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.

使用智能手机获取视频进行步态分析可以揭示许多临床机会以检测和量化步态障碍。然而，现有视频估计步态参数的方法可能产生不符合物理规律的结果。为了解决这个问题，我们使用强化学习训练一个策略来控制人类运动的物理模拟，以复制视频中观察到的运动。这样可以强制推断的运动具有物理可行性，同时提高步幅和步行速度的准确性。

基于物理模拟的运动模仿推进单目视频步态分析

Advancing Monocular Video-Based Gait Analysis Using Motion Imitation  with Physics-Based Simulation

Background: Physiological tremor is defined as an involuntary and rhythmic
shaking. Tremor of the hand is a key symptom of multiple neurological diseases,
and its frequency and amplitude differs according to both disease type and
disease progression. In routine clinical practice, tremor frequency and
amplitude are assessed by expert rating using a 0 to 4 integer scale. Such
ratings are subjective and have poor inter-rater reliability. There is thus a
clinical need for a practical and accurate method for objectively assessing
hand tremor.
Objective: to develop a proof of principle method to measure hand tremor
amplitude from smartphone videos.
Methods: We created a computer vision pipeline that automatically extracts
salient points on the hand and produces a 1-D time series of movement due to
tremor, in pixels. Using the smartphones' depth measurement, we convert this
measure into real distance units. We assessed the accuracy of the method using
60 videos of simulated tremor of different amplitudes from two healthy adults.
Videos were taken at distances of 50, 75 and 100 cm between hand and camera.
The participants had skin tone II and VI on the Fitzpatrick scale. We compared
our method to a gold-standard measurement from a slide rule. Bland-Altman
methods agreement analysis indicated a bias of 0.04 cm and 95% limits of
agreement from -1.27 to 1.20 cm. Furthermore, we qualitatively observed that
the method was robust to differences in skin tone and limited occlusion, such
as a band-aid affixed to the participant's hand.
Clinical relevance: We have demonstrated how tremor amplitude can be measured
from smartphone videos. In conjunction with tremor frequency, this approach
could be used to help diagnose and monitor neurological diseases

本文介绍了一种从智能手机视频中测量手部震颤幅度的方法，可与震颤频率一起用于帮助诊断和监测神经疾病。