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

Abstract

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.

Keywords

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