# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 2 Materials and Methods
### Methods  
The software used in this study was MatlabR2023a with the Neural Networks toolbox (MathWorks). An analysis using fuzzy numbers (including directed fuzzy numbers) was also performed by [PERSON] using MU-FEG software. The use of a neural network in this study allowed us to extract from the studied parameters (spatial-temporal, fractal, fuzzy) associations and features given indirectly, classify and present the results in a form enabling their use in assessing a patient's condition (patients qualified for a specific group according to whether they were healthy or sick, including patients with hemiplegia), and to direct the further rehabilitation process, including by assigning the studied patient to a group with specific gait characteristics. Asystem developed in this way could become the basis of a future artificially intelligent therapeutic decision support system or so-called second opinion. The fractal dimension (FD) is included among the tools of chaos theory, as is entropy. It is an alternative to variance, standard deviation, or coefficient of variation. In the present study, the fractal dimension complements the measurement of step duration irregularity for the measurement of the traditional spatial-temporal parameters of gait: speed, pace, and gait cycle (two-step length),as well as their normalized values. In response to rehabilitation, post-stroke patients (e.g.,those with hemiplegia) exhibit a qualitative change in gait \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Study Group** & **Reference Group** \\  & **n = 46 (100\%)** & **n = 46 (100\%)** \\ \hline Age (years): & & \\ mean & 64.70 & 66.67 \\ SD (standard deviation) & 9.99 & 10.75 \\ min & 41 & 43 \\ QI (lower quartile) & 58 & 57 \\ median & 66 & 68 \\ Q3 (upper quartile) & 72 & 74 \\ max & 81 & 83 \\ \hline Gender: & & \\ female (F) & 23 (50.00\%) & 22 (47.82\%) \\ male (M) & 23 (50.00\%) & 24 (52.18\%) \\ \hline Side affected: & & \\ left (L) & 21(45.65\%) & 23 (50\%) \\ right (P) & 25(54.34\%) & 23 (50\%) \\ \hline Time after cerebrovascular accident & & \\ (weeks): & & \\ mean & 63.22 & 64.17 \\ SD (standard deviation) & 15.87 & 15.21 \\ min & 6 & 6 \\ Q1 (lower quartile) & 24 & 19 \\ median & 61 & 62 \\ Q3 (upper quartile) & 103 & 107 \\ max & 151 & 155 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the study and reference groups. characteristics which is reflected in the time intervals of their gait cycle (typically, their gait becomes smoother). The analysis of gait parameters using directed fuzzy numbers allows a targeted aggregation of gait parameters into a single number in the range 0-1 which denotes the degree to which the gait parameters deviate from the norm for a healthy population. This approach enables the quick assessment of whether a patient's parameters are within the normal range, and, if not, how far they deviate from this norm. The gait quality of a healthy personis at least 0.5 on a scale of 0 to 1. However, in order to obtain a value of 1, the person tested must have gait parameters that are equal to the mean for all the gait parameters considered. The patterns of norms are thus part of a fuzzy system that is generated automatically by the MU-FEG program on the basis of data from a file, and it is possible to generate new norms by replacing the data file. The results obtained were subjected to statistical analysis. Data were archived using a Microsoft Excel 2013 spreadsheet. Statistical calculations were performed using the Statistica 12 computer program from Statsoft (including Medical Kit) and IBM SPSS Statistics 24. In both groups of patients undergoing rehabilitation, changes in the values of individual parameters were determined by subtracting the results of measurement one (before therapy, BP) from the results of measurement two (after therapy, AT).