CoChat
Sign Up
Cited by 56Gait Recognition and AnalysisBalance, Gait, and Falls PreventionLower Extremity Biomechanics and Pathologies

Accuracy Verification of Spatio-Temporal and Kinematic Parameters for Gait Using Inertial Measurement Unit System

Open access
Yeo, Sang Seok, Park, Ga Young·2020·Sensors
DOI 10.3390/s20051343PMC PMC7085570
Open this paper in CoChat — free accountOpen PDF

Abstract

Inertial measurement unit systems are wearable sensors that can measure the movement of a human in real-time with relatively little space and high portability. The purpose of this study was to investigate the accuracy of the inertial measurement unit (IMU) system for gait analysis by comparing it with measurements obtained using an optical motion capture (OMC) system. To compare the accuracies of these two different motion capture systems, the Spatio-temporal and kinematic parameters were measured in young adults during normal walking. Thirty healthy participants participated in the study. Data were collected while walking 5 strides on a 7 m walkway at a self-selected speed. Results of gait analysis showed that the Spatio-temporal (stride time, stride length, cadence, step length) and kinematic (knee joint peak to peak of movement) parameters were not significantly different in the participant. Spatio-temporal and kinematic parameters of the two systems were compared using the Bland–Altman method. The results obtained showed that the measurements of Spatio-temporal and kinematic parameters of gait by the two systems were similar, which suggested that IMU and OMC systems could be used interchangeably for gait measurements. Therefore, gait analysis performed using the wearable IMU system might efficiently provide gait measurements and enable accurate analysis.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/s20051343

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Template-based methods aim to obtain the movement of the trunk or legs, focusing on dynamics of movement through space or spatiotemporal based methods.

    “Template-based methods aim to obtain the movement of the trunk or legs, i.e., they usually focus on the dynamics of movement through space or spatiotemporal based methods [124].”

    From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [124]

    The Methods section (2.1.1 and 2.1.3; page unavailable) describes sensors on the pelvis and legs measuring spatiotemporal and kinematic gait parameters. It does not identify this approach as template-based or establish what template-based methods generally aim to do. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 35 · Cited by 56

Research works printed but not linked: 32

  1. [1]Vachranukunkiet T. Esquenazi A. Pathophysiology of gait disturbance in neurologic disorders and clinical presentations Phys. Med. Rehabil. Clin. N. Am. 2013 24 233 246 10.1016/j.pmr.2012.11.010 23598260 · DOI
  2. [2]Yeo S.S. Cho I.H. Gait Characteristic in a Stroke Patient with an Intact Corticospinal Tract and Corticoreticular Pathway: A Case Study J. Korean Phys. Ther. 2018 30 73 77 10.18857/jkpt.2018.30.2.73 · DOI
  3. [3]Esquenazi A. Gait analysis in lower-limb amputation and prosthetic rehabilitation Phys. Med. Rehabil. Clin. N. Am. 2014 25 153 167 10.1016/j.pmr.2013.09.006 24287245 · DOI
  4. [4]Najafi B. Khan T. Wrobel J. Laboratory in a box: Wearable sensors and its advantages for gait analysis Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Boston, MA, USA 30 August–3 September 2011 6507 6510 22255829
  5. [6]Donath L. Faude O. Lichtenstein E. Nuesch C. Mundermann A. Validity and reliability of a portable gait analysis system for measuring spatiotemporal gait characteristics: Comparison to an instrumented treadmill J. Neuroeng. Rehabil. 2016 13 6 10.1186/s12984-016-0115-z 26790409 PMC4719749 · DOI
  6. [7]Gomez Bernal A. Becerro-de-Bengoa-Vallejo R. Losa-Iglesias M.E. Reliability of the OptoGait portable photoelectric cell system for the quantification of spatial-temporal parameters of gait in young adults Gait Posture 2016 50 196 200 10.1016/j.gaitpost.2016.08.035 27644096 · DOI
  7. [8]Tao W. Liu T. Zheng R. Feng H. Gait analysis using wearable sensors Sensors 2012 12 2255 2283 10.3390/s120202255 22438763 PMC3304165 · DOI
  8. [9]Liu T. Inoue Y. Shibata K. Development of a wearable sensor system for quantitative gait analysis Measurement 2009 42 978 988 10.1016/j.measurement.2009.02.002 · DOI
  9. [10]Lanovaz J.L. Oates A.R. Treen T.T. Unger J. Musselman K.E. Validation of a commercial inertial sensor system for spatiotemporal gait measurements in children Gait Posture 2017 51 14 19 10.1016/j.gaitpost.2016.09.021 27693956 · DOI
  10. [11]Sijobert B. Denys J. Coste C.A. Geny C. IMU based detection of freezing of gait and festination in Parkinson’s disease Proceedings of the 2014 IEEE 19th International Functional Electrical Stimulation Society Annual Conference (IFESS) Kuala Lumpur, Malaysia 17–19 September 2014 1 3
  11. [12]Margiotta N. Avitabile G. Coviello G. A wearable wireless system for gait analysis for early diagnosis of Alzheimer and Parkinson disease Proceedings of the 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA) Ras Al Khaimah, United Arab Emirates 6–8 December 2016 1 4
  12. [13]Cando O.A. Hidalgo K.R. Palacios B.C. A low-cost vibratory stimulus system to mitigate freezing of gait in Parkinson’s disease Proceedings of the 2016 IEEE ANDESCON Arequipa, Peru 19–21 October 2016 1 4
  13. [14]Glowinski S. Blazejewski A. Krzyzynski T. Inertial sensors and wavelets analysis as a tool for pathological gait identification Innovations in Biomedical Engineering Springer Berlin/Heidelberg, Germany 2017 106 114
  14. [15]Glowinski S. Blazejewski A. Krzyzynski T. Human gait feature detection using inertial sensors wavelets Wearable Robotics: Challenges and Trends Springer Berlin/Heidelberg, Germany 2017 397 401
  15. [16]Najafi B. Lee-Eng J. Wrobel J.S. Goebel R. Estimation of center of mass trajectory using wearable sensors during golf swing J. Sports Sci. Med. 2015 14 354 363 25983585 PMC4424465
  16. [17]Washabaugh E.P. Kalyanaraman T. Adamczyk P.G. Claflin E.S. Krishnan C. Validity and repeatability of inertial measurement units for measuring gait parameters Gait Posture 2017 55 87 93 10.1016/j.gaitpost.2017.04.013 28433867 PMC5507609 · DOI
  17. [18]Seel T. Raisch J. Schauer T. IMU-based joint angle measurement for gait analysis Sensors 2014 14 6891 6909 10.3390/s140406891 24743160 PMC4029684 · DOI
  18. [19]Lee M. Youm C. Jeon J. Cheon S.M. Park H. Validity of shoe-type inertial measurement units for Parkinson’s disease patients during treadmill walking J. Neuroeng. Rehabil. 2018 15 38 10.1186/s12984-018-0384-9 29764466 PMC5952468 · DOI
  19. [20]Yang S. Zhang J.-T. Novak A.C. Brouwer B. Li Q. Estimation of spatio-temporal parameters for post-stroke hemiparetic gait using inertial sensors Gait Posture 2013 37 354 358 10.1016/j.gaitpost.2012.07.032 23000235 · DOI
  20. [21]Najafi B. Helbostad J.L. Moe-Nilssen R. Zijlstra W. Aminian K. Does walking strategy in older people change as a function of walking distance? Gait Posture 2009 29 261 266 10.1016/j.gaitpost.2008.09.002 18952435 · DOI
  21. [23]Zhou H. Al-Ali F. Kang G. Hamad A. Ibrahim R. Talal T. Najafi B. Application of Wearables to Improve Uptake of Exercise Therapy during Hemodialysis Treatment for Reducing Depression Symptom—A Single Blinded Randomized Controlled Trial Med. Pharmacol. 2020 10.20944/preprints202001.0272.v1 · DOI
  22. [24]King S.L. Barton G.J. Ranganath L.R. Interpreting sources of variation in clinical gait analysis: A case study Gait Posture 2017 52 1 4 10.1016/j.gaitpost.2016.10.022 27842282 · DOI
  23. [25]McGinley J.L. Baker R. Wolfe R. Morris M.E. The reliability of three-dimensional kinematic gait measurements: A systematic review Gait Posture 2009 29 360 369 10.1016/j.gaitpost.2008.09.003 19013070 · DOI
  24. [26]Gill S.V. Walsh M.K. Pratt J.A. Toosizadeh N. Najafi B. Travison T.G. Changes in spatiotemporal gait patterns during flat ground walking and obstacle crossing 1 year after bariatric surgery Surg. Obes. Relat. Dis. 2016 12 1080 1085 10.1016/j.soard.2016.03.029 27320220 PMC4987194 · DOI
  25. [27]Chen C.L. Chen H.C. Tang S.F. Wu C.Y. Cheng P.T. Hong W.H. Gait performance with compensatory adaptations in stroke patients with different degrees of motor recovery Am. J. Phys. Med. Rehabil. 2003 82 925 935 10.1097/01.PHM.0000098040.13355.B5 14627929 · DOI
  26. [28]Muchna A. Najafi B. Wendel C.S. Schwenk M. Armstrong D.G. Mohler J.J. Foot problems in older adults: Associations with incident falls, frailty syndrome, and sensor-derived gait, balance, and physical activity measures J. Am. Podiatr. Med. Assoc. 2018 108 126 139 10.7547/15-186 28853612 PMC6647839 · DOI
  27. [29]Weidow J. Tranberg R. Saari T. Karrholm J. Hip and knee joint rotations differ between patients with medial and lateral knee osteoarthritis: Gait analysis of 30 patients and 15 controls J. Orthop. Res. 2006 24 1890 1899 10.1002/jor.20194 16838360 · DOI
  28. [30]Bland J.M. Altman D.G. Applying the right statistics: Analyses of measurement studies Ultrasound Obstet. Gynecol. 2003 22 85 93 10.1002/uog.122 12858311 · DOI
  29. [31]Bartlett J. Frost C. Reliability, repeatability and reproducibility: Analysis of measurement errors in continuous variables Ultrasound Obstet. Gynecol. 2008 31 466 475 10.1002/uog.5256 18306169 · DOI
  30. [32]Ko S.U. Stenholm S. Metter E.J. Ferrucci L. Age-associated gait patterns and the role of lower extremity strength-results from the Baltimore Longitudinal Study of Aging Arch. Gerontol. Geriatr. 2012 55 474 479 10.1016/j.archger.2012.04.004 22564361 PMC3415576 · DOI
  31. [33]Xu X. McGorry R.W. Chou L.S. Lin J.H. Chang C.C. Accuracy of the Microsoft Kinect for measuring gait parameters during treadmill walking Gait Posture 2015 42 145 151 10.1016/j.gaitpost.2015.05.002 26002604 · DOI
  32. [34]Wu G. Siegler S. Allard P. Kirtley C. Leardini A. Rosenbaum D. Whittle M. D’Lima D. Cristofolini L. Witte H. ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion—Part I: Ankle, hip, and spine J. Biomech. 2002 35 543 548 10.1016/S0021-9290(01)00222-6 11934426 · DOI

Authors
Yeo, S. S., & Park, G. Y.
Affiliations
Dankook University (KR)
Corresponding author
Park, Ga Young
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2020-02-29
Volume
20
Issue
5
Pages
1343
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s20051343
PMC
PMC7085570
OpenAlex
W3009247559
Cited by
56
References
35
Citations by year
2026: 9 · 2025: 7 · 2024: 8 · 2023: 7 · 2022: 8 · 2021: 13 · 2020: 4
FWCI
2.51
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Research Foundation
  • National Research Foundation of Korea (NRF-2018R1D1A1B07049510)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
  • Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
  • Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
Keywords
inertial measurement unit, wearable sensors, gait analysis, optical motion capture, measurement accuracy, spatiotemporal gait parameters, stride length, cadence, knee kinematics, normal walking
MeSH terms
Adult, Biomechanical Phenomena, Female, Gait, Humans, Knee Joint, Male, Young Adult, Spatio-Temporal Analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review