论文标题
基于CNN注意的轻度加权通过肌电图识别手势识别的结构
Light-weighted CNN-Attention based architecture for Hand Gesture Recognition via ElectroMyography
论文作者
论文摘要
生物信号处理（BSP）和机器学习（ML）模型的进步已为新型沉浸式人机界面（HMI）的发展铺平了道路。在这种情况下，利用表面 - 电解图（SEMG）信号的手势识别（HGR）引起了重大兴趣。这是由于它具有解码可穿戴数据来解释人类沉浸在混合现实（MR）环境中的独特潜力。为了达到最高的准确性，通常开发复杂且重加权的深度神经网络（DNN），这限制了其在低功率和资源约束的可穿戴系统中的实际应用。在这项工作中，我们提出了基于卷积神经网络（CNN）和注意机制的轻加权混合体系结构（HDCAM），以有效提取输入的局部和全局表示。提出的具有58,441个参数的HDCAM模型达到了新的最新性能（SOTA）性能，窗口尺寸为300 ms，精度为81.28％，分类为17个手势。训练所提出的HDCAM架构的参数数量比以前的SOTA对应物少18.87倍。
Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML) models have paved the path for development of novel immersive Human-Machine Interfaces (HMI). In this context, there has been a surge of significant interest in Hand Gesture Recognition (HGR) utilizing Surface-Electromyogram (sEMG) signals. This is due to its unique potential for decoding wearable data to interpret human intent for immersion in Mixed Reality (MR) environments. To achieve the highest possible accuracy, complicated and heavy-weighted Deep Neural Networks (DNNs) are typically developed, which restricts their practical application in low-power and resource-constrained wearable systems. In this work, we propose a light-weighted hybrid architecture (HDCAM) based on Convolutional Neural Network (CNN) and attention mechanism to effectively extract local and global representations of the input. The proposed HDCAM model with 58,441 parameters reached a new state-of-the-art (SOTA) performance with 82.91% and 81.28% accuracy on window sizes of 300 ms and 200 ms for classifying 17 hand gestures. The number of parameters to train the proposed HDCAM architecture is 18.87 times less than its previous SOTA counterpart.
