论文标题
来自SEMG传感器数据的连续手势识别具有复发性神经网络和对抗领域的适应性
Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation
论文作者
论文摘要
近年来，人工四肢的运动控制取得了巨大进步。新的传感器和控制技术增强了人造四肢的功能和实用性，即可以在有限的程度上执行复杂的运动（例如抓握）。迄今为止，最成功的结果是通过应用复发性神经网络（RNN）实现的。但是，在人造手的领域，到目前为止的实验仅限于非摩规手腕，这大大降低了此类假体的功能。在本文中，我们首次使用移动和非移动手腕介绍了手势识别的经验结果。此外，我们证明，在两种情况下，在手势识别的精度方面，具有简单复发单元（SRU）的复发性神经网络在两种情况下都优于常规RNN，这是由手臂传感电磁信号从手臂肌肉（通过表面骨术或SEMG）获取的数据。最后，我们表明，将域适应技术添加到连续的手势识别中，可以提高受试者之间的转移能力，在这种情况下，一个对另一个人的数据训练的肢体控制器。
Movement control of artificial limbs has made big advances in recent years. New sensor and control technology enhanced the functionality and usefulness of artificial limbs to the point that complex movements, such as grasping, can be performed to a limited extent. To date, the most successful results were achieved by applying recurrent neural networks (RNNs). However, in the domain of artificial hands, experiments so far were limited to non-mobile wrists, which significantly reduces the functionality of such prostheses. In this paper, for the first time, we present empirical results on gesture recognition with both mobile and non-mobile wrists. Furthermore, we demonstrate that recurrent neural networks with simple recurrent units (SRU) outperform regular RNNs in both cases in terms of gesture recognition accuracy, on data acquired by an arm band sensing electromagnetic signals from arm muscles (via surface electromyography or sEMG). Finally, we show that adding domain adaptation techniques to continuous gesture recognition with RNN improves the transfer ability between subjects, where a limb controller trained on data from one person is used for another person.
