An approach to model and estimate human walking kinematics in real-time for Physical Human-Robot Interaction is presented. The human gait velocity along the forward and vertical direction of motion is modelled according to the Yoyo-model. We designed an Extended Kalman Filter (EKF) algorithm to estimate the frequency, bias and trigonometric state of a biased sinusoidal signal, from which the kinematic parameters of the Yoyo-model can be extracted. Quality and robustness of the estimation are improved by opportune filtering based on heuristics. The approach is successfully evaluated on a real dataset of walking humans, including complex trajectories and changing step frequency over time.

翻译：介绍了在人体物理机器人互动实时模拟和估计人类行走动动脉学的方法;根据Yoyo模型,模拟了运动前向和垂直方向的人类步速;我们设计了扩展卡尔曼过滤器算法,以估计偏向性脊髓信号的频率、偏向和三角状态,从中可以提取Yoyo模型的运动参数;通过根据超自然学进行适当过滤,提高了估计的质量和稳健性;根据行走人的真实数据集,包括复杂的轨迹和跨步频率的变化,成功地评价了这一方法。