Li Zhan, Guiraud David, Andreu David, Benoussaad Mourad, Fattal Charles, Hayashibe Mitsuhiro
INRIA, University of Montpellier, 860 rue St Priest, Montpellier Cedex 5, 34095, France.
School of Automation Engineering, University of Electronic Science and Technology of China, Xiyuan Ave 2006, Chengdu, 611731, China.
J Neuroeng Rehabil. 2016 Jun 22;13(1):60. doi: 10.1186/s12984-016-0169-y.
Functional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respectively on muscles or nerves so that muscle activity can be characterized by the synchronous recruitment of motor units with its compound electromyography (EMG) signal is called M-wave. The recorded evoked EMG (eEMG) can be employed to predict the resultant joint torque, and modeling of FES-induced joint torque based on eEMG is an essential step to provide necessary prediction of the expected muscle response before achieving accurate joint torque control by FES.
Previous works on FES-induced torque tracking issues were mainly based on offline analysis. However, toward personalized clinical rehabilitation applications, real-time FES systems are essentially required considering the subject-specific muscle responses against electrical stimulation. This paper proposes a wireless portable stimulator used for estimating/predicting joint torque based on real time processing of eEMG. Kalman filter and recurrent neural network (RNN) are embedded into the real-time FES system for identification and estimation.
Prediction results on 3 able-bodied subjects and 3 SCI patients demonstrate promising performances. As estimators, both Kalman filter and RNN approaches show clinically feasible results on estimation/prediction of joint torque with eEMG signals only, moreover RNN requires less computational requirement.
The proposed real-time FES system establishes a platform for estimating and assessing the mechanical output, the electromyographic recordings and associated models. It will contribute to open a new modality for personalized portable neuroprosthetic control toward consolidated personal healthcare for motor-impaired patients.
功能性电刺激（FES）是一种神经假体技术，通过向脊髓损伤（SCI）患者和运动功能受损者的瘫痪肌肉或运动神经传递短电脉冲，来恢复其丧失的运动功能。FES分别在肌肉或神经上诱发动作电位，从而使肌肉活动可通过运动单位的同步募集来表征，其复合肌电图（EMG）信号称为M波。记录的诱发肌电图（eEMG）可用于预测产生的关节扭矩，基于eEMG对FES诱发的关节扭矩进行建模是在通过FES实现精确关节扭矩控制之前，对预期肌肉反应进行必要预测的关键步骤。
以往关于FES诱发扭矩跟踪问题的研究主要基于离线分析。然而，对于个性化临床康复应用，考虑到个体对电刺激的肌肉反应，实时FES系统是必不可少的。本文提出一种基于eEMG实时处理用于估计/预测关节扭矩的无线便携式刺激器。卡尔曼滤波器和循环神经网络（RNN）被嵌入到实时FES系统中用于识别和估计。
对3名健全受试者和3名SCI患者的预测结果显示出良好的性能。作为估计器，卡尔曼滤波器和RNN方法仅使用eEMG信号在关节扭矩估计/预测方面均显示出临床可行的结果，而且RNN的计算需求更少。
所提出的实时FES系统建立了一个用于估计和评估机械输出、肌电图记录及相关模型的平台。它将有助于为运动功能受损患者的综合个人医疗保健开启个性化便携式神经假体控制的新模式。
