School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China.
Collaborative Innovation Center of Henan Province for High-End Bearing, Luoyang 471003, China.
Sensors (Basel). 2024 Mar 25;24(7):2082. doi: 10.3390/s24072082.
The implementation of a progressive rehabilitation training model to promote patients' motivation efforts can greatly restore damaged central nervous system function in patients. Patients' active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb's movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient's expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient's healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient's ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human-robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient's movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient's healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient's pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements.
实施渐进式康复训练模式以促进患者的努力动机，可以极大地恢复患者受损的中枢神经系统功能。辅助按需（AAN）机器人康复训练可以有效地激发患者的主动参与。然而，其在机器人治疗中的应用受到了限制，因为其对机器人辅助扭矩的简单确定方法仅侧重于评估受影响肢体的运动能力。此外，辅助的预期效果取决于设计者，与患者的期望不符，并且其对不同患者的适用性不足。在本研究中，我们提出了一种基于估计和映射患者健康肢体的刚度的个性化治疗特征的控制方法。该控制方法包括基于运动需求的定量评估的任务导向空间中的交互控制模块和关节空间中的气动摆动缸的内循环位置控制模块。构建了上肢末端刚度估计模型，并设计了参数识别算法。通过收集患者运动过程中的表面肌电图（sEMG）信号和人机交互力，获得了表征患者完成训练运动能力的上肢末端刚度。然后，基于健康肢体的表现，对受影响肢体完成相同运动时的运动需求进行了量化。设计了一种刚度映射算法，根据受影响肢体的实际运动能力，动态调整机器人的康复训练轨迹和辅助力，实现 AAN 控制。在自主开发的气动上肢康复机器人上进行了实验研究，结果表明，所提出的 AAN 控制方法可以有效地估计患者的运动需求，并实现渐进式康复训练。这种康复训练机器人模拟了患者健康肢体的运动特征，驱动受影响的肢体，使康复训练任务的强度更符合患者发病前的肢体使用习惯，也有利于双侧肢体运动的一致性。
