Original Articles Open access

[The instrumental assessment of visuomotor coordination of stroke patients after neurorehabilitation]

Authors

Published
20 February 2025
Section
Original Articles
DOI
10.4081/gimle.545
Pages
19-23

Abstract

Instumented movement analysis allows to obtain quantitative and hence objective data about the mobility and the visuomotor coordination of patients with stroke. It also provides the possibility to predict the outcome of neurorehabilitation even in terms of identification of patients able to return to work. The wide amount of data provided by instrumental movement analysis could be an opportunity on one hand, but also required a great computational effort on the other hand. The development of artificial neural network could help clinicians in managing these data. In this study we analysed the visuomotor coordination of 16 patients with stroke and 18 age-matched healthy subjects during a task separately performed by each upper limb and assessing the asymmetry index for each one of the parameters extracted by an inertial measurement unit placed on the dorsal part of the hand. The artificial neural network showed an accuracy of 94.1% in identifying the subjects able to work. The combined used of light wearable sensors developed for human movement analysis and artificial neural networks could provide an accurate prognosis of the possibility to return to work of patients with stroke after neurorehabilitation and probably also of the monitoring of working activities in terms of ergonomics.

References

  1. 1) losa M, Mazzà C, Frusciante R, et al. Mobility assessment of patients with facioscapulohumeral dystrophy. Clin Biomech (Bristol, Avon). 2007;22(10):1074-823. DOI: https://doi.org/10.1016/j.clinbiomech.2007.07.013
  2. 2) Winairuk T, Pang MYC, Saengsirisuwan V, et al. Comparison of measurement properties of three shortened versions of the balance evaluation system test (BESTest) in people with subacute stroke. J Rehabil Med. 2019;51(9):683-691.3. DOI: https://doi.org/10.2340/16501977-2589
  3. 3) Morone G, Paolucci S, losa M. In What Daily Activities Do Patients Achieve Independence after Stroke? J Stroke Cerebrovasc Dis. 2015;24(8):1931-7. DOI: https://doi.org/10.1016/j.jstrokecerebrovasdis.2015.05.006
  4. 4) losa M, Bini F, Marinozzi F, et al. Stability and Harmony of Gait in Patients with Subacute Stroke. J Med Biol Eng. 2016;36(5):635-643. 5) losa, Capodaglio, Pelà, et al. Artificial Neural Network analyzing wearable device gait data for identifying patients with stroke unable to return to work. Front Neurol 2021.
  5. 6) Cappozzo A, Della Croce U, Leardini A, Chiari L. Human movement analysis using stereophotogrammetry. Part 1: theoretical background. Gait Posture. 2005;21(2): 186-96. DOI: https://doi.org/10.1016/S0966-6362(04)00025-6
  6. 7) losa M, Picerno P, Paolucci S, Morone G. Wearable inertial sensors for human movement analysis. Expert Rev Med Devices.
  7. 2016;13(7):641-59.
  8. 8) Bastiaansen BJC, Wilmes E, Brink MS, et al. An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field. J Vis Exp. 2020;159. DOI: https://doi.org/10.3791/60857
  9. 9) Lind CM, Diaz-Olivares JA, Lindecrantz K, Eklund J. A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback. Sensors (Basel). 2020;20(21):6010. DOI: https://doi.org/10.3390/s20216010
  10. 10) losa M, Bini F, Marinozzi F, et al. Stability and Harmony of Gait in Patients with Subacute Stroke. J Med Biol Eng. 2016;36(5):635-643. 11) Moon S, Ahmadnezhad P, Song HJ, et al. Artificial neural networks in neurorehabilitation: A scoping review. NeuroRehabilitation. 2020;46(3):259-269. DOI: https://doi.org/10.3233/NRE-192996
  11. 12) losa M, Paradisi F, Brunelli S, et al. Assessment of gait stability, harmony, and symmetry in subjects with lower-limb amputation evaluated by trunk accelerations. J Rehabil Res Dev. 2014;5 1(4):623-34. 13) losa M, Ghanbari Ghooshchy S, Morone G, et al. Visuomotor Integration for Coupled Hand Movements in Healthy Subjects and Patients With Stroke. Front Bioeng Biotechnol. 2020;8:591. DOI: https://doi.org/10.1682/JRRD.2013.07.0162
  12. 14) Ghanbari Ghooshchy S, losa M, Ciancarelli I, et al. Sensorized assessment of bilateral hand movements in patients with stroke driven by rhythmic auditory or visual-auditory stimulation. J Biol Regul Homeost Agents. 2020;34(5 Suppl. 3):53-58. Technology in Medicine.
  13. 15) Ghanbari Ghoshchi S, De Angelis S, Morone G, et al. Return to Work and Quality of Life after Stroke in Italy: A Study on the Efficacy of Technologically Assisted Neurorehabilitation. Int J Environ Res Public Health. 2020;17(14):5233. DOI: https://doi.org/10.3390/ijerph17145233
  14. 16) Burdack J, Horst F, Giesselbach S, et al. Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning. Front Bioeng Biotechnol. 2020;8:260. DOI: https://doi.org/10.3389/fbioe.2020.00260
  15. 17) Wang FC, Chen SF, Lin CH, et al. Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units. Sensors (Basel). 2021;21(5):1864. DOI: https://doi.org/10.3390/s21051864
  16. 18) Tani N, Ichikawa F, Mitani R, et al. Fitting the task to a person with disabilities: A case of return-to-work support for a patient due to leftsided poststroke hemiplegia using tailor-made jigs-and-tools. J Occup Health. 2021;63(1):e12201. DOI: https://doi.org/10.1002/1348-9585.12201
  17. 19) Zhang S, Cheng S, Zhang Z, et al. Related risk factors associated with post-stroke fatigue: a systematic review and meta-analysis, Neurol. Sci., 2021;42(4):1463-1471. DOI: https://doi.org/10.1007/s10072-020-04633-w
  18. 20) Payton H, Soundy A. The Experience of Post-Stroke Pain and The Impact on Quality of Life: An Integrative Review, Behav. Sci. (Basel). 2020;10(8):128. DOI: https://doi.org/10.3390/bs10080128
  19. 21) Hester T, Sherrill DM, Hamel M, et al. Identification of tasks performed by stroke patients using a mobility assistive device. Conf Proc IEEE Eng Med Biol Soc. 2006;2006:1501-4. DOI: https://doi.org/10.1109/IEMBS.2006.259437
  20. 22) Hu B, Kim C, Ning X, Xu X. Using a deep learning network to recognise low back pain in static standing. Ergonomics. 2018;61(10):1374-1381. DOI: https://doi.org/10.1080/00140139.2018.1481230

Metrics

Citations and online attention from Dimensions, Altmetric and PlumX.

PlumX Metrics

License

Copyright (c) 2021 The Author(s)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.

Most read articles by the same author(s)

1 2 > >>