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.

翻译：从智能手机获取的视频中进行步态分析，将为检测和量化步态障碍开启许多临床机遇。然而，现有从视频估算步态参数的方法可能产生不符合物理规律的结果。为此，我们通过强化学习训练一个策略来控制人体运动的物理仿真，以复现视频中观察到的运动。这使得推断出的运动必须符合物理规律，同时提高了推断步长和步行速度的准确性。