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

Citations Over TimeTop 1% of 2020 papers

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.

Related Papers

• → An exercise intervention to improve diabetic patients’ gait in a real-life environment(2010)68 cited
• → The Primary Gait Screen in Parkinson’s disease: Comparison to standardized measures(2019)8 cited
• → Development of Gait at Slow, Free, and Fast Speeds in 3- and 5-Year-Old Children(1983)53 cited
• → Overground versus treadmill walking in Parkinson’s disease: Relationship between speed and spatiotemporal gait metrics(2022)12 cited
• → Nordic walking can improve dynamic stability of human gait in Parkinson disease(2015)1 cited