Aoyagi Daisuke, Ichinose Wade E, Harkema Susan J, Reinkensmeyer David J, Bobrow James E
Los Amigos Research and Education Institute, Downey, CA 90242, USA.
IEEE Trans Neural Syst Rehabil Eng. 2007 Sep;15(3):387-400. doi: 10.1109/TNSRE.2007.903922.
Locomotor training using body weight support on a treadmill and manual assistance is a promising rehabilitation technique following neurological injuries, such as spinal cord injury (SCI) and stroke. Previous robots that automate this technique impose constraints on naturalistic walking due to their kinematic structure, and are typically operated in a stiff mode, limiting the ability of the patient or human trainer to influence the stepping pattern. We developed a pneumatic gait training robot that allows for a full range of natural motion of the legs and pelvis during treadmill walking, and provides compliant assistance. However, we observed an unexpected consequence of the device's compliance: unimpaired and SCI individuals invariably began walking out-of-phase with the device. Thus, the robot perturbed rather than assisted stepping. To address this problem, we developed a novel algorithm that synchronizes the device in real-time to the actual motion of the individual by sensing the state error and adjusting the replay timing to reduce this error. This paper describes data from experiments with individuals with SCI that demonstrate the effectiveness of the synchronization algorithm, and the potential of the device for relieving the trainers of strenuous work while maintaining naturalistic stepping.
在跑步机上利用体重支持并辅以人工辅助的运动训练，是脊髓损伤（SCI）和中风等神经损伤后一种很有前景的康复技术。以往实现该技术自动化的机器人因其运动结构对自然行走施加了限制，并且通常以僵硬模式运行，限制了患者或人类训练师影响步幅模式的能力。我们开发了一种气动步态训练机器人，它能在跑步机行走过程中使腿部和骨盆实现全方位自然运动，并提供顺应性辅助。然而，我们观察到该设备顺应性带来了一个意外结果：未受损个体和脊髓损伤个体总是与设备不同步行走。因此，机器人干扰而非辅助了行走。为解决这个问题，我们开发了一种新颖算法，通过感知状态误差并调整回放时间来实时使设备与个体的实际运动同步，以减少该误差。本文描述了对脊髓损伤个体进行实验的数据，这些数据证明了同步算法的有效性，以及该设备在减轻训练师繁重工作同时保持自然步幅的潜力。
