Hand movement classification via surface electromyographic (sEMG) signal is a well-established approach for advanced Human-Computer Interaction. However, sEMG movement recognition has to deal with the long-term reliability of sEMG-based control, limited by the variability affecting the sEMG signal. Embedded solutions are affected by a recognition accuracy drop over time that makes them unsuitable for reliable gesture controller design. In this paper, we present a complete wearable-class embedded system for robust sEMG-based gesture recognition, based on Temporal Convolutional Networks (TCNs). Firstly, we developed a novel TCN topology (TEMPONet), and we tested our solution on a benchmark dataset (Ninapro), achieving 49.6% average accuracy, 7.8%, better than current State-Of-the-Art (SoA). Moreover, we designed an energy-efficient embedded platform based on GAP8, a novel 8-core IoT processor. Using our embedded platform, we collected a second 20-sessions dataset to validate the system on a setup which is representative of the final deployment. We obtain 93.7% average accuracy with the TCN, comparable with a SoA SVM approach (91.1%). Finally, we profiled the performance of the network implemented on GAP8 by using an 8-bit quantization strategy to fit the memory constraint of the processor. We reach a $\mathbf {4\times }$ lower memory footprint (460 kB) with a performance degradation of only 3% accuracy. We detailed the execution on the GAP8 platform, showing that the quantized network executes a single classification in 12.84 ms with a power envelope of 0.9 mJ, making it suitable for a long-lifetime wearable deployment.

中文翻译：

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在多核IoT处理器上使用时间卷积网络的强大的实时嵌入式EMG识别框架。

通过表面肌电图（sEMG）信号进行的手部运动分类是一种先进的人机交互方法。但是，sEMG运动识别必须处理基于sEMG的控制的长期可靠性，受到影响sEMG信号的可变性的限制。嵌入式解决方案会随着时间的推移而受到识别精度下降的影响，这使其不适用于可靠的手势控制器设计。在本文中，我们基于时间卷积网络（TCN），提出了一个完整的可穿戴类嵌入式系统，用于基于鲁棒sEMG的手势识别。首先，我们开发了新颖的TCN拓扑（TEMPONet），并在基准数据集（Ninapro）上测试了我们的解决方案，其平均准确度达到49.6％，比当前的最新技术（SoA）改善了7.8％。此外，我们设计了基于GAP8（一种新型的8核IoT处理器）的节能嵌入式平台。使用我们的嵌入式平台，我们收集了第二个20个会话的数据集，以在代表最终部署的设置上验证系统。我们使用TCN可获得93.7％的平均准确度，可与SoA SVM方法相媲美（91.1％）。最后，我们通过使用8位量化策略来适应处理器的内存限制，分析了在GAP8上实现的网络的性能。我们达到了 我们通过使用8位量化策略来满足处理器的内存限制，分析了在GAP8上实现的网络的性能。我们达到了 我们通过使用8位量化策略来满足处理器的内存限制，分析了在GAP8上实现的网络的性能。我们达到了$ \ mathbf {4 \ times} $较低的内存占用空间（460 kB），而性能下降的准确性仅为3％。我们详细介绍了GAP8平台上的执行情况，结果表明量化网络在12.84毫秒内执行了单个分类，功率包络为0.9 mJ，使其非常适合长期可穿戴部署。