IEEE Trans Neural Syst Rehabil Eng. 2023;31:2381-2390. doi: 10.1109/TNSRE.2023.3273819. Epub 2023 May 25.
Robot-aided gait training (RAGT) plays a crucial role in providing high-dose and high-intensity task-oriented physical therapy. The human-robot interaction during RAGT remains technically challenging. To achieve this aim, it is necessary to quantify how RAGT impacts brain activity and motor learning. This work quantifies the neuromuscular effect induced by a single RAGT session in healthy middle-aged individuals. Electromyographic (EMG) and motion (IMU) data were recorded and processed during walking trials before and after RAGT. Electroencephalographic (EEG) data were recorded during rest before and after the entire walking session. Linear and nonlinear analyses detected changes in the walking pattern, paralleled by a modulation of cortical activity in the motor, attentive, and visual cortices immediately after RAGT. Increases in alpha and beta EEG spectral power and pattern regularity of the EEG match the increased regularity of body oscillations in the frontal plane, and the loss of alternating muscle activation during the gait cycle, when walking after a RAGT session. These preliminary results improve the understanding of human-machine interaction mechanisms and motor learning and may contribute to more efficient exoskeleton development for assisted walking.
机器人辅助步态训练(RAGT)在提供高剂量和高强度以任务为导向的物理治疗方面发挥着至关重要的作用。RAGT 期间的人机交互仍然具有技术挑战性。为了实现这一目标,有必要量化 RAGT 如何影响大脑活动和运动学习。这项工作量化了单次 RAGT 会话对健康中年个体的神经肌肉影响。在 RAGT 前后的步行试验中记录和处理肌电图(EMG)和运动(IMU)数据。在整个步行过程前后的休息期间记录脑电图(EEG)数据。线性和非线性分析检测到行走模式的变化,同时在 RAGT 后立即调制运动、注意和视觉皮层的皮质活动。EEG 的 alpha 和 beta 频带功率增加以及 EEG 的模式规律性与身体在前平面上的振荡规律性增加以及步态周期中交替肌肉激活的丧失相匹配,当进行 RAGT 会话后行走时。这些初步结果提高了对人机交互机制和运动学习的理解,并可能有助于更有效地开发用于辅助行走的外骨骼。