Conceitos essenciais
The author proposes a novel SNN method for high-speed, low-consumption micro-gesture recognition using sEMG signals, achieving superior accuracy and efficiency compared to traditional methods.
Resumo
The content discusses the importance of gesture recognition on wearable devices and introduces a novel method using spiking neural networks (SNN) for efficient micro-gesture recognition. The proposed method includes adaptive multi-delta coding, TAD-LIF algorithm for action detection, and an SNN structure with additive solvers. Experimental results show higher accuracy and lower power consumption compared to CNN and other SNN-based methods.
The paper highlights the challenges in capturing micro-gestures accurately and efficiently on wearable devices. It emphasizes the significance of EMG signals in providing natural interaction with devices. The proposed SNN method offers a solution for precise, high-speed, and low-power micro-gesture recognition tasks suitable for consumer-level wearables.
Key points include the comparison of different sensors for gesture recognition, the use of EMG signals for natural interaction, challenges faced by traditional methods in balancing accuracy and performance, introduction of spiking neural networks as a solution, experimental results showcasing improved accuracy and efficiency with the proposed method.
Estatísticas
The accuracy of the proposed SNN is 83.85% and 93.52% on two datasets respectively.
The inference latency of the proposed SNN is about 1% of CNN.
The power consumption is about 0.1% of CNN.
The memory occupation is about 20% of CNN.
Citações
"The proposed SNN has higher recognition accuracy than CNN."
"The inference latency of the proposed SNN is significantly lower than traditional methods."
"The power consumption of the proposed SNN is extremely lower than CNN."