Objective Previous studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with assistive technologies. Despite the high estimation accuracy, current methods based on neural networks need to be trained with specific motor unit action potential (MUAP) shapes updated for each condition (i.e., varying muscle contraction intensities or joint angles). This preliminary step dramatically limits the potential generalization of these algorithms across tasks. We propose a novel approach to estimate the neural drive using a deep convolutional neural network (CNN), which can identify the cumulative spike train (CST) through general features of MUAPs from a pool of motor units.

中文翻译：

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一个深度 CNN 框架，用于从 HD-EMG 跨收缩强度和关节角度进行神经驱动估计

客观的先前的研究表明，使用高密度肌电图 (HD-EMG) 来估计肌肉的神经驱动力，即支配肌肉的所有运动神经元的净输出，以与辅助技术连接，取得了可喜的结果。尽管估计精度很高，但当前基于神经网络的方法需要针对每种情况（即不同的肌肉收缩强度或关节角度）更新特定的运动单位动作电位 (MUAP) 形状进行训练。这一初步步骤极大地限制了这些算法跨任务的潜在泛化。我们提出了一种使用深度卷积神经网络 (CNN) 估计神经驱动的新方法，该方法可以通过来自运动单元池的 MUAP 的一般特征来识别累积脉冲序列 (CST)。