With the aid of various physical and biological sensors, research is actively being conducted to understand the intention of wearer’s motions through parameters such as joint torque. sEMG signals can be measured faster than physical sensors, which are often used in the field of behavioral intent identification studies. However, electrodes must be placed in the correct positions, and due to the high volume of noise, professional knowledge and accurate hardware design are required. In this paper, a system is constructed to improve the sEMG signal measurement environment by producing small multichannel sEMG modules. In addition, deep neural network supervised learning algorithms are implemented to estimate the torque using only the sEMG signal. Based on this, we analyze the organization of algorithms, the processing of the sEMG data, and how the number of channels affects learning. The optimal deep natural network model selected by the analysis is implanted to embedded after learning. The implanted model performs a portable real-time torque optimization (PRTE) according to the sEMG signal entered. In this paper, we study the deep natural network algorithm for estimating sEMG hardware and torque, and how it is implanted into a portable embedded system for use in estimating real-time motion intent. The proposed deep natural network algorithm and the usefulness of the PRTE system are verified through experiments.

中文翻译：

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使用 sEMG 和深度神经网络的联合扭矩估计

借助各种物理和生物传感器，正在积极开展研究，以通过关节扭矩等参数了解佩戴者的运动意图。sEMG 信号的测量速度比物理传感器更快，物理传感器通常用于行为意图识别研究领域。但是，电极必须放置在正确的位置，并且由于噪声量大，需要专业知识和准确的硬件设计。在本文中，构建了一个通过生产小型多通道 sEMG 模块来改善 sEMG 信号测量环境的系统。此外，实施深度神经网络监督学习算法以仅使用 sEMG 信号来估计扭矩。在此基础上，我们分析算法的组织，sEMG数据的处理，以及通道数量如何影响学习。分析选出的最优深度自然网络模型，学习后植入嵌入。植入模型根据输入的 sEMG 信号执行便携式实时扭矩优化 (PRTE)。在本文中，我们研究了用于估计 sEMG 硬件和扭矩的深度自然网络算法，以及如何将其植入便携式嵌入式系统中以用于估计实时运动意图。通过实验验证了所提出的深度自然网络算法和PRTE系统的有用性。我们研究了用于估计 sEMG 硬件和扭矩的深度自然网络算法，以及如何将其植入便携式嵌入式系统中以用于估计实时运动意图。通过实验验证了所提出的深度自然网络算法和PRTE系统的有用性。我们研究了用于估计 sEMG 硬件和扭矩的深度自然网络算法，以及如何将其植入便携式嵌入式系统中以用于估计实时运动意图。通过实验验证了所提出的深度自然网络算法和PRTE系统的有用性。