论文标题
SEMG手势识别具有简单的注意模型
sEMG Gesture Recognition with a Simple Model of Attention
论文作者
论文摘要
肌电控制是机器人假肢领域的主要研究领域之一。我们介绍了表面肌电图（SEMG）信号分类的研究，我们的简单和新颖的基于注意力的方法现在引导该行业，普遍击败更复杂的最新模型。我们的新型基于注意力的模型可在多个行业标准数据集中获得基准领先的结果，包括53个手指，手腕和抓住动作，从而改善了复杂的信号处理和基于CNN的方法。我们使用直接模型的强劲结果还表明，SEMG代表了未来机器学习研究的有希望的途径，不仅在假肢中应用，而且在其他重要领域，例如诊断和预后神经退行性疾病，计算介导的手术和先进的机器人控制。我们通过广泛的消融研究加强了这一建议，表明神经网络可以轻松从可负担得起的消费级传感器收集的嘈杂的SEMG数据中提取更高阶的时空特征。
Myoelectric control is one of the leading areas of research in the field of robotic prosthetics. We present our research in surface electromyography (sEMG) signal classification, where our simple and novel attention-based approach now leads the industry, universally beating more complex, state-of-the-art models. Our novel attention-based model achieves benchmark leading results on multiple industry-standard datasets including 53 finger, wrist, and grasping motions, improving over both sophisticated signal processing and CNN-based approaches. Our strong results with a straightforward model also indicate that sEMG represents a promising avenue for future machine learning research, with applications not only in prosthetics, but also in other important areas, such as diagnosis and prognostication of neurodegenerative diseases, computationally mediated surgeries, and advanced robotic control. We reinforce this suggestion with extensive ablative studies, demonstrating that a neural network can easily extract higher order spatiotemporal features from noisy sEMG data collected by affordable, consumer-grade sensors.
