access icon openaccess BioKin: an ambulatory platform for gait kinematic and feature assessment

A platform to move gait analysis, which is normally restricted to a clinical environment in a well-equipped gait laboratory, into an ambulatory system, potentially in non-clinical settings is introduced. This novel system can provide functional measurements to guide therapeutic interventions for people requiring rehabilitation with limited access to such gait laboratories. BioKin system consists of three layers: a low-cost wearable wireless motion capture sensor, data collection and storage engine, and the motion analysis and visualisation platform. Moreover, a novel limb orientation estimation algorithm is implemented in the motion analysis platform. The performance of the orientation estimation algorithm is validated against the orientation results from a commercial optical motion analysis system and an instrumented treadmill. The study results demonstrate a root-mean-square error less than 4° and a correlation coefficient more than 0.95 when compared with the industry standard system. These results indicate that the proposed motion analysis platform is a potential addition to existing gait laboratories in order to facilitate gait analysis in remote locations.

Inspec keywords: kinematics; wireless sensor networks; motion measurement; motion estimation; medical image processing; patient rehabilitation; image motion analysis; gait analysis

Other keywords: BioKin system; correlation coefficient; ambulatory platform; low-cost wearable wireless motion capture sensor; feature assessment; gait kinematic; motion analysis platform; root-mean-square error; limb orientation estimation algorithm; data collection; instrumented treadmill; storage engine; gait analysis; visualisation platform

Subjects: Wireless sensor networks; Optical, image and video signal processing; Sensing devices and transducers; Patient care and treatment; Biology and medical computing; Spatial variables measurement; Spatial variables measurement; Computer vision and image processing techniques; Patient care and treatment; Physics of body movements

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
      • 32. Reiss, A., Stricker, D.: ‘Introducing a new benchmarked dataset for activity monitoring’. The 16th IEEE Int. Symp. on Wearable Computers, Newcastle, UK, June 2012.
    16. 16)
      • 31. Madgwick, S., Harrison, A.J.L., Vaidyanathan, R.: ‘Estimation of IMU and MARG orientation using a gradient descent algorithm’. Singapore, August 2011 IEEE Int. Conf. on Rehabilitation Robotics (ICORR), Singapore, August 2011, pp. 17.
    17. 17)
    18. 18)
      • 4. Lee, E.H., Goh, J.C., Bose, K.: ‘Value of gait analysis in the assessment of surgery in cerebral palsy’, Arch. Phys. Med. Rehabil., 1992, 73, (7), pp. 642646.
    19. 19)
    20. 20)
      • 1. Whittle, M.W.: ‘Gait analysis: an introduction’ (Butterworth-Heinemann, Oxford, UK, 2007, 4th edn.).
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
      • 30. 3D Optical Motion Capture. Available at: http://www.vicon.com.
    29. 29)
      • 8. DeLisa, J., Kerrigan, C.: ‘Gait analysis in the science of rehabilitation’ (Diane Publishing Company, Collingdale, USA, 2000).
    30. 30)
    31. 31)
      • 33. Dixon, L.C.W.: ‘Nonlinear optimization’ (English Universities Press, 1972).
    32. 32)
    33. 33)
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2014.0094
Loading

Related content

content/journals/10.1049/htl.2014.0094
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading