Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients

Research on gait function assessment is important not only in terms of the patient's mobility, but also in terms of the patient's current and future quality of life, ability to achieve health goals, family life, study and/or work, and participation in society. The main methods used herein include a literature review and an analysis of our own original research and concepts. This study used the historical data of 92 ischemic stroke patients (convenience trial) undergoing two kinds of rehabilitation. An artificial neural network, fractal analysis, and fuzzy analysis were used to analyze the results. Our findings suggest that artificial neural networks, fuzzy logic, and multifractal analysis are useful for building simple, low-cost, and efficient computational tools for gait analysis, especially in post-stroke patients. The novelty lies in the simultaneous application of the three aforementioned technologies to develop a computational model for the analysis of a patient's post-stroke gait. The contribution of this work consists not only in its proposal of a new and useful clinical tool for gait assessment, even in the most severe post-stroke cases, but also in its attempt to offer a comprehensive computational explanation of observed gait phenomena and mechanisms. We conclude by anticipating more advanced and broader future applications of artificial intelligence (AI) in gait analysis, especially in post-stroke patients.

Automated summary

Research on gait function assessment is important for patients' mobility, quality of life, health goals, family life, study/work, and participation in society. This study used historical data of 92 ischemic stroke patients to analyze their gait using artificial neural networks, fractal analysis, and fuzzy analysis. The findings suggest that these technologies can build low-cost and efficient computational tools for gait analysis in post-stroke patients. The study's contribution lies in proposing a new clinical tool for gait assessment and offering a computational explanation of observed gait phenomena and mechanisms.