The development of sensor-based gait training system for locomotive syndrome: The effect of real-time gait feature feedback on gait pattern during treadmill walking

Hiroyuki Honda, Yoshiyuki Kobayashi, Akihiko Murai, Hiroshi Fujimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The concept of locomotive syndrome was proposed by the Japanese Orthopedic Association; it typifies the condition of reduced mobility resulting from a locomotive organ disorder related to aging. Although several sensor-based gait training systems, which can feedback the gait features in real-time, have been developed for various musculoskeletal disorders, there are no such systems for locomotive syndrome. In this study, we reported how real-time locomotive syndrome related gait feature feedback effects on gait patterns during treadmill walking. 18 healthy participants were assigned into either intervention- or control-group. During 4 sessions (training-session, pre-intervention-session, intervention-session, and post-intervention-session), gait patterns were measured by a motion-capture system. During the intervention-session of the intervention-group, participants received LS-risk-scores made in this study. Meanwhile, they were asked to minimize the LS-risk-scores by modifying their knee joint motion. A two-way-repeated measure ANOVA was conducted on the LS-risk-scores to examine effects of the intervention. When interaction was found, paired t-tests were conducted on the LS-risk-scores and knee angles between the sessions respectively. As a result, the LS-risk-scores were significantly smaller (p < 0.05) during the post-intervention-session than the pre-intervention-session in the intervention-group. There were no significant differences on the LS-risk-scores between the sessions in the control-group. Further, in the intervention-group, significant differences (p < 0.05) were found between the sessions on the knee angles partially. There were no significant differences between the sessions on the knee angles in the control-group. These results indicate that people can alter their gait pattern if the LS-risk-scores are feedback in real-time.

LanguageEnglish
Title of host publicationProceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) - Volume I
Subtitle of host publicationHealthcare Ergonomics
EditorsSebastiano Bagnara, Yushi Fujita, Riccardo Tartaglia, Sara Albolino, Thomas Alexander
PublisherSpringer-Verlag
Pages305-311
Number of pages7
ISBN (Print)9783319960975
DOIs
Publication statusPublished - 2019 Jan 1
Event20th Congress of the International Ergonomics Association, IEA 2018 - Florence, Italy
Duration: 2018 Aug 262018 Aug 30

Publication series

NameAdvances in Intelligent Systems and Computing
Volume818
ISSN (Print)2194-5357

Other

Other20th Congress of the International Ergonomics Association, IEA 2018
CountryItaly
CityFlorence
Period18/8/2618/8/30

Fingerprint

Exercise equipment
Locomotives
Feedback
Sensors
Orthopedics
Analysis of variance (ANOVA)
Aging of materials

Keywords

  • Gait training
  • Locomotive syndrome
  • Real-time visual feedback

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Honda, H., Kobayashi, Y., Murai, A., & Fujimoto, H. (2019). The development of sensor-based gait training system for locomotive syndrome: The effect of real-time gait feature feedback on gait pattern during treadmill walking. In S. Bagnara, Y. Fujita, R. Tartaglia, S. Albolino, & T. Alexander (Eds.), Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) - Volume I: Healthcare Ergonomics (pp. 305-311). (Advances in Intelligent Systems and Computing; Vol. 818). Springer-Verlag. https://doi.org/10.1007/978-3-319-96098-2_40

The development of sensor-based gait training system for locomotive syndrome : The effect of real-time gait feature feedback on gait pattern during treadmill walking. / Honda, Hiroyuki; Kobayashi, Yoshiyuki; Murai, Akihiko; Fujimoto, Hiroshi.

Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) - Volume I: Healthcare Ergonomics. ed. / Sebastiano Bagnara; Yushi Fujita; Riccardo Tartaglia; Sara Albolino; Thomas Alexander. Springer-Verlag, 2019. p. 305-311 (Advances in Intelligent Systems and Computing; Vol. 818).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Honda, H, Kobayashi, Y, Murai, A & Fujimoto, H 2019, The development of sensor-based gait training system for locomotive syndrome: The effect of real-time gait feature feedback on gait pattern during treadmill walking. in S Bagnara, Y Fujita, R Tartaglia, S Albolino & T Alexander (eds), Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) - Volume I: Healthcare Ergonomics. Advances in Intelligent Systems and Computing, vol. 818, Springer-Verlag, pp. 305-311, 20th Congress of the International Ergonomics Association, IEA 2018, Florence, Italy, 18/8/26. https://doi.org/10.1007/978-3-319-96098-2_40
Honda H, Kobayashi Y, Murai A, Fujimoto H. The development of sensor-based gait training system for locomotive syndrome: The effect of real-time gait feature feedback on gait pattern during treadmill walking. In Bagnara S, Fujita Y, Tartaglia R, Albolino S, Alexander T, editors, Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) - Volume I: Healthcare Ergonomics. Springer-Verlag. 2019. p. 305-311. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-96098-2_40
Honda, Hiroyuki ; Kobayashi, Yoshiyuki ; Murai, Akihiko ; Fujimoto, Hiroshi. / The development of sensor-based gait training system for locomotive syndrome : The effect of real-time gait feature feedback on gait pattern during treadmill walking. Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) - Volume I: Healthcare Ergonomics. editor / Sebastiano Bagnara ; Yushi Fujita ; Riccardo Tartaglia ; Sara Albolino ; Thomas Alexander. Springer-Verlag, 2019. pp. 305-311 (Advances in Intelligent Systems and Computing).
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