Affiliations

  1. Technical University of Valencia, Valencia, Valencia, Spain

    Vicente Julian

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. The University of Manchester, Manchester, UK

    Hujun Yin

  4. Universitat Politècnica de València, Valencia, Valencia, Spain

    Juan M. Alberola

  5. University of Evora, Evora, Portugal

    Vitor Beires Nogueira

  6. Universidade do Minho, Braga, Portugal

    Paulo Novais

  7. University of Huelva, Huelva, Spain

    Antonio Tallón-Ballesteros

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jaiswal, P., Kaushik, A., Lawless, F., Malaquias, T., McCaffery, F. (2025). Preliminary Investigation on Machine Learning and Deep Learning Models for Change of Direction Classification in Running. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15346. Springer, Cham. https://doi.org/10.1007/978-3-031-77731-8_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-77731-8_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77730-1

  • Online ISBN: 978-3-031-77731-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation