Abstract
Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods.Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings.Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included.Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.
Highlights
Stroke, defined as the sudden rupture or blockage of a cerebral blood vessel and consequent damage to central nervous system cells and tissues due to the interruption of oxygen supply, remains a major health challenge throughout the world
The main goal is to offer a practical resource on the multidimensional aspects of post stroke gait focusing on novel tools and technologies for quantitative assessment that can be feasibly incorporated into clinical practice
As generally agreed upon by many clinicians, quantitative gait analysis outperforms traditional observational scales as it generates unbiased outcomes that can be used as benchmarks for rehabilitation
Summary
Stroke, defined as the sudden rupture or blockage of a cerebral blood vessel and consequent damage to central nervous system cells and tissues due to the interruption of oxygen supply, remains a major health challenge throughout the world. According to the World Health Organization (WHO), every year, 15 million people worldwide are diagnosed with stroke, of which, approximately 6 million die and another 5 million are left with permanent disabilities [1]. Human movement is characterized using individual gait cycles and functional phases. The stance phase corresponds to the duration between heel strike and toe-off of the same foot, and it constitutes approximately 60% of the gait cycle. Any abnormalities observed in the phases or events of gait may be linked to musculoskeletal and/or neuro-muscular complications, such as the case of individuals with post-stroke gait impairment. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods. Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
