Hippotherapy, riding a horse in the context of rehabilitation, is a medical treatment that successfully has been employed in various fields, e.g. for improving locomotion performance of patients with movement disorders. Robotic systems enable the application of hippotherapy in clinical environments with additional benefits, like adjustable speed and high repeatability. Fundamental for a therapy outcome equivalent to classical hippotherapy is that the trajectory of the robotic system is as realistic as possible. This paper introduces a method for the synthesis of horseback motions using motion capture data of various horse gaits. Based on complete gait cycles, an analytical, time-continuous and periodic motion description is constructed. Measured 3D marker positions are reconstructed with a mean error not exceeding 8.6 mm. If the motion capture data of several gait cycles are considered, a more generalized trajectory is generated. An adjustable time dilatation parameter enables the adaptation of the generated motion according to the physical abilities of the patient or the capabilities of the robotic system. This method allows for motion synthesis with arbitrary time span and time resolution, generating realistic trajectories effectively applicable to robotic systems for riding simulation in general and robotic hippotherapy in particular.

在康复的背景下骑马的河马疗法是一种医疗方法，已成功地应用于各种领域，例如用于改善运动障碍患者的运动能力。机器人系统可在临床环境中应用河马疗法，并具有其他优势，例如速度可调，重复性高。相当于经典河马疗法的治疗结果的根本在于，机器人系统的轨迹应尽可能逼真。本文介绍了一种利用各种步态的运动捕获数据来合成骑马运动的方法。基于完整的步态周期，构造了一个分析，时间连续和周期性运动描述。重建测量的3D标记位置，平均误差不超过8.6毫米。如果考虑几个步态周期的运动捕获数据，则会生成更通用的轨迹。可调节的时间膨胀参数使得能够根据患者的身体能力或机器人系统的能力来适应所产生的运动。这种方法允许以任意时间跨度和时间分辨率进行运动合成，从而生成现实的轨迹，有效地适用于一般骑乘模拟的机器人系统，尤其是机器人河马疗法。