# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 3 Results  
The analysis using neural networks resulted in an excellent fit of the learned network to the complex model. Errors in the healthy/post-stroke patient classification were not observed. An attempt was made to extend the model to include the calibration of the hemiplegic gait (the fractal dimension played a decisive role as a discriminator of gait irregularity), and this was successful. A multilayer perceptron (MLP)-type neural network with three layers was used for classification due to its simplicity (Figure 1). The following six inputs related to the six basic spatial-temporal gait parameters were defined: velocity, cadence, stride length, normalized velocity, normalized cadence, and normalized stride length. Only one output indicated probability of stroke. All inputs and outputs were normalized to arrange of [0,1]. The network learning process used all the data and parameters  
The changes in the fractal parameters calculated before and after therapy in the study group were statistically significant (\(p\)\(<\) 0.05). The differences in the changes in parameters measured before and after therapy in the study group and the reference group were statistically significant (\(p\)\(<\)0.05). Thus, one of the above-mentioned therapy modalities was clearly superior to the other, as is reflected in the results of the study. A comparison of means and medians shows that, for all parameters studied, greater improvement occurred in the study group than inthe control group (Table 4). The changes in the fuzzy parameters calculated before and after therapy in both groups werestatistically significant (\(p\)\(<\)0.05) (Table 5). \begin{table}
\begin{tabular}{c c c} \hline \hline  & Mean & 0.08 \\ \cline{2-3}  & SD & 0.02 \\ \cline{2-3}  & Min & 0.02 \\ \cline{2-3}  & Q1 & 0.04 \\ \cline{2-3}  & Median & 0.07 \\ \cline{2-3}  & Q3 & 0.08 \\ \cline{2-3}  & Max & 0.09 \\ \hline  & Mean & 0.07 \\ \cline{2-3}  & SD & 0.02 \\ \cline{2-3}  & Min & 0.03 \\ \cline{2-3}  & Q1 & 0.08 \\ \cline{2-3}  & Median & 0.07 \\ \cline{2-3}  & Q3 & 0.08 \\ \cline{2-3}  & Max & 0.09 \\ \hline \multicolumn{2}{c}{Distribution} & Not normal \\ \hline \multicolumn{2}{c}{\(p\)} & 0.042 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of changes in fractal parameters before and after therapy in the study and reference groups. Figure 2: Change in root mean square error (RMSE) during learning. Correlations among the traditional (spatial-temporal) and novel (fractal and fuzzy) gait parameters are shown in Tables 6 and 7. In the study group, high and very high correlations were observed between changes in the studied spatial-temporal gait parameters, with the exception of a statistically insignificant association between change in pace and change in the normalized gait cycle, as well as moderate to high correlations between the fractal dimension, the fuzzy parameter and the spatial-temporal gait parameters. The negative correlations for the fractal dimension are due to the fact that an improvement in this parameter implies a decrease in this parameter (inversely to the other parameters studied).