Skip to main content
Springer Nature Link
Log in

Correlation of Spatiotemporal and EMG Measures with Lower Extremity Fugl-Meyer Assessment Score in Post-Stroke Walking

  • Conference paper
  • First Online:
Robot 2023: Sixth Iberian Robotics Conference (ROBOT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 978))

Included in the following conference series:

  • 290 Accesses

Abstract

Lower Extremity Fugl-Meyer Assessment (FMA-LE) is recommended as the primary outcome for assessing motor function in post-stroke population. However, the subjectivity, dependency on professional experience, and time-consuming visual inspection by healthcare professionals limit the use of FMA-LE in clinical practice. Contrarily to clinical scales, sensor-based assessments can automatically provide objective measurements of motor function. This work advances literature by evaluating the Spearman correlation between the FMA-LE clinical scores and both spatiotemporal and electromyographic (EMG) measures, acquired during different mobility walking tasks (self-selected speed, maximum speed, maximum cadence, maximum step length, and maximum step height). Data were extracted from ARRA dataset, including 27 post-stroke participants. The results showed that step length (0.44 ≤ r ≤ 0.60), stride time (−0.48 ≤ r ≤ −0.40), and cadence (0.40 ≤ r ≤ 0.46) spatiotemporal measures, and peak power frequency (PKF) EMG measure of gluteus medius (r = 0.42), lateral hamstring (0.40 ≤ r ≤ 0.46), and vastus medialis (0.42 ≤ r ≤ 0.45) muscles revealed significant strong correlations in multiple walking tasks. Overall, spatiotemporal measures presented higher correlations with FMA-LE than EMG measures. These findings are promising for future research to develop artificial intelligence methods to estimate the Lower FMA clinical scores for motor assessment, maximizing its use in clinical practice.

This work was funded by the Fundação para a Ciência e Tecnologia under the scholarship reference 2020.05709.BD, under the Stimulus of Scientific Employment with the grant 2020.03393. CEECIND, under the national support to R&D units grant through the reference project UIDB/04436/2020 and UIDP/04436/2020.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Luengo-Fernandez, R., Leal, J., Candio, P., Violato, M.: The economic impact of stroke in Europe

    Google Scholar 

  2. Johnson, C.O., Nguyen, M., Roth, G.A., et al.: Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the global burden of disease Study 2016. Lancet Neurol. 18, 439–458 (2019). https://doi.org/10.1016/S1474-4422(19)30034-1

    Article  Google Scholar 

  3. Li, S., Francisco, G.E., Zhou, P.: Post-stroke hemiplegic gait: new perspective and insights. Front. Physiol. 9, 1–8 (2018). https://doi.org/10.3389/fphys.2018.01021

    Article  Google Scholar 

  4. Béjot, Y., Bailly, H., Durier, J., Giroud, M.: Epidemiology of stroke in Europe and trends for the 21st century. Presse Med. 45, e391–e398 (2016). https://doi.org/10.1016/j.lpm.2016.10.003

    Article  Google Scholar 

  5. (2017) Burden of stroke report launched in EU parliament. In: SAFE. https://strokeeurope.eu/burden-of-stroke-report-launched-in-eu-parliament/. Accessed 27 May 2019

  6. Goldman, R.: The effects of stroke on the body. In: Healthline (2017). https://www.healthline.com/health/stroke/effects-on-body#1. Accessed 19 Feb 2019

  7. Quinn, T., Harrison, M.: Assessment scales in stroke: clinimetric and clinical considerations. Clin. Interv. Aging. 201–211 (2013).https://doi.org/10.2147/CIA.S32405

  8. Pinheiro, C., Figueiredo, J., Cerqueira, J., Santos, C.P.: Robotic biofeedback for post-stroke gait rehabilitation: a scoping review. Sensors 22, 7197 (2022). https://doi.org/10.3390/s22197197

    Article  Google Scholar 

  9. Bushnell, C., Bettger, J.P., Cockroft, K.M., et al.: Chronic stroke outcome measures for motor function intervention trials. Circ. Cardiovasc. Qual. Outcomes 8, S163–S169 (2015). https://doi.org/10.1161/CIRCOUTCOMES.115.002098

    Article  Google Scholar 

  10. Duncan, P.W., Propst, M., Nelson, S.G.: Reliability of the Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident. Phys. Ther. 63, 1606–1610 (1983). https://doi.org/10.1093/ptj/63.10.1606

    Article  Google Scholar 

  11. Sanford, J., Moreland, J., Swanson, L.R., et al.: Reliability of the Fugl-Meyer assessment for testing motor performance in patients following stroke. Phys. Ther. 73, 447–454 (1993). https://doi.org/10.1093/ptj/73.7.447

    Article  Google Scholar 

  12. Sullivan, K.J., Tilson, J.K., Cen, S.Y., et al.: Fugl-Meyer assessment of sensorimotor function after stroke. Stroke 42, 427–432 (2011). https://doi.org/10.1161/STROKEAHA.110.592766

    Article  Google Scholar 

  13. Siniscalchi, A.: Use of stroke scales in clinical practice: current concepts. Turkish J. Emerg. Med. 22, 119 (2022). https://doi.org/10.4103/2452-2473.348440

    Article  Google Scholar 

  14. Routson, R.L, Kautz, S.A., Neptune, R.R.: Modular organization across changing task demands in healthy and poststroke gait. Physiol. Rep. 2(6), e12055 (2014). https://doi.org/10.14814/phy2.12055

  15. Julianjatsono, R., Ferdiana, R., Hartanto, R.: High-resolution automated Fugl-Meyer assessment using sensor data and regression model. In: 2017 3rd International Conference on Science and Technology - Computer (ICST). IEEE, pp. 28–32 (2017)

    Google Scholar 

  16. Gebruers, N., Truijen, S., Engelborghs, S., De Deyn, P.P.: Prediction of upper limb recovery, general disability, and rehabilitation status by activity measurements assessed by accelerometers or the Fugl-Meyer score in acute stroke. Am. J. Phys. Med. Rehabil. 93, 245–252 (2014). https://doi.org/10.1097/PHM.0000000000000045

    Article  Google Scholar 

  17. Song, X., Chen, S., Jia, J., Shull, P.B.: Cellphone-based automated Fugl-Meyer assessment to evaluate upper extremity motor function after stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 2186–2195 (2019). https://doi.org/10.1109/TNSRE.2019.2939587

    Article  Google Scholar 

  18. Tozlu, C., Edwards, D., Boes, A., et al.: Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke. Neurorehabil. Neural Repair 34, 428–439 (2020). https://doi.org/10.1177/1545968320909796

    Article  Google Scholar 

  19. Rech, K.D., Salazar, A.P., Marchese, R.R., et al.: Fugl-Meyer assessment scores are related with kinematic measures in people with chronic hemiparesis after stroke. J. Stroke Cerebrovasc. Dis. 29, 104463 (2020). https://doi.org/10.1016/j.jstrokecerebrovasdis.2019.104463

    Article  Google Scholar 

  20. Lin, S.-I.: Motor function and joint position sense in relation to gait performance in chronic stroke patients. Arch. Phys. Med. Rehabil. 86, 197–203 (2005). https://doi.org/10.1016/j.apmr.2004.05.009

    Article  Google Scholar 

  21. Kautz, S.A, Neptune, R.R.: Medical university of South Carolina stroke data (ARRA). In: ICPSR (2018). https://www.icpsr.umich.edu/web/ICPSR/studies/37122

  22. Sanchez, N.: Stroke initiative for gait data evaluation (STRIDE), United States, 2012–2020. In: ICPSR (2021). https://www.icpsr.umich.edu/web/ICPSR/studies/38002

  23. Kwong, P.W.H., Ng, S.S.M.: Cutoff score of the lower-extremity motor subscale of Fugl-Meyer assessment in chronic stroke survivors: a cross-sectional study. Arch. Phys. Med. Rehabil. 100, 1782–1787 (2019). https://doi.org/10.1016/j.apmr.2019.01.027

    Article  Google Scholar 

  24. Oskoei, M.A., Huosheng, H.: Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans. Biomed. Eng. 55, 1956–1965 (2008). https://doi.org/10.1109/TBME.2008.919734

    Article  Google Scholar 

  25. Perry, J.: Gait Analysis: Normal and Pathological Function. SLACK Incorporated, West Deptford (1992)

    Google Scholar 

  26. Dancey, C.P., Reidy, J.: Statistics without Maths for psychology. Pearson education, London (2007)

    Google Scholar 

  27. Guzik, A., Drużbicki, M., Kwolek, A., et al.: Analysis of the association between selected factors and outcomes of treadmill gait training with biofeedback in patients with chronic stroke. J. Back Musculoskelet. Rehabil. 33, 159–168 (2020). https://doi.org/10.3233/BMR-170991

    Article  Google Scholar 

  28. Srivastava, S., Patten, C., Kautz, S.A.: Altered muscle activation patterns (AMAP): an analytical tool to compare muscle activity patterns of hemiparetic gait with a normative profile. J. Neuroeng. Rehabil. 16, 21 (2019). https://doi.org/10.1186/s12984-019-0487-y

    Article  Google Scholar 

  29. Hussain, I., Park, S.-J.: Prediction of myoelectric biomarkers in post-stroke gait. Sensors 21, 5334 (2021). https://doi.org/10.3390/s21165334

    Article  Google Scholar 

  30. Wang, Y., Mukaino, M., Ohtsuka, K., et al.: Gait characteristics of post-stroke hemiparetic patients with different walking speeds. Int. J. Rehabil. Res. 43, 69–75 (2020). https://doi.org/10.1097/MRR.0000000000000391

    Article  Google Scholar 

  31. Xu, Y., Shu, X., Sheng, X., et al.: Assessment of sEMG Performance and its Correlation with Upper Fugl-Meyer Assessment in Stroke Patients. In: 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). IEEE, pp. 522–527

    Google Scholar 

  32. Ward, N.J., Farmer, S.F., Berthouze, L., Halliday, D.M.: Rectification of EMG in low force contractions improves detection of motor unit coherence in the beta-frequency band. J. Neurophysiol. 110, 1744–1750 (2013). https://doi.org/10.1152/jn.00296.2013

    Article  Google Scholar 

Download references

Acknowledgments

The results published here are based on data obtained from Steven A. Kautz and Richard R. Neptune’s Dataset [21]: Medical University of South Carolina Stroke Data (ARRA) (ICPSR 37122).

Author information

Authors and Affiliations

  1. Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058, Guimarães, Portugal

    Cristiana Pinheiro, Luís Abreu, Joana Figueiredo & Cristina P. Santos

  2. LABBELS Associative Laboratory, University of Minho, Braga/Guimarães, Portugal

    Joana Figueiredo & Cristina P. Santos

Authors
  1. Cristiana Pinheiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luís Abreu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joana Figueiredo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cristina P. Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joana Figueiredo .

Editor information

Editors and Affiliations

  1. Dep. de Eng. Electrotecnica e de Computadores, University of Coimbra, Coimbra, Portugal

    Lino Marques

  2. Department of Electrónica Industrial, University of Minho, Escola de Engenharia, Guimarães, Portugal

    Cristina Santos

  3. Department of Electrical Engineering, ESTIG, Polytechnic Institute of Bragança, Bragança, Portugal

    José Luís Lima

  4. Centro Universitario de la Defensa, Zaragoza, Spain

    Danilo Tardioli

  5. Centre for Automation and Robotics UPM-CSIC, Universidad Politécnica de Madrid, Madrid, Spain

    Manuel Ferre

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pinheiro, C., Abreu, L., Figueiredo, J., Santos, C.P. (2024). Correlation of Spatiotemporal and EMG Measures with Lower Extremity Fugl-Meyer Assessment Score in Post-Stroke Walking. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-031-59167-9_35

Download citation

Publish with us

Policies and ethics