Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks
Computational gait analysis constitutes a useful tool for quantitative assessment of gait disturbances, improving functional diag nosis, assessment of treatment planning, and monitoring of disease progress. There is little research on use of computational gait analysis in neurorehabilitation of post...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2020-04-01
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| Series: | Bulletin of the Polish Academy of Sciences: Technical Sciences |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/115170/PDF/03D_191-198_01300_Bpast.No.68-2_29.04.20_K4A_SS_TeX.pdf |
| _version_ | 1828443298239348736 |
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| author | P. Prokopowicz D. Mikołajewski K. Tyburek E. Mikołajewska |
| author_facet | P. Prokopowicz D. Mikołajewski K. Tyburek E. Mikołajewska |
| author_sort | P. Prokopowicz |
| collection | DOAJ |
| description | Computational gait analysis constitutes a useful tool for quantitative assessment of gait disturbances, improving functional diag nosis, 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. |
| first_indexed | 2024-12-10T21:27:15Z |
| format | Article |
| id | doaj.art-f91890e5ab5e4eed8037fbd4177dbd6a |
| institution | Directory Open Access Journal |
| issn | 2300-1917 |
| language | English |
| last_indexed | 2024-12-10T21:27:15Z |
| publishDate | 2020-04-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Bulletin of the Polish Academy of Sciences: Technical Sciences |
| spelling | doaj.art-f91890e5ab5e4eed8037fbd4177dbd6a2022-12-22T01:32:57ZengPolish Academy of SciencesBulletin of the Polish Academy of Sciences: Technical Sciences2300-19172020-04-0168No. 2 (i.a. Special Section on Computational Intelligence in Communications)191198https://doi.org/10.24425/bpasts.2020.131843Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networksP. ProkopowiczD. MikołajewskiK. TyburekE. MikołajewskaComputational gait analysis constitutes a useful tool for quantitative assessment of gait disturbances, improving functional diag nosis, 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.https://journals.pan.pl/Content/115170/PDF/03D_191-198_01300_Bpast.No.68-2_29.04.20_K4A_SS_TeX.pdfcomputational analysisspatio-temporal gait parametersfuzzy analysisgait classificationdisorder recognition |
| spellingShingle | P. Prokopowicz D. Mikołajewski K. Tyburek E. Mikołajewska Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks Bulletin of the Polish Academy of Sciences: Technical Sciences computational analysis spatio-temporal gait parameters fuzzy analysis gait classification disorder recognition |
| title | Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks |
| title_full | Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks |
| title_fullStr | Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks |
| title_full_unstemmed | Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks |
| title_short | Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks |
| title_sort | computational gait analysis for post stroke rehabilitation purposes using fuzzy numbers fractal dimension and neural networks |
| topic | computational analysis spatio-temporal gait parameters fuzzy analysis gait classification disorder recognition |
| url | https://journals.pan.pl/Content/115170/PDF/03D_191-198_01300_Bpast.No.68-2_29.04.20_K4A_SS_TeX.pdf |
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