Deep neural networks enable quantitative movement analysis using single-camera videos

Łukasz Kidziński (), Bryan Yang, Jennifer L. Hicks, Apoorva Rajagopal, Scott L. Delp and Michael H. Schwartz ()
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Łukasz Kidziński: Department of Bioengineering, Stanford University
Bryan Yang: Department of Bioengineering, Stanford University
Jennifer L. Hicks: Department of Bioengineering, Stanford University
Apoorva Rajagopal: Department of Bioengineering, Stanford University
Scott L. Delp: Department of Bioengineering, Stanford University
Michael H. Schwartz: Center for Gait and Motion Analysis, Gillette Children’s Specialty Healthcare

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-17807-z

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