Biomedical Engineering Letters

, Volume 7, Issue 4, pp 287–298 | Cite as

Real-time biofeedback device for gait rehabilitation of post-stroke patients

  • I-Hung Khoo
  • Panadda Marayong
  • Vennila Krishnan
  • Michael Balagtas
  • Omar Rojas
  • Katherine Leyba
Original Article


In this work, we develop a device, called ‘Walk-Even’, that can provide real-time feedback to correct gait asymmetry commonly exhibited in post-stroke survivors and persons with certain neurological disorders. The device computes gait parameters, including gait time, swing time, and stance time of each leg, to detect gait asymmetry and provide corresponding real-time biofeedback by means of auditory and electrotactile stimulation to actively correct the user’s gait. The system consists of customized force-sensor-embedded insoles adjustable to fit any shoe size, electrotactile and auditory feedback circuits, microcontroller, and wireless XBee transceivers. The device also offers data saving capability. To validate its accuracy and reliability, we compared the gait measurements from our device with a commercial gait and balance assessment device, Zeno Walkway. The results show good correlation and agreement in a validity study with six healthy subjects and reliability study with seventeen healthy subjects. In addition, preliminary testing on six post-stroke patients after an 8-week training shows that the Walk-Even device helps to improve gait symmetry, foot pressure and forefoot loading of the affected side. Thus, initial testing indicates that the device is accurate in measuring the gait parameters and effective in improving gait symmetry using real-time feedback. The device is portable and low cost and has the potential for use in a non-clinical setting for patients that can walk independently without assistance. A more extensive testing with stroke patients is still ongoing.


Biofeedback Gait asymmetry Rehabilitation Stroke 



The authors thank Kristin DeMars, Amber Duong, Alyssa Matheson, Rae Rivera, and Javier Plazola for their help with the data collection and Gerardo Bocanegra and Kristi Yoshikawa for initial device development. We also acknowledge the internal grant support from CSU Long Beach Multidisciplinary grant.

Compliance with ethical standards

Conflict of interest

The authors (I-Hung Khoo, Panadda Marayong, Vennila Krishnan, Michael Balagtas, Omar Rojas, Katherine Leyba) declare that they have no conflict of interests in relation to the work in this article.

Human and animal rights

Approval was obtained from the CSULB Institutional Review Board for the experiment involving human subjects.


  1. 1.
    Mun BM, Kim TH, Lee JH, Lim JY, Seo DK, Lee DJ. Comparison of gait aspects according to FES stimulation position applied to stroke patients. J Phys Ther Sci. 2014;26:563–6.CrossRefGoogle Scholar
  2. 2.
    De Quervain IA, Simon SR, Leurgans S, Pease WS, McAllister D. Gait pattern in the early recovery period after stroke. J Bone Joint Surg Am. 1996;78:1506–14.CrossRefGoogle Scholar
  3. 3.
    Awad LN, Palmer JA, Pohlig RT, Binder-Macleod SA, Reisman DS. Walking speed and step length asymmetry modify the energy cost of walking after stroke. Neurorehabil Neural Repair. 2015;29:416–23.CrossRefGoogle Scholar
  4. 4.
    Eng JJ, Tang PF. Gait training strategies to optimize walking ability in people with stroke: a synthesis of the evidence. Expert Rev Neurother. 2007;7:1417–36.CrossRefGoogle Scholar
  5. 5.
    Kim SY, Yang L, Park IJ, Kim EJ, Park MS, You SH, Kim YH, Ko HY, Shin YI. Correction to “Effects of Innovative WALKBOT Robotic-Assisted Locomotor Training on Balance and Gait Recovery in Hemiparetic Stroke: a Prospective, Randomized, Experimenter Blinded Case Control Study With a Four-Week Follow-Up”. IEEE Trans Neural Syst Rehabil Eng. 2015;23:1128.CrossRefGoogle Scholar
  6. 6.
    Murray SA, Ha KH, Hartigan C, Goldfarb M. An assistive control approach for a lower-limb exoskeleton to facilitate recovery of walking following stroke. IEEE Trans Neural Syst Rehabil Eng. 2015;23:441–9.Google Scholar
  7. 7.
    Srivastava S, Kao PC, Kim SH, Stegall P, Zanotto D, Higginson JS, Agrawal SK, Scholz JP. Assist-as-needed robot-aided gait training improves walking function in individuals following stroke. IEEE Trans Neural Syst Rehabil Eng. 2015;23:956–63.CrossRefGoogle Scholar
  8. 8.
    Koenig A, Novak D, Omlin X, Pulfer M, Perreault E, Zimmerli L, Mihelj M, Riener R. Real-time closed-loop control of cognitive load in neurological patients during robot-assisted gait training. IEEE Trans Neural Syst Rehabil Eng. 2011;19:453–64.CrossRefGoogle Scholar
  9. 9.
    Banala SK, Kim SH, Agrawal SK, Scholz JP. Robot assisted gait training with active leg exoskeleton (ALEX). IEEE Trans Neural Syst Rehabil Eng. 2009;17:2–8.CrossRefGoogle Scholar
  10. 10.
    Lunenburger L, Colombo G, Riener R. Biofeedback for robotic gait rehabilitation. J Neuroeng Rehabil. 2007;4:1.CrossRefGoogle Scholar
  11. 11.
    Redd CB, Bamberg SJM. A wireless sensory feedback system for real-time gait modification. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2011. p. 1507–10.Google Scholar
  12. 12.
    Muto T, Herzberger B, Hermsdoerfer J, Miyake Y, Poeppel E. Interactive cueing with walk-mate for hemiparetic stroke rehabilitation. J Neuroeng Rehabil. 2012;9:1.CrossRefGoogle Scholar
  13. 13.
    Bamberg SJM, Benbasat AY, Scarborough DM, Krebs DE, Paradiso JA. Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans Inf Technol Biomed. 2008;12:413–23.CrossRefGoogle Scholar
  14. 14.
    Pappas IPI, Keller T, Mangold S, Popovic MR, Dietz V, Morari M. A reliable gyroscope-based gait-phase detection sensor embedded in a shoe insole. IEEE Sens J. 2004;4:268–74.CrossRefGoogle Scholar
  15. 15.
    Howell AM, Kobayashi T, Hayes HA, Foreman KB, Bamberg SJM. Kinetic gait analysis using a low-cost insole. IEEE Trans Biomed Eng. 2013;60:3284–90.CrossRefGoogle Scholar
  16. 16.
    Bogataj U, Gros N, Kljajic M, Acimovic-Janezic R. Enhanced rehabilitation of gait after stroke: a case report of a therapeutic approach using multichannel functional electrical stimulation. IEEE Transa Rehabil Eng. 1997;5:221–32.CrossRefGoogle Scholar
  17. 17.
    Ambrosini E, Ferrante S, Ferrigno G, Molteni F, Pedrocchi A. Cycling induced by electrical stimulation improves muscle activation and symmetry during pedaling in hemiparetic patients. IEEE Trans Neural Syst Rehabil Eng. 2012;20:320–30.CrossRefGoogle Scholar
  18. 18.
    Kafri M, Laufer Y. Therapeutic effects of functional electrical stimulation on gait in individuals post-stroke. Ann Biomed Eng. 2015;43:451–66.CrossRefGoogle Scholar
  19. 19.
    Klose KJ, Jacobs PL, Broton JG, Guest RS, Needham-Shropshire B, Lebwohl N, Nash MS, Green BA. Evaluation of a training program for persons with SCI paraplegia using the Parastep® 1 ambulation system: part 1. Ambulation performance and anthropometric measures. Arch Phys Med Rehabil. 1997;78:789–93.CrossRefGoogle Scholar
  20. 20.
    Afzal MR, Oh MK, Lee CH, Park YS, Yoon J. A Portable gait asymmetry rehabilitation system for individuals with stroke using a vibrotactile feedback. Biomed Res Int. 2015;2015:375638.CrossRefGoogle Scholar
  21. 21.
    Khoo IH, Marayong P, Krishnan V, Balagtas MN, Rojas O. Design of a biofeedback device for gait rehabilitation in post-stroke patients. In: 2015 IEEE 58th international midwest symposium on circuits and systems (MWSCAS). 2015. p. 1–4.Google Scholar
  22. 22.
    American Imex. BioTENS 2 datasheet.
  23. 23.
    Krishnan V, Khoo I, Marayong P, DeMars K, Cormack J. Gait training in chronic stroke using Walk-Even feedback device: a pilot study. Neurosci J. 2016; 2016.Google Scholar

Copyright information

© Korean Society of Medical and Biological Engineering and Springer 2017

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentCalifornia State UniversityLong BeachUSA
  2. 2.Mechanical and Aerospace Engineering DepartmentCalifornia State UniversityLong BeachUSA
  3. 3.Physical Therapy DepartmentCalifornia State UniversityLong BeachUSA

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