Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG

Introduction: This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user’s intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.

引言：本研究开发了一种基于表面肌电信号（sEMG）的运动意图控制新型膝关节外骨骼机器人（robotic knee exoskeleton），该装置采用导纳控制方式，为运动功能减退人群提供辅助以改善其运动机能。临床研究表明，此类与人体神经肌肉骨骼系统持续交互的辅助装置，可有效提升功能代偿效果与康复效果。借此，使用者可成为训练与康复过程中的主动参与者，提升其参与度并改善神经可塑性。为实现下肢运动意图识别与膝关节运动辨识，本研究采用下肢与躯干的表面肌电信号作为控制依据，这为机器人辅助装置的控制提供了一种全新方案。
方法：本研究开发了一套控制系统，其中包含基于膝关节相关运动类别分类技术的人体运动意图识别（HMIR）模块。为将使用者的运动意图转化为膝关节外骨骼机器人的期望运行状态，该系统还集成了有限状态机、导纳控制器、速度控制器与轨迹控制器，并具备根据使用者意图终止运动的功能。
结果：所提出的人体运动意图识别模型在分类下肢运动类别时，下肢肌肉的识别准确率介于76%至83%之间，躯干肌肉的识别准确率介于71%至77%之间。控制器的实验结果表明，本研究提出的导纳控制器可在50%的步态周期内为膝关节提供支撑，并准确辅助各类膝关节运动。
结论：本研究提出的膝关节外骨骼机器人是一种基于下肢与躯干表面肌电信号增强膝关节运动能力的全新方案。