# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 1 Introduction
### Clinical Gait Analysis  
Gait analysis is defined as the analysis of human walking locomotion, oftenusing computerized and instrumented measurements of its constituent movement patterns [9]. Gait can be described using a large number of parameters, and their statistical and simulational analysis enables the relatively early detection of many abnormalities, the selection of an appropriate therapeutic method, and the evaluation of therapy progress. However, it must be remembered that, despite the importance of gait research, a mathematical model of gait was not developed until the 1990s. This is due to the fact that a comprehensive description of the human locomotor system is difficult, if not impossible: in a healthy person, more than 280 degrees of freedom may be observed.  
More and more tools are being provided to enable clinical gait analysis to be performed reliably and reproducibly. The development of certain software and hardware has reduced the effort needed to perform even complex calculations or simulations and hashus led to their increasing clinical use. Such tools (even simplified ones) can provide a better understanding of physiological gait and the whole spectrum of its possible disorders, both through the pervasiveness of their use in clinical settings and in studies on patients not previously subjected to them in gait laboratories [10; 11; 12]. Simplified procedures and computer support enable significant time savings as well as greater accuracy and reproducibility than traditional observational methods. Despite the current advances in clinical gait analysis, limitations are still apparent, and these mainly relate to the following factors:  
* the large amount of acquired data, which areoften redundant or even useless;
* the need to match tools to observed clinical problems, conditions, and deficits;
* the needs and expectations of clinicians regarding the way in which the results of clinical gait analysis are examined, presented, and interpreted (e.g., it should be fast, accurate, efficient, and based on an individual patient model);
* Its time-consuming nature and the difference between input (time, effort, equipment) and effects (no step improvement in rehabilitation effectiveness) [11; 12].  
For the aforementioned reasons, even today it is difficult to consider clinical gait analysis as a routine test, despite the fact that gait assessment is undertaken routinely even by primary care physicians, e.g., visual analysis of abnormal gait and posture and the sources of associated pain [13]. However, this is difficult to carry out promiscuously and reproducibly in the patient's natural environment [2], even using sophisticated statistical methods and comparisons with data for the general population [14].  
The theory and practice of clinical gait analysis has been the subject of scientific interest since as early as the 1980s and 1990s [15; 16], with early attempts at automation and the introduction of clinical decision support systems [17]. This also applies to clinical gait analysis in post-stroke patients [18; 19]. [PERSON] et al. showed approximately 1.6 times more gait cycle variability in hemiparetic patients than in controls [20]. Differences in approaches to clinical gait analysis among different medical specialists have also been investigated, and the need for standardization in this area has been demonstrated [21; 22].