IEEE Trans Neural Syst Rehabil Eng. 2018 Jan;26(1):115-124. doi: 10.1109/TNSRE.2017.2740060. Epub 2017 Aug 14.
Gait rehabilitation is often focused on the legs and overlooks the role of the upper limbs. However, a variety of studies have demonstrated the importance of proper arm swing both during healthy walking and during rehabilitation. In this paper, we describe a method for generating proper arm-swing trajectories in real time using only measurements of the angular velocity of a person's thighs, to be used during gait rehabilitation with self-selected walking speed. A data-driven linear time-invariant transfer function is developed, using frequency-response methods, which captures the frequency-dependent magnitude and phase relationship between the thighs' angular velocities and the arm angles (measured at the shoulder, in the sagittal plane), using a data set of 30 healthy adult subjects. We show that the proposed method generates smooth trajectories for both healthy individuals and patients with mild to moderate Parkinson disease. The proposed method can be used in future robotic devices that integrate arm swing in gait rehabilitation of patients with walking impairments to improve the efficacy of their rehabilitation.
步态康复通常侧重于腿部，而忽略了上肢的作用。然而，各种研究已经证明了在健康行走和康复过程中手臂正确摆动的重要性。在本文中，我们描述了一种仅使用一个人的大腿角速度测量值实时生成适当的手臂摆动轨迹的方法，用于在以自选择的行走速度进行步态康复期间使用。使用频响方法开发了一个数据驱动的线性时不变传递函数，该函数捕获了大腿角速度与手臂角度（在矢状面中测量肩部）之间的频率相关幅度和相位关系，使用了 30 名健康成年受试者的数据集。我们表明，所提出的方法为健康个体和轻度至中度帕金森病患者生成了平滑的轨迹。该方法可用于未来的机器人设备中，这些设备将手臂摆动集成到步态康复中，以改善有行走障碍的患者的康复效果。
