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

Łukasz Kidziński, Bryan Yang, Jennifer L. Hicks, Apoorva Rajagopal, Scott L. Delp, Michael H. Schwartz

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

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.

Original languageEnglish (US)
Article number4054
JournalNature communications
Volume11
Issue number1
DOIs
StatePublished - Aug 13 2020

Bibliographical note

Funding Information:
Our research was supported by the Mobilize Center, a National Institutes of Health Big Data to Knowledge (BD2K) Center of Excellence through Grant U54EB020405, and RESTORE Center, a National Institutes of Health Center through Grant P2CHD10191301.

Publisher Copyright:
© 2020, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.

Keywords

  • Electronic Data Processing
  • Female
  • Gait/physiology
  • Humans
  • Machine Learning
  • Male
  • Neural Networks, Computer
  • Walking/physiology

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

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