Skip to main content

Advertisement

SpringerLink
Log in

An adaptive gyroscope-based algorithm for temporal gait analysis

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Body-worn kinematic sensors have been widely proposed as the optimal solution for portable, low cost, ambulatory monitoring of gait. This study aims to evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis using body-worn wireless gyroscopes. Gyroscope data from nine healthy adult subjects performing four walks at four different speeds were then compared against data acquired simultaneously using two force plates and an optical motion capture system. Data from a poliomyelitis patient, exhibiting pathological gait walking with and without the aid of a crutch, were also compared to the force plate. Results show that the mean true error between the adaptive gyroscope algorithm and force plate was −4.5 ± 14.4 ms and 43.4 ± 6.0 ms for IC and TC points, respectively, in healthy subjects. Similarly, the mean true error when data from the polio patient were compared against the force plate was −75.61 ± 27.53 ms and 99.20 ± 46.00 ms for IC and TC points, respectively. A comparison of the present algorithm against temporal gait parameters derived from an optical motion analysis system showed good agreement for nine healthy subjects at four speeds. These results show that the algorithm reported here could constitute the basis of a robust, portable, low-cost system for ambulatory monitoring of gait.

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
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Aminian K et al (2002) Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. J Biomech 35(5):689–699

    Article  PubMed  Google Scholar 

  2. Aminian K et al (2004) Evaluation of an ambulatory system for gait analysis in hip osteoarthritis and after total hip replacement. Gait Posture 20(1):102–107

    Article  CAS  PubMed  Google Scholar 

  3. Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Stat Methods Med Res 8(2):135–160

    Article  CAS  PubMed  Google Scholar 

  4. Burns A et al (2010) SHIMMER™—a Wireless sensor platform for non-invasive biomedical research. IEEE Sens 10(9):1527–1534

    Article  Google Scholar 

  5. Burns A et al (2010) Open shareable research platform for developing interoperable personal health systems. In: 1st AMA-IEEE medical technology conference on individualized medicine, Washington, DC

  6. Catalfamo P et al (2008) Detection of gait events using an F-Scan in-shoe pressure measurement system. Gait Posture 28(3):420–426

    Article  PubMed  Google Scholar 

  7. Cutti A et al (2010) ‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors. Med Biol Eng Comput 48(1):17–25

    Article  PubMed  Google Scholar 

  8. de Chazal P, Reilly RB (2006) A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 53(12):2535–2543

    Article  PubMed  Google Scholar 

  9. Ferraris F, Grimaldi U, Parvis M (1995) Procedure for effortless in-field calibration of three-axis rate gyros and accelerometers. Sens Mater 7(5):311–330

    Google Scholar 

  10. Frenkel-Toledo S et al (2005) Effect of gait speed on gait rhythmicity in Parkinson’s disease: variability of stride time and swing time respond differently. J Neuroeng Rehabil 31:2–23

    Google Scholar 

  11. Han J et al (2009) Adaptive windowing for gait phase discrimination in Parkinsonian gait using 3-axis acceleration signals. Med Biol Eng Comput 47(11):1155–1164

    Article  PubMed  Google Scholar 

  12. Hausdorff JM et al (1997) Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. J Appl Physiol 82(1):262–269

    CAS  PubMed  Google Scholar 

  13. Hreljac A, Marshall RN (2000) Algorithms to determine event timing during normal walking using kinematic data. J Biomech 33(6):783–786

    Article  CAS  PubMed  Google Scholar 

  14. Iasemidis LD et al (2003) Adaptive epileptic seizure prediction system. IEEE Trans Biomed Eng 50(5):616–627

    Article  PubMed  Google Scholar 

  15. Jasiewicz JM et al (2006) Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. Gait Posture 24(4):502–509

    Article  PubMed  Google Scholar 

  16. Kotiadis D, Hermens HJ, Veltink PH (2010) Inertial gait phase detection for control of a drop foot stimulator: inertial sensing for gait phase detection. Med Eng Phys 32(4):287–297

    Article  CAS  PubMed  Google Scholar 

  17. Lorincz K et al (2007) Wearable wireless sensor network to assess clinical status in patients with neurological disorders. In: 6th International symposium on information processing in sensor networks

  18. McGrath MJ, Dishongh TJ (2009) A common personal health research platform—SHIMMER™ and BioMOBIUS™. Intel Technol J 13(3):122–147

    Google Scholar 

  19. Miller A (2009) Gait event detection using a multilayer neural network. Gait Posture 29(4):542–545

    Article  PubMed  Google Scholar 

  20. Najafi B et al (2002) Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Trans Biomed Eng 49(8):843–851

    Article  PubMed  Google Scholar 

  21. O’Connor CM et al (2007) Automatic detection of gait events using kinematic data. Gait Posture 25(3):469–474

    Article  PubMed  Google Scholar 

  22. O’Donovan KJ et al (2009) SHIMMER: a new tool for temporal Gait analysis. In: Proceedings of IEEE engineering in medicine and biology, Minneapolis, MN

  23. Rangayyan RM (2002) Biomedical signal analysis. IEEE Press/Wiley, New York, NY

    Google Scholar 

  24. Sabatini AM et al (2005) Assessment of walking features from foot inertial sensing. IEEE Trans Biomed Eng 52(3):486–494

    Article  PubMed  Google Scholar 

  25. Salarian A et al (2004) Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans Biomed Eng 51(8):1434–1443

    Article  PubMed  Google Scholar 

  26. Shrout PE, Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86(2):420–428

    Article  CAS  PubMed  Google Scholar 

  27. Tong K, Granat MH (1999) A practical gait analysis system using gyroscopes. Med Eng Phys 21(2):87–94

    Article  CAS  PubMed  Google Scholar 

  28. Zijlstra W, Hof AL (2003) Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 18(2):1–10

    Article  PubMed  Google Scholar 

Download references

Acknowledgment

This research was completed as part of a wider programme of research within the TRIL Centre (Technology Research for Independent Living). The TRIL Centre is a multi-disciplinary research centre, bringing together researchers from UCD, TCD, NUIG, Intel, and GE Healthcare, funded by Intel, GE Healthcare and IDA Ireland (http://www.trilcentre.org). The authors would like to thank Dr. Emer Doheny for providing useful feedback on the manuscript as well as Mr. Ben Dromey for his help with graphically detailing the experimental layout.

Author information

Authors and Affiliations

  1. Intel Digital Health Group, Leixlip, Co, Kildare, Ireland

    Barry R. Greene, Karol J. O’Donovan & Adrian Burns

  2. The TRIL Centre, Dublin, Ireland

    Barry R. Greene, Karol J. O’Donovan & Adrian Burns

  3. School of Physiotherapy and Performance Science, University College Dublin, Dublin, Ireland

    Denise McGrath

  4. TRIL Centre, University College Dublin, Dublin, Ireland

    Ross O’Neill

  5. CLARITY Centre for Sensor Web Technologies, School of Physiotherapy and Performance Science, University College Dublin, Dublin, Ireland

    Brian Caulfield

Authors
  1. Barry R. Greene
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Denise McGrath
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ross O’Neill
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Karol J. O’Donovan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adrian Burns
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Brian Caulfield
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Barry R. Greene.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Greene, B.R., McGrath, D., O’Neill, R. et al. An adaptive gyroscope-based algorithm for temporal gait analysis. Med Biol Eng Comput 48, 1251–1260 (2010). https://doi.org/10.1007/s11517-010-0692-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-010-0692-0

Keywords

Search

Navigation