Real-time isometric finger extension force estimation based on motor unit discharge information
Real-time isometric finger extension force estimation based on motor unit discharge information
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DOI：
10.1088/1741-2552/ab2c55
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发表时间：
2019-12-01
影响因子：
4
通讯作者：
Hu，Xiaogang
中科院分区：
文献类型：
--
作者：
Zheng，Yang;Hu，Xiaogang
目的利用运动单元（mu）放电信息实时估计手指等距伸指力。方法提出了一种基于快速独立分量分析（FastICA）算法的实时肌电分解方法，从高密度（HD）肌电记录中提取MU放电事件。首先在初始化期间离线进行分解，然后将得到的分离矩阵实时应用于新的数据样本。由于MU池放电概率反映了脊髓运动神经元的神经驱动，因此根据初始化过程中建立的射速-力模型（称为神经驱动法）估计单个手指的力。采用传统的基于肌电振幅的方法来估计力作为比较，称为肌电振幅法。首先使用模拟HD-EMG信号来评估实时分解的准确性。采用假随机力水平下5分钟等距手指伸展的实验肌电记录来评估力估计随时间的表现。仿真结果表明，实时分解准确率为86%，离线分解准确率为94%。然而，随着时间的推移，实时分解精度是稳定的。实验结果表明，与肌电振幅法相比，神经驱动法的力估计均方根误差（RMSE）显著小于肌电振幅法，且在手指间具有一致性。此外，神经驱动法的RMSE在230 s前保持稳定，而肌电振幅法的RMSE随时间逐渐增加。意义与传统肌电振幅法相比，神经驱动法在长时间肌肉收缩时对手指力的实时估计更准确。研究结果可能为基于MU池流量信息的可靠神经机器交互提供更准确和鲁棒的神经接口技术。
ObjectiveThe goal of this study was to perform real-time estimation of isometric finger extension force using the discharge information of motor units (MUs).ApproachA real-time electromyogram (EMG) decomposition method based on the fast independent component analysis (FastICA) algorithm was developed to extract MU discharge events from high-density (HD) EMG recordings. The decomposition was first performed offline during an initialization period, and the obtained separation matrix was then applied to new data samples in real-time. Since MU pool discharge probability reflects the neural drive to spinal motoneurons, individual finger forces were estimated based on a firing rate-force model established during the initialization, termed the neural-drive method. The conventional EMG amplitude-based method was used to estimate the forces as a comparison, termed the EMG-amplitude method. Simulated HD-EMG signals were first used to evaluate the accuracy of the real-time decomposition. Experimental EMG recordings of 5 min of isometric finger extension with pseudorandom force levels were used to assess the performance of force estimation over time.Main resultsThe simulation results showed that the accuracy of real-time decomposition was 86%, compared with an offline accuracy of 94%. However, the real-time decomposition accuracy was stable over time. The experimental results showed that the neural-drive method had a significantly smaller root mean square error (RMSE) of the force estimation compared with the EMG-amplitude method, which was consistent across fingers. Additionally, the RMSE of the neural-drive method was stable until 230 s, while the RMSE of the EMG-amplitude method increased progressively over time.SignificanceThe neural-drive method on real-time finger force estimation was more accurate over time compared with the conventional EMG-amplitude method during prolonged muscle contractions. The outcomes can potentially offer a more accurate and robust neural interface technique for reliable neural-machine interactions based on MU pool discharge information.