Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:115-120. doi: 10.1109/EMBC48229.2022.9871036.
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.
从单目视频中进行人体姿态估计是一个快速发展的领域，为人体运动科学和康复提供了巨大的潜力。然而，这一潜力受到了确保输出具有临床意义和适当校准的工作数量较少的限制。步态分析通常在专门的实验室中进行，可产生包括运动学和步时在内的精确测量。我们使用来自仪器化步态分析实验室的超过 7000 个单目视频，训练了一个神经网络，将 3D 关节轨迹和个体高度映射到可解释的生物力学输出上，包括步态周期时间以及矢状面关节运动学和时空轨迹。这个特定于任务的层可以准确估计脚触地和脚离地事件的时间。在将运动学输出解析为单个步态周期后，它还可以实现步频、步时、双支撑和单支撑时间、行走速度和步长的逐周期精确估计。
