# Post-Stroke Gait Classification Based on Feature Space Transformation and Data Labeling
###### Abstract  
Despite scientific and clinical advances, stroke is still considered one of the main causes of disability, including gait disorders. The search for more effective methods of gait re-education in post-stroke patients is one of the most important issues in contemporary neurorehabilitation. In this paper, we propose a transformation of the feature space and definition of class labels in the post-stroke gait problem to more efficiently study related phenomena and assess gait faster. Clustering is used to define two class labels (improvement and recurrence) in the data labeling process. The proposed approach was tested on a real-world dataset consisting of 50 patients (male and female, aged 49-82 years) after ischemic stroke who participated in a gait rehabilitation program. Gait in the study was described using speed, cadence, and stride length and their normalized values. Ten treatment sessions (10 therapy days) were conducted over two weeks (10 working days). The same specialist took measurements, and hence inter-rater reliability can be neglected. Machine learning methods, support vector machine and quadratic discriminant analysis were used to classify post-stroke gait for three cases with different class labels. The proposed novel approach, characterized by its speed of execution and accuracy of classification, may be helpful for screening, better targeting, and rehabilitation monitoring. The proposed approach minimizes clinical testing and supports the work of physicians, physiotherapists, and diagnosticians.  
clinical gait analysis; stroke; rehabilitation; machine learning +
Footnote †: journal:  
changes in the patients' gait parameters, changes in hand function, and in activities of daily living; however, it is difficult to achieve a simultaneous improvement in all areas: gait parameters, hand function, and ADLs within two weeks of rehabilitation [7; 8; 9].  
The advent of many new therapeutic approaches as well as the effects of the COVID-19 pandemic have brought with them gaps in contemporary gait analysis associated with the need for prolonged direct contact, hence the need for new, more automated approaches, including rapid, cost-effective, accurate, and useful methods for collecting second opinions in clinical gait analysis.  
A review of three major bibliographic databases (PubMed, PEDro, Scopus) using specified keywords (gait analysis, machine learning (ML), physiotherapy, among others) showed that between 2009 and 2022, only 20 papers were published on ML applications in clinical gait analysis. Only 2 of the papers were related to gait analysis [10; 11]. Both indicated that the use of ML tools in clinical gait analysis in post-stroke patients is insufficient and requires further research. This indicates an important and identified research and clinical gap, which our study is now filling.  
The main novelties and contributions of the presented methods are the speed of execution, the reproducibility and accuracy of the classification, and a better fit with daily clinical practice than a traditional gait laboratory. Additionally, these will support doctors and physiotherapists in their daily work with patients without needing sophisticated and expensive hardware and software. Ultimately, this type of software can be installed on mobile devices (tablets, smartphones) and for home rehabilitation or telerehabilitation.  
This work aimed to propose feature space transformation that allows the definition of new class labels in the post-stroke classification problem. The new feature space defines two class labels, improvement and relapse, based on clustering. The proposed approach serves to minimize clinical testing, as well as support the work of physicians, physiotherapists, and diagnosticians. Machine learning methods, support vector machine (SVM) and quadratic discriminant analysis (QDA) were used for post-stroke gait classification for three cases with different class labels. One of these cases is related to previously defined new class labels.