SpringerLink
Log in

Smart Gait Detection and Analysis

  • Chapter
  • First Online:
Smart and Healthy Walking

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

  • 37 Accesses

Abstract

This chapter first introduces the basic principles and parameters of gait and then explains the laboratory equipment for gait analysis, including motion capture systems, force plates, and pressure pads. Real-life gait detection equipment is introduced, including video and sensor-based detection. Regarding visual perception, video surveillance systems, such as multiple CCTV cameras, can capture gait cycles by processing consecutive video frames using threshold filtering, edge detection, pixel counting, and background segmentation. Regarding wearable sensors, pressure pads and IMUs (inertial measurement units) can be used for gait detection. Pressure pads can be placed inside shoes to measure foot pressure distribution by detecting applied pressure and corresponding electronic changes. IMUs can measure and record motion data, including displacement, velocity, and acceleration. These wearable sensors can effectively detect gait and have good wearability and freedom, suitable for natural gait indoors and outdoors. The application of these technologies provides various options for gait detection and has comprehensive practical value in smart walking. It also covers the application of machine learning and deep learning in gait analysis, as well as research results on health-related walking applications. The chapter provides a comprehensive introduction and overview of the related technologies and applications of health-related walking detection and analysis.

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
EUR 32.99 /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
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 55.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A.A. Hulleck et al., Present and future of gait assessment in clinical practice: towards the application of novel trends and technologies. Front Med Technol 4, 901331 (2022)

    Article  Google Scholar 

  2. M. Burnfield, Gait analysis: normal and pathological function. J. Sports Sci. Med. 9(2), 353 (2010)

    Google Scholar 

  3. I. Rida et al., Improved Human Gait Recognition (Springer International Publishing, Cham, 2015)

    Book  Google Scholar 

  4. T. Ramakrishnan, S.H. Kim, K.B. Reed, Human gait analysis metric for gait retraining. Appl. Bionics Biomech. 2019, 1286864 (2019)

    Article  Google Scholar 

  5. A.M. Muniz, J. Nadal, Application of principal component analysis in vertical ground reaction force to discriminate normal and abnormal gait. Gait Posture 29(1), 31–35 (2009)

    Article  Google Scholar 

  6. J.B. Dingwell, B.L. Davis, A rehabilitation treadmill with software for providing real-time gait analysis and visual feedback. J. Biomech. Eng. 118(2), 253–255 (1996)

    Article  Google Scholar 

  7. Available from https://docs.vicon.com/display/Nexus212/Automatically+assess+foot+strikes?preview=/133829020/133829290/AutoValidate.png

  8. R. Cross, Standing, walking, running and jum** on a force plate. Am. J. Phys. 67(4), 304–309 (1999)

    Article  Google Scholar 

  9. G. Beckham, T. Suchomel, S. Mizuguchi, Force plate use in performance monitoring and sport science testing. New Stud. Athletics 29(3), 25–37 (2014)

    Google Scholar 

  10. Available from https://www.movella.com/products/sensor-modules

  11. https://www.amti.biz/

  12. https://www.amti.biz/product/bioanalysis/

  13. Y.-J. Lee, J.N. Liang, Characterizing intersection variability of butterfly diagram in post-stroke gait using Kernel density estimation. Gait Posture 76, 157–161 (2020)

    Article  Google Scholar 

  14. Y.-L. Yen et al., Recognition of walking directional intention employed ground reaction forces and center of pressure during gait initiation. Gait Posture 106, 23–27 (2023)

    Article  Google Scholar 

  15. Available from https://www.amti.biz/product/netforce/

  16. J. Hjelmgren, Dynamic Measurement of Pressure. A Literature Survey (2002)

    Google Scholar 

  17. Available from https://www.tekscan.com/products-solutions/systems/strideway-system

  18. Available from https://www.tekscan.com/sites/default/files/mdl_DS_Strideway_RevE.pdf

  19. M.H. Khan, M.S. Farid, M. Grzegorzek, Vision-based approaches towards person identification using gait. Comput. Sci. Rev. 42, 100432 (2021)

    Article  Google Scholar 

  20. K. Sato et al., Quantifying normal and parkinsonian gait features from home movies: practical application of a deep learning–based 2D pose estimator. PLoS ONE 14(11), e0223549 (2019)

    Article  Google Scholar 

  21. C.S.T. Hii et al., Automated gait analysis based on a marker-free pose estimation model. Sensors 23(14), 6489 (2023)

    Article  Google Scholar 

  22. E. Hossain, G. Chetty, Multimodal feature learning for gait biometric based human identity recognition, in Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3–7, 2013. Proceedings, Part II 20 (Springer, 2013)

    Google Scholar 

  23. M. Jeevan et al., Gait recognition based on gait pal and pal entropy image, in 2013 IEEE International Conference on Image Processing (IEEE, 2013)

    Google Scholar 

  24. C. Wang et al., Chrono-gait image: a novel temporal template for gait recognition, in Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5–11, 2010, Proceedings, Part I 11 (Springer, 2010)

    Google Scholar 

  25. A.S. Alharthi, S.U. Yunas, K.B. Ozanyan, Deep learning for monitoring of human gait: a review. IEEE Sens. J. 19(21), 9575–9591 (2019)

    Article  Google Scholar 

  26. C. Yan, B. Zhang, F. Coenen, Multi-attributes gait identification by convolutional neural networks, in 2015 8th International Congress on Image and Signal Processing (CISP) (IEEE, 2015)

    Google Scholar 

  27. J. Tao et al., Real-time pressure map** smart insole system based on a controllable vertical pore dielectric layer. Microsyst. Nanoeng. 6(1), 62 (2020)

    Article  Google Scholar 

  28. C.M. Senanayake, S.A. Senanayake, Computational intelligent gait-phase detection system to identify pathological gait. IEEE Trans. Inf. Technol. Biomed. 14(5), 1173–1179 (2010)

    Article  Google Scholar 

  29. R. Harle et al., Towards real-time profiling of sprints using wearable pressure sensors. Comput. Commun. 35(6), 650–660 (2012)

    Article  Google Scholar 

  30. T. Stöggl, A. Martiner, Validation of Moticon’s OpenGo sensor insoles during gait, jumps, balance and cross-country skiing specific imitation movements. J. Sports Sci. 35(2), 196–206 (2017)

    Article  Google Scholar 

  31. H. Prasanth et al., Wearable sensor-based real-time gait detection: a systematic review. Sensors 21(8), 2727 (2021)

    Article  Google Scholar 

  32. Z. Huang, J. Li, J. Lian, Wearable sensors for detecting and measuring kinetic characteristics, in Journal of Physics: Conference Series (IOP Publishing, 2022)

    Google Scholar 

  33. Y.-J. Chen, L.-X. Chen, Y.-J. Lee, Systematic evaluation of features from pressure sensors and step number in gait for age and gender recognition. IEEE Sens. J. 22(3), 1956–1963 (2021)

    Article  Google Scholar 

  34. T.-H. Chen et al., Classification of high mental workload and emotional statuses via machine learning feature extractions in gait. Int. J. Ind. Ergon. 97, 103503 (2023)

    Article  Google Scholar 

  35. P. Arens et al., Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke. Wearable Technol. 2, e2 (2021)

    Article  Google Scholar 

  36. N. Ketkar, Convolutional neural networks, in Deep Learning with Python: A Hands-On Introduction. (Apress, Berkeley, CA, 2017), pp.63–78

    Chapter  Google Scholar 

  37. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  38. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning (MIT Press, 2016)

    Google Scholar 

  39. S.U. Yunas, K.B. Ozanyan, Gait activity classification using multi-modality sensor fusion: a deep learning approach. IEEE Sens. J. 21(15), 16870–16879 (2021)

    Article  Google Scholar 

  40. R. Romijnders et al., A deep learning approach for gait event detection from a single shank-worn IMU: validation in healthy and neurological cohorts. Sensors 22(10), 3859 (2022)

    Article  Google Scholar 

  41. S. Bai, J.Z. Kolter, V. Koltun, An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. ar**v preprint ar**v:1803.01271 (2018)

  42. B. Filtjens et al., A data-driven approach for detecting gait events during turning in people with Parkinson’s disease and freezing of gait. Gait Posture Posture 80, 130–136 (2020)

    Article  Google Scholar 

  43. C.-C. Wu, Y.-T. Wen, Y.-J. Lee, IMU sensors beneath walking surface for ground reaction force prediction in gait. IEEE Sens. J. 20(16), 9372–9376 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Tin-Chih Toly Chen

  2. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan

    Yun-Ju Lee

Authors
  1. Tin-Chih Toly Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yun-Ju Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tin-Chih Toly Chen .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chen, TC.T., Lee, YJ. (2024). Smart Gait Detection and Analysis. In: Smart and Healthy Walking. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-59443-4_3

Download citation

Publish with us

Policies and ethics

Search

Navigation