  Human pose estimation from monocular video is a rapidly advancing field that
offers great promise to human movement science and rehabilitation. This
potential is tempered by the smaller body of work ensuring the outputs are
clinically meaningful and properly calibrated. Gait analysis, typically
performed in a dedicated lab, produces precise measurements including
kinematics and step timing. Using over 7000 monocular video from an
instrumented gait analysis lab, we trained a neural network to map 3D joint
trajectories and the height of individuals onto interpretable biomechanical
outputs including gait cycle timing and sagittal plane joint kinematics and
spatiotemporal trajectories. This task specific layer produces accurate
estimates of the timing of foot contact and foot off events. After parsing the
kinematic outputs into individual gait cycles, it also enables accurate
cycle-by-cycle estimates of cadence, step time, double and single support time,
walking speed and step length.
