  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.
