Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named ‘Low-Complex Movement recognition-Net’ (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing. Methods: The proposed framework was validated on two different datasets- our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin- Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT’s), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and low-complex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons.

中文翻译：

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Locomo-Net：一种低复杂度的深度学习框架，用于基于 sEMG 的手部运动识别以进行假肢控制


背景：基于深度学习算法的肌电模式识别技术性能的增强具有计算成本高且表现出广泛的记忆行为。因此，在本文中，我们报告了一种名为“低复杂运动识别网络”（LoCoMo-Net）的深度学习框架，该框架使用卷积神经网络（CNN）构建，用于识别手腕和手指弯曲运动；抓握和功能性动作；和单通道表面肌电图 (sEMG) 记录的力模式。该网络由两级管道组成：1）输入数据压缩； 2）数据驱动的权重共享。方法：所提出的框架在两个不同的数据集上进行了验证——我们自己的数据集 (DS1) 和公开可用的 NinaPro 数据集 (DS2)，分别针对 16 个动作和 50 个动作。此外，我们还在 Virtex-7 Xilinx 现场可编程门阵列 (FPGA) 平台上对提议的 LoCoMo-Net 进行了原型设计，并针对 DS1 的 15 个动作进行了验证，以证明其实时执行的可行性。结果：通过使用相同数据集与基准模型进行比较分析，验证了所提出的 LoCoMo-Net 的有效性，其中我们提出的模型优于双支持向量机 (SVM) 和现有的基于 CNN 的模型，平均分类精度为 8.5%分别为 16.0% 和 16.0%。此外，还进行了硬件复杂性分析，揭示了两级流水线的优势，其中查找表 (LUT)、寄存器、内存和功耗分别节省了约 27%、49%、50%、23% 和 43%和计算时间分别。 结论：这种基于 sEMG 的准确且低复杂度的运动识别系统的临床意义有利于截肢者生活质量的潜在改善。