Abstract
Many neurological and musculoskeletal diseases impair movement, which limits people’s function and social participation. Quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion capture systems and highly trained personnel. Here, we present a method for predicting clinically relevant motion parameters from an ordinary video of a patient. Our machine learning models predict parameters include walking speed (r = 0.73), cadence (r = 0.79), knee flexion angle at maximum extension (r = 0.83), and Gait Deviation Index (GDI), a comprehensive metric of gait impairment (r = 0.75). These correlation values approach the theoretical limits for accuracy imposed by natural variability in these metrics within our patient population. Our methods for quantifying gait pathology with commodity cameras increase access to quantitative motion analysis in clinics and at home and enable researchers to conduct large-scale studies of neurological and musculoskeletal disorders.
Highlights
Many neurological and musculoskeletal diseases impair movement, which limits people’s function and social participation
Our method capitalizes on 2D pose estimates from video to predict (i) quantitative gait metrics commonly used in clinical gait analysis, and (ii) clinical decisions
We first sought to determine visit-level average walking speed, cadence, and knee flexion angle at maximum extension from a 15 s sagittal-plane walking video. These gait metrics are routinely used as part of diagnostics and treatment planning for cerebral palsy[4] and many other disorders, including Parkinson’s disease[19,20], Alzheimer’s disease[21,22], osteoarthritis[2,23], stroke[3,24], non-Alzheimer’s dementia[25], multiple sclerosis[5,26], and muscular dystrophy[6]
Summary
Many neurological and musculoskeletal diseases impair movement, which limits people’s function and social participation. Our machine learning models predict parameters include walking speed (r = 0.73), cadence (r = 0.79), knee flexion angle at maximum extension (r = 0.83), and Gait Deviation Index (GDI), a comprehensive metric of gait impairment (r = 0.75) These correlation values approach the theoretical limits for accuracy imposed by natural variability in these metrics within our patient population. Our models were trained on 1792 videos of 1026 unique patients with cerebral palsy These videos, along with gold-standard optical motion capture data, were collected as part of a clinical gait analysis. We predicted visit-level gait metrics (i.e., values averaged over multiple strides from multiple experimental trials), since the videos and gold-standard optical motion capture were collected contemporaneously but not simultaneously These visit-level estimates of values, such as average speed or cadence, are widely adopted in clinical practice. We present the a Optical motion capture using reflective markers b
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