Perturbation training for enhancing stability and limb support control for fall-risk reduction among stroke survivors

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Residual gait and balance impairments are major risk factors for post-stroke falls and reduced mobility. Therefore, gait characteristics are primary diagnosis and treatment targets for people living with chronic stroke (PwCS), and accurate and repeatable quantitative gait analysis is crucial to the field. Further, balance and gait rehabilitation are the most utilized ICD 10 (International Classification of Diseases) codes for physical rehabilitation of people living with neurological disorders. NICHD funds several clinical trials targeting novel balance and gait interventions, yet there is a gap in the field pertaining to data sharing, accessibility and utilization. Instrumented gait analysis generates a large amount of interdependent data of various kinds; hence, gait data are difficult to analyze and interpret. Furthermore, human information processing errors could lead to high variability in interpretations. Such barriers can be resolved via machine learning for health (ML4H) research, whose goal is to create models to solve complex tasks with limited or no human supervision. Unfortunately, recent studies show that ML4H compares poorly to other ML fields regarding data and code accessibility, and public gait data repositories for clinicians and researchers are limited. Even though researchers are willing to share data, differences in collection methods and file structures require computational expertise for anyone to use the data. We have access to a large data set collected from R01HD088543:“Perturbation training for enhancing stability and limb support control for fall-risk reduction among stroke survivors.” The project is a randomized controlled trial examining the ability of PwCS to acquire, generalize and retain adaptations to slip-perturbation training for not only mitigating fall risk but also improving walking function. The hypothesis of this study if supported by the results will provide an evidence-supported training protocol to reduce the fall-risk in PwCS. The data sets generated from the grant include kinematics, kinetics, and clinical measures for stance posture control, perturbed and unperturbed gait (about 1,500 trials total) from PwCS. This collaborative project aims to take a step towards democratizing data-driven approaches in gait analysis to empower a broad range of stakeholders (AL/ML researchers, physical therapists, rehab scientists). In Aim 1, we will evaluate and enable metadata through data wrangling and harmonization capabilities (DataWrangler library) following FAIR data principles (findability, accessibility, interoperability and reusability). Aim 2 will leverage harmonized data sets to create scientific workflows for biomechanical data utilization (GaitVis library, with data visualization, cleaning and analysis functionalities) and provide access this data through a centralized website (DataPortal). Lastly in Aim 3, we will show an use case of the transformed data for developing a predictive fall-risk model based on gait data and clinical measures. By enabling the data to be discoverable and machine readable through shared terminologies we will enable future researchers to combine unforeseen future data sets and ask questions we have not yet considered.