Gesture recognition on wearable devices is extensively applied in human-computer interaction. Electromyography (EMG) has been used in many gesture recognition systems for its rapid perception of muscle signals. However, analyzing EMG signals on devices, like smart wristbands, usually needs inference models to have high performances, such as low inference latency, low power consumption, and low memory occupation. Therefore, this paper proposes an improved spiking neural network (SNN) to achieve these goals. We propose an adaptive multi-delta coding as a spiking coding method to improve recognition accuracy. We propose two additive solvers for SNN, which can reduce inference energy consumption and amount of parameters significantly, and improve the robustness of temporal differences. In addition, we propose a linear action detection method TAD-LIF, which is suitable for SNNs. TAD-LIF is an improved LIF neuron that can detect transient-state gestures quickly and accurately. We collected two datasets from 20 subjects including 6 micro gestures. The collection devices are two designed lightweight consumer-level sEMG wristbands (3 and 8 electrode channels respectively). Compared to CNN, FCN, and normal SNN-based methods, the proposed SNN has higher recognition accuracy. The accuracy of the proposed SNN is 83.85% and 93.52% on the two datasets respectively. In addition, the inference latency of the proposed SNN is about 1% of CNN, the power consumption is about 0.1% of CNN, and the memory occupation is about 20% of CNN. The proposed methods can be used for precise, high-speed, and low-power micro-gesture recognition tasks, and are suitable for consumer-level intelligent wearable devices, which is a general way to achieve ubiquitous computing.

翻译：可穿戴设备上的手势识别在人机交互中应用广泛。肌电信号因其对肌肉信号的快速感知能力，已被应用于许多手势识别系统中。然而，在智能手环等设备上分析肌电信号通常需要推理模型具备低推理延迟、低功耗和低内存占用等高性能。为此，本文提出一种改进的脉冲神经网络以实现这些目标。我们提出自适应多增量编码作为脉冲编码方法以提高识别精度。提出两种SNN加法求解器，可显著降低推理能耗和参数量，并增强时间差异的鲁棒性。此外，提出一种适用于SNN的线性动作检测方法TAD-LIF。TAD-LIF是改进的LIF神经元，可快速准确地检测瞬态手势。我们从20名受试者中采集了两个数据集，包含6种微手势。采集设备为两款轻量级消费级肌电手环（分别具有3和8个电极通道）。与基于CNN、FCN和常规SNN的方法相比，本文提出的SNN具有更高的识别准确率。该SNN在两个数据集上的准确率分别为83.85%和93.52%。此外，该SNN的推理延迟约为CNN的1%，功耗约为0.1%，内存占用约为20%。所提方法可用于精确、高速、低功耗的微手势识别任务，适用于消费级智能可穿戴设备，是实现普适计算的通用方案。