Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulation
This addresses gait analysis for clinical applications using smartphone videos, but it is incremental as it builds on existing motion imitation and physics simulation methods.
The paper tackled the problem of physically implausible gait parameter estimates from monocular videos by training a reinforcement learning policy to control a physics simulation that replicates video movement, resulting in improved accuracy for step length and walking velocity.
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.