Advancing Monocular Video-Based Gait Analysis Using Motion Imitation
  with Physics-Based Simulation
Nikolaos Smyrnakis Tasos Karakostas R. James Cotton
Gait analysis from videos obtained from a smartphone would open up many
clinical opportunities for detecting and quantifying gait impairments. However,
existing approaches for estimating gait parameters from videos can produce
physically implausible results. To overcome this, we train a policy using
reinforcement learning to control a physics simulation of human movement to
replicate the movement seen in video. This forces the inferred movements to be
physically plausible, while improving the accuracy of the inferred step length
and walking velocity.