# Post-Stroke Gait Classification Based on Feature Space Transformation and Data Labeling
## 2 Material and Methods
### Methods  
Gait evaluation was based on spatial-temporal gait parameters (gait speed, cadence, and stride length), i.e., their normalized parameters--related to lower limb length, which allows their comparison between patients of different height and stride length. In the healthy group, parameter measurements were performed by the same therapist once during the 10 m walking test. In the sick group, the parameters were measured by the same therapist twice during the 10 m walking test: on admission to the hospital and after the tenth session of gait re-education in order to evaluate the effects of rehabilitation. The study group consisted of 25 patients after ischemic stroke who participated in a rehabilitation program. Ten therapy sessions (10 days of therapy) were conducted over a 2-week period (ten working days). Measurements were made by the same specialist, and hence inter-rater reliability may be omitted.  
To summarize, each patient was subjected to a 10 m walk test (10 MWT), which was recorded using a digital smartphone camera. The ground contact time etc., was calculated and used to calculate the velocity, cadence, and stride length in relation to their normalized values using [PERSON]'s Clinical Gait Analyzer software, which is available online.  
To obtain the leg length, the examiner measures from the anterior superior iliac spine (ASIS) to the medial malleolus. By entering the leg length (ASIS to medial malleolus), you can see how close the patient is to normal and allow you to compare gait parameters in patients with various leg lengths. Thus, velocity, cadence, and stride length after the normalization procedure using leg length become normalized velocity, normalized cadence, and normalized stride length. Normalization was performed using Clinical Gait Analysis software by [PERSON] ([[http://www.clinicalgaitanalysis.com/](http://www.clinicalgaitanalysis.com/)]([http://www.clinicalgaitanalysis.com/](http://www.clinicalgaitanalysis.com/))(accessed on 1 June 2022)). Hardware and software tools employed in our work:  
* Clinical Gait Analysis software by [PERSON] (available online at [[http://www.clini](http://www.clini)]([http://www.clini](http://www.clini)) calgaitanalysis.com/(accessed on 1 June 2022)) for gait parameters calculation purposes;
* Statistica 13 (StatSoft, Tulsa, OK, USA) for statistical analysis purposes;  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Study Group** & **Reference Group** \\  & **(n = 25, 100\%)** & **(n = 25, 100\%)** \\ \hline \hline  & & Age (years) \\ \hline Mean & 65.28 & 58.6 \\ SD & 9.65 & 6.29 \\ Min & 49 & 51 \\ Q1 & 57 & 54 \\ Median & 68 & 56 \\ Q3 & 74 & 61 \\ Max & 82 & 72 \\ \hline \hline  & & Side of paresis: \\ \hline Left (L) & 13 (52\%) & n. a. \\ Right (R) & 12 (48\%) & n. a. \\ \hline \hline  & Sex: \\ \hline Female (F) & 12 (48\%) & 13 (52\%) \\ Male (M) & 13 (52\%) & 12 (48\%) \\ \hline n.a.—not applicable. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clinical summary of the patients.  
SAS 9.4 (SAS Institute, Cary, NC, USA) software was used to conduct computational analyses.  
The results were subjected to statistical and computational analyses. All the data were analyzed with Statistica version 13 software. The parameter change was defined as the result of subtraction. The distribution of values was determined using the Shapiro-Wilk test. Values for distributions close to the normal distribution were presented by mean values and standard deviation (SD). Values for distributions different from the normal distribution were presented by median, minimum value, maximum value, lower quartile, and upper quartile. The T-student test was used to compare the results. Spearmans rho was used to extract correlations. The significance level was set at 0.05.