Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks

• Autorzy: Prokopowicz P. , Mikołajewski D. , Tyburek K. , Mikołajewska E.

Identyfikatory

DOI

10.24425/bpasts.2020.131843

• Języki publikacji: EN

Abstrakty

EN

Computational gait analysis constitutes a useful tool for quantitative assessment of gait disturbances, improving functional diagnosis, assessment of treatment planning, and monitoring of disease progress. There is little research on use of computational gait analysis in neurorehabilitation of post-stroke survivors, but current evidence on its clinical application supports a favorable cost-benefit ratio. The research was conducted among 50 adult people: 25 of them after ischemic stroke constituted the study group, and 25 healthy volunteers constituted the reference group. Study group members were treated for 2 weeks (10 neurorehabilitation sessions). Spatio-temporal gait parameters were assessed before and after therapy and compared using a novel fuzzy-based assessment tool, fractal dimension measurement and gait classification based on artificial neural networks. Measured results of rehabilitation (changes of gait parameters) were statistically relevant and reflected recovery. There is good evidence to extend its use to patients with various gait diseases undergoing neurorehabilitation. However, methodology for properly conducting and interpreting the proposed assessment and analysis procedures, providing validity and reliability of their results remains a key issue. More objective clinical reasoning, based on proposed novel tools, requires further research.

Słowa kluczowe

EN

computational analysis; spatio-temporal gait parameters; fuzzy analysis; gait classification; disorder recognition

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2020
• Tom: Vol. 68, nr 2
• Strony: 191--198
• Opis fizyczny: Bibliogr. 34 poz. rys., tab.

Twórcy

autor

Prokopowicz P.

• Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Kopernika 1, 85-074 Bydgoszcz, Poland

autor

Mikołajewski D.

• Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Kopernika 1, 85-074 Bydgoszcz, Poland
• Neurocognitive Laboratory, Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Toruń, Poland

autor

Tyburek K.

• Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Kopernika 1, 85-074 Bydgoszcz, Poland

autor

Mikołajewska E.

• Department of Physiotherapy, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Poland

Bibliografia

• [1] P. Klimkiewicz, A. Kubsik and M. Woldańska-Okońska, “NDT-Bobath method used in the rehabilitation of patients with a history of ischemic stroke”, Wiad. Lek. 65(2), 102–107 (2012).
• [2] B.J. Kollen, S. Lennon, B. Lyons, L. Wheatley-Smith, M. Scheper, J.H. Buurke, J. Halfens, A.C. Geurts, and G. Kwakkel, “The effectiveness of the bobath concept in stroke rehabilitation”, Stroke 40(4) (2009).
• [3] J. Vaughan-Graham, C. Cott, and F. V. Wright, “The bobath (NDT) concept in adult neurological rehabilitation: what is the state of the knowledge? a scoping review. part II: intervention studies perspectives”, Disability and Rehabilitation 37(21), 1909–1928 (2014).
• [4] P. Prokopowicz, E. Mikołajewska, D. Mikołajewski, and P. Kotlarz, “Analysis of temporospatial gait parameters”, In P. Prokopowicz, J. Czerniak, D. Mikołajewski, Ł. Apiecionek and D. Ślezak (eds.), Theory and Applications of Ordered Fuzzy Numbers: A Tribute to Professor Witold Kosiński, 289‒302. Springer International Publishing, Cham (2017).
• [5] P. Prokopowicz, D. Mikołajewski, E. Mikołajewska, and P. Kotlarz, “Fuzzy system as an assessment tool for analysis of the health-related quality of life for the people after stroke”, In L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh and J. M. Zurada (eds.), Artificial Intelligence and Soft Computing, 710–721. Springer International Publishing, Cham (2017).
• [6] M. Paci, “Physiotherapy based on the bobath concept for adults with post-stroke hemiplegia: a review of effectiveness studies”, Journal of rehabilitation medicine 35(1), 2–7 (2003).
• [7] E. Mikołajewska, “Metoda NDT-Bobath w neurorehabilitacji osób dorosłych”, Wydawnictwo Lekarskie PZWL (2012).
• [8] J. Kacprzyk and S. Zadrożny, “Towards human consistent data driven decision support systems using verbalization of data mining results via linguistic data summaries”, Bull. Pol. Ac.: Tech. 58(3), 359–370 (2010).
• [9] M. Jasiński, P. Majtczak, and A. Malinowski, “Fuzzy logic in decision support system as a simple human/internet of things interface for shunt active power filter”, Bull. Pol. Ac.: Tech. 64(4), 877–886 (2016).
• [10] A. Sukhanov, S. Kovalev, and V. Stýskala, “Fuzzy interpretation for temporal-difference learning in anomaly detection problems”, Bull. Pol. Ac.: Tech. 64(3), 625–632 (2016).
• [11] D. Dubois, Fuzzy Sets and Systems: Theory and Applications. Mathematics in Science and Engineering, Elsevier Science (1980).
• [12] F.C.-D. la Torre, J. I. González-Trejo, C.A. Real-Ramírez, and L.F. Hoyos-Reyes, “Fractal dimension algorithms and their application to time series associated with natural phenomena”, J. Phys. Conf. Ser. 475, 012002 (2013).
• [13] T.T. Şimşek and K. Çekok, “The effects of nintendo WiiTM-based balance and upper extremity training on activities of daily living and quality of life in patients with sub-acute stroke: a randomized controlled study”, Int J Neurosci 126(12), 1061‒1070 (2015).
• [14] J. Krukowska, M. Bugajski, M. Sienkiewicz, and J. Czernicki, “The inf luence of NDT-bobath and PNF methods on the field support and total path length measure foot pressure (COP) in patients af ter stroke”, Neurol Neurochir Pol 50(6), 449–454 (2016).
• [15] K.B. Lee, S.H. Lim, Y.D. Kim, B.I. Yang, K.H. Kim, K.S. Lee, E.J. Kim, and B.Y. Hwang, “The contributions of balance to gait capacity and motor function in chronic stroke”, Journal of Physical Therapy Science 28(6), 1686–1690 (2016).
• [16] E. Mikołajewska, “Normalized gait parameters in NDT-bobath post-stroke gait rehabilitation”, Open Medicine 7(2) (2012).
• [17] E. Mikołajewska, “The value of the NDT-Bobath method in post-stroke gait training”, Adv Clin Exp Med 22(2), 261–272 (2013).
• [18] E. Mikołajewska, “Associations between results of post-stroke NDT-Bobath rehabilitation in gait parameters, ADL and hand functions”, Adv Clin Exp Med 22(5), 731–738 (2013).
• [19] E. Mikołajewska, “NDT-bobath method in post-stroke rehabilitation in adults aged 42–55 years – preliminary findings”, Polish Annals of Medicine 22(2), 98–104 (2015).
• [20] M. Kılınç, F. Avcu, O. Onursal, E. Ayvat, C.S. Demirci, and S.A. Yildirim, “The effects of bobath-based trunk exercises on trunk control, functional capacity, balance, and gait: a pilot randomized controlled trial”, Topics in Stroke Rehabilitation 23(1), 50–58 (2016).
• [21] M.B. García, M.Á.A. Arratibel, and M.E.T. Azpiroz, “The bobath concept in walking activity in chronic stroke measured through the international classification of functioning, disability and health”, Physiother Res Int 20(4), 242–250 (2014).
• [22] B. Langhammer and J.K. Stanghelle, “Can physiotherapy after stroke based on the bobath concept result in improved quality of movement compared to the motor relearning programme”, Physiother Res Int 16 (2), 69–80 (2010).
• [23] Q. Tang, L. Tan, B. Li, X. Huang, C. Ouyang, H. Zhan, Q. Pu, and L.Wu, “Early sitting, standing, and walking in conjunction with contemporary bobath approach for stroke patients with severe motor deficit”, Topics in Stroke Rehabilitation 21(2), 120‒127 (2014).
• [24] J.-C. Chen, “Progress in sensorimotor rehabilitative physical therapy programs for stroke patients”, World Journal of Clinical Cases 2(8), 316 (2014).
• [25] G. Taveggia, A. Borboni, C. Mulé, J. H. Villafañe, and S. Negrini, “Conflicting results of robot-assisted versus usual gait training during postacute rehabilitation of stroke patients”, Int. J. Rehabil. Res. 39(1), 29–35 (2016).
• [26] K. Brock, G. Haase, G. Rothacher, and S. Cotton, “Does physiotherapy based on the bobath concept, in conjunction with a task practice, achieve greater improvement in walking ability in people with stroke compared to physiotherapy focused on structured task practice alone? a pilot randomized controlled trial”, Clin Rehabil 25(10), 903–912 (2011).
• [27] S. Gallagher, C.P. Phadke, F. Ismail, and C. Boulias, “Physical therapy for an adult with chronic stroke after botulinum toxin injection for spasticity: A case report”, Physiotherapy Canada 67(1), 65–68 (2015).
• [28] F. Wang, L. Zhang, J. Wang, Y. Shi, and L. Zheng, “Efficacy on hemiplegic spasticity treated with plum blossom needle tapping therapy at the key points and bobath therapy: a randomized controlled trial”, Zhongguo zhen jiu= Chinese acupuncture & moxibustion 35(8), 781–784 (2015).
• [29] X. Zhang, R. Qi, and J. Yan, “Clinical research on post-stroke hemiplegia treated with the optimized rehabilitation program of integrated chinese and western medicine”, Zhongguo zhen jiu= Chinese acupuncture & moxibustion 33(12), 1113–1117 (2013).
• [30] N.M. Salbach, K.K. O’brien, D. Brooks, E. Irvin, R. Martino, P. Takhar, S. Chan, and J.-A. Howe, “Considerations for the selection of time-limited walk tests poststroke: a systematic review of test protocols and measurement properties”, Journal of Neurologic Physical Therapy 41(1), 3–17 (2017).
• [31] Q. Zhao, Y. Qiu, G. Zhou, and A. Cichocki, “Comparative study on the classification methods for breast cancer diagnosis”, Bull. Pol. Ac.: Tech. (2018).
• [32] M. Grochowski, A. Kwasigroch, and A. Mikołajczyk, “Selected technical issues of deep neural networks for image classification purposes”, Bull. Pol. Ac.: Tech. 67(2), 363–376 (2019).
• [33] A. Cichocki, T. Poggio, S. Osowski, and V. Lempitsky, “Deep learning: Theory and practice”, Bull. Pol. Ac.: Tech. 66(6), 757‒759 (2018).
• [34] G. Anbarjafari, R. Haamer, I. Lüsi, T. Tikk, and L. Valgma, “3d face reconstruction with region based best fit blending using mobile phone for virtual reality based social media”, Bull. Pol. Ac.: Tech. 67(1) (2019).

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).