Department of Systems Engineering and Automation, Carlos III University of Madrid, 28911 Leganés, Madrid, Spain.
Sensors (Basel). 2018 Aug 2;18(8):2522. doi: 10.3390/s18082522.
A high-level control algorithm capable of generating position and torque references from surface electromyography signals (sEMG) was designed. It was applied to a shape memory alloy (SMA)-actuated exoskeleton used in active rehabilitation therapies for elbow joints. The sEMG signals are filtered and normalized according to data collected online during the first seconds of a therapy session. The control algorithm uses the sEMG signals to promote active participation of patients during the therapy session. In order to generate the reference position pattern with good precision, the sEMG normalized signal is compared with a pressure sensor signal to detect the intention of each movement. The algorithm was tested in simulations and with healthy people for control of an elbow exoskeleton in flexion⁻extension movements. The results indicate that sEMG signals from elbow muscles, in combination with pressure sensors that measure arm⁻exoskeleton interaction, can be used as inputs for the control algorithm, which adapts the reference for exoskeleton movements according to a patient's intention.
设计了一种能够根据表面肌电信号 (sEMG) 生成位置和扭矩参考的高级控制算法。该算法应用于一种用于肘关节主动康复治疗的形状记忆合金 (SMA) 驱动的外骨骼。根据治疗过程开始时的在线数据，对 sEMG 信号进行滤波和归一化处理。控制算法利用 sEMG 信号促进患者在治疗过程中的积极参与。为了生成具有良好精度的参考位置模式，将归一化后的 sEMG 信号与压力传感器信号进行比较，以检测每个运动的意图。该算法在模拟和健康人身上进行了测试，用于控制肘部外骨骼的屈伸运动。结果表明，来自肘部肌肉的 sEMG 信号，结合测量手臂-外骨骼相互作用的压力传感器，可以作为控制算法的输入，根据患者的意图调整外骨骼运动的参考。
