Clinical gait analysis using video-based pose estimation: multiple perspectives, clinical populations, and measuring change

Abstract

Gait dysfunction is common in many clinical populations and often has a profound and deleterious impact on independence and quality of life. Gait analysis is a foundational component of rehabilitation because it is critical to identify and understand the specific deficits that should be targeted prior to the initiation of treatment. Unfortunately, current state-of-the-art approaches to gait analysis (e.g., marker-based motion capture systems, instrumented gait mats) are largely inaccessible due to prohibitive costs of time, money, and effort required to perform the assessments. Here, we demonstrate the ability to perform quantitative gait analyses in multiple clinical populations using only simple videos recorded using household devices (tablets). We report four primary advances: 1) a novel, versatile workflow that leverages an open-source human pose estimation algorithm (OpenPose) to perform gait analyses using videos recorded from multiple different perspectives (e.g., frontal, sagittal), 2) validation of this workflow in three different populations of participants (adults without gait impairment, persons post-stroke, and persons with Parkinson’s disease) via comparison to ground-truth three-dimensional motion capture, 3) demonstration of the ability to capture clinically relevant, condition-specific gait parameters, and 4) tracking of within-participant changes in gait, as is required to measure progress in rehabilitation and recovery. Importantly, our workflow has been made freely available and does not require prior gait analysis expertise. The ability to perform quantitative gait analyses in nearly any setting using only household devices and computer vision offers significant potential for dramatic improvement in the accessibility of clinical gait analysis across different patient populations.

Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson’s disease

“…While the lack of standardization in the camera position and orientation with respect to the direction of movement is similar to a potential real-world data collection environment, this also increased the variance in the input (as compared to the DIP dataset) and thus likely the output predictions as well. While this study only evaluated videos taken from a primarily coronal view, however certain aspects of gait such as those associated with step length or speed are more reliably captured from a sagittal view [ 18 ]. However, sagittal views generally require more physical space to take which is not always feasible in a home environment, whereas coronal view videos can be taken in hallways commonly found in home-living environments [ 19 ].…”

Computer Vision-Based Approach for Automated Monitoring and Assessment of Gait Rehabilitation at Home

“…This research aimed to enhance rehabilitation strategies for such patients. In another study referenced as [12], the authors compared spatiotemporal gait parameters, lower-limb sagittal plane joint kinematics, and condition-specific clinically relevant parameters. They simultaneously obtained Computer Vision-Based Approach for Automated Monitoring and Assessment of Gait Rehabilitation at Home data using 3D motion capture and sagittal and frontal plane videos.…”