# Post-Stroke Gait Classification Based on Feature Space Transformation and Data Labeling
## 6 Discussion
### Comparment to the Results from the Other Studies  
The ongoing pandemic poses new challenges to gait diagnosis and therapy. Limitations in access to medical specialists and gait laboratories and the prospect of the above-mentioned situation being indefinitely prolonged into the future prioritizes fast, low-cost and telemedically and telenehabilitation-enabled clinical gait analysis procedures. As strokes are not the only conditions resulting in gait function deficits, the aforementioned tools should investigate gait deficits resulting from other conditions with similar efficiency levels, such as neurodegenerative changes in the elderly, effects of cranicerebral and spinal cord injuries, severe poisoning, metabolic diseases, and burns. They should also be suitable for use by general practitioners, physiotherapists and, in special situations, by patients themselves at home thanks to the automation of the measurement process in the framework of, for example, the 10 m walking test and a quick dichotomous result: a healthy patient or a patient with a deficit (to be further diagnosed in a more complex manner). This would relieve the burden on the healthcare system without compromising the quality of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Classification Algorithm**} \\ \hline  & \(\Psi_{L}^{100}\) & \(\Psi_{RBF}^{100,1}\) & \(\Psi_{Pul}^{100,10}\) & \(\Psi_{Sig}^{1.5}\) & \(\Psi_{QDA}\) \\ \hline Metric & \multicolumn{6}{c}{Normalized velocity subspace} \\ \hline AUC & 1 & 1 & 1 & 0.5 & 1 \\ TPR & 1 & 1 & 1 & 1 & 1 \\ FPR & 1 & 1 & 1 & 0 & 1 \\ \hline Metric & \multicolumn{6}{c}{Normalized cadence subspace} \\ \hline AUC & 1 & 1 & 1 & 0.5 & 0.9 \\ TPR & 1 & 1 & 1 & 1 & 1 \\ FPR & 1 & 1 & 1 & 0 & 0.81 \\ \hline Metric & \multicolumn{6}{c}{Normalized stride length subspace} \\ \hline AUC & 0.96 & 0.96 & 0.96 & 0.53 & 0.94 \\ TPR & 1 & 1 & 1 & 1 & 1 \\ FPR & 0.93 & 0.93 & 0.93 & 0.06 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Result of classification—improvement vs. relapse.  
gait diagnosis. In our opinion, the solution we demonstrated meets the requirements of being a faster solution than the artificial neural networks, fuzzy analysis, or fractal analysis used to date, and it is therefore possible to use on mobile devices (52% of the Internet access in the world occurs through mobile devices). A comparison with the results of other researchers is not straightforward, as practical, rapid, and low-cost studies are scarce [5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. The existing indices of gait functionality, including post-stroke, can describe the distance of gait from normal and help to monitor the return of gait to normal as a result of self-healing or recovery processes in the body supported by the rehabilitation processes by observing improvements in gait parameters (e.g., stride length, durations of gait cycle components, etc.). However, these indices reveal little information related to gait quality or kinesiology status [15], and are not quick and cheap to use as screening tests. This overlaps with the need to more fully understand the process of human motor control by developing a model of behavior in different situations (including gait disturbed by external factors) in patients with postural disorders [16].