School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, China.
Department of Mechanical Engineering, University of Auckland, Auckland 1142, New Zealand.
J Healthc Eng. 2017;2017:6819056. doi: 10.1155/2017/6819056. Epub 2017 Sep 17.
This study aims to establish a steady-state visual evoked potential- (SSVEP-) based passive training protocol on an ankle rehabilitation robot and validate its feasibility.
This paper combines SSVEP signals and the virtual reality circumstance through constructing information transmission loops between brains and ankle robots. The robot can judge motion intentions of subjects and trigger the training when subjects pay their attention on one of the four flickering circles. The virtual reality training circumstance provides real-time visual feedback of ankle rotation.
All five subjects succeeded in conducting ankle training based on the SSVEP-triggered training strategy following their motion intentions. The lowest success rate is 80%, and the highest one is 100%. The lowest information transfer rate (ITR) is 11.5 bits/min when the biggest one of the robots for this proposed training is set as 24 bits/min.
The proposed training strategy is feasible and promising to be combined with a robot for ankle rehabilitation. Future work will focus on adopting more advanced data process techniques to improve the reliability of intention detection and investigating how patients respond to such a training strategy.
本研究旨在建立一种基于稳态视觉诱发电位(SSVEP)的踝关节康复机器人被动训练方案,并验证其可行性。
本文通过在大脑和踝关节机器人之间构建信息传输回路,将 SSVEP 信号与虚拟现实环境相结合。当受试者将注意力集中在四个闪烁圆圈中的一个上时,机器人可以判断受试者的运动意图并触发训练。虚拟现实训练环境提供踝关节旋转的实时视觉反馈。
所有 5 名受试者均根据 SSVEP 触发的训练策略成功地按照运动意图进行了踝关节训练。最低成功率为 80%,最高成功率为 100%。当将此训练方案中最大的机器人设置为 24 位/分时,最低信息传输率(ITR)为 11.5 位/分。
提出的训练策略是可行的,有望与机器人结合用于踝关节康复。未来的工作将集中采用更先进的数据处理技术来提高意图检测的可靠性,并研究患者对这种训练策略的反应。