Li Yurong, Yang Xu, Zhou Yuezhu, Chen Jun, Du Min, Yang Yuan
IEEE J Biomed Health Inform. 2021 Jan;25(1):59-68. doi: 10.1109/JBHI.2020.2989747. Epub 2021 Jan 5.
Functional electrical stimulation (FES) provides an effective way for foot drop (FD) correction. To overcome the redundant and blind stimulation problems in the state-of-the-art methods, this study proposes a closed-loop scheme for an adaptive electromyography (EMG)-modulated stimulation profile. The developed method detects real-time angular velocity during walking. It provides feedbacks to a long short-term memory (LSTM) neural network for predicting synchronous tibialis anterior (TA) EMG. Based on the prediction, it modulates the stimulation intensity, taking into account of the subject-specific dead zone and saturation of the electrically evoked activation. The proposed method is tested on ten able-bodied participants and six FD subjects as proof of concept. The experimental results show that the proposed method can successfully induce the dorsiflexion of the ankle joint, and generate an activation pattern similar to a natural gait, with the mean Correlation Coefficient of 0.9021. Thus, the proposed method has the potential to help patients to retrieve normal gait.
功能性电刺激(FES)为矫正足下垂(FD)提供了一种有效方法。为克服现有方法中冗余和盲目刺激的问题,本研究提出了一种用于自适应肌电图(EMG)调制刺激曲线的闭环方案。所开发的方法在行走过程中检测实时角速度。它向长短期记忆(LSTM)神经网络提供反馈,以预测同步胫前肌(TA)肌电图。基于该预测,它在考虑个体特定死区和电诱发激活饱和度的情况下调制刺激强度。作为概念验证,在十名健全参与者和六名足下垂受试者身上对所提出的方法进行了测试。实验结果表明,所提出的方法能够成功诱发踝关节背屈,并产生与自然步态相似的激活模式,平均相关系数为0.9021。因此,所提出的方法具有帮助患者恢复正常步态的潜力。