To cultivate professional sports referees, we develop a sports referee training system, which can recognize whether a trainee wearing the Myo armband makes correct judging signals while watching a prerecorded professional game. The system has to correctly recognize a set of gestures related to official referee's signals (ORSs) and another set of gestures used to intuitively interact with the system. These two gesture sets involve both large motion and subtle motion gestures, and the existing sensor-based methods using handcrafted features do not work well on recognizing all kinds of these gestures. In this work, deep belief networks (DBNs) are utilized to learn more representative features for hand gesture recognition, and selective handcrafted features are combined with the DBN features to achieve more robust recognition results. Moreover, a hierarchical recognition scheme is designed to first recognize the input gesture as a large or subtle motion gesture, and the corresponding classifiers for large motion gestures and subtle motion gestures are further used to obtain the final recognition result. Moreover, the Myo armband consists of eight-channel surface electromyography (sEMG) sensors and an inertial measurement unit (IMU), and these heterogeneous signals can be fused to achieve better recognition accuracy. We take basketball as an example to validate the proposed training system, and the experimental results show that the proposed hierarchical scheme considering DBN features of multimodality data outperforms other methods.

中文翻译：

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基于运动裁判训练的 EMG 和加速度传感器的分级手势识别框架

为了培养职业体育裁判员，我们开发了体育裁判员培训系统，该系统可以识别佩戴Myo臂章的学员在观看预先录制的职业比赛时是否做出正确的判断信号。系统必须正确识别一组与官方裁判信号（ORS）相关的手势，以及另一组用于直观地与系统交互的手势。这两个手势集涉及大动作和细微动作手势，现有的使用手工特征的基于传感器的方法在识别所有类型的这些手势方面效果不佳。在这项工作中，深度信念网络（DBN）被用来学习更具代表性的手势识别特征，并将选择性手工特征与 DBN 特征相结合，以实现更鲁棒的识别结果。而且，设计了一种分层识别方案，首先将输入的手势识别为大动作或细微动作手势，然后进一步使用对应的大动作手势和细微动作手势分类器得到最终的识别结果。此外，Myo 臂章由八通道表面肌电（sEMG）传感器和惯性测量单元（IMU）组成，这些异质信号可以融合在一起，以达到更好的识别精度。我们以篮球为例来验证所提出的训练系统，实验结果表明，所提出的考虑多模态数据的 DBN 特征的分层方案优于其他方法。并进一步使用相应的大动作手势和细微动作手势分类器得到最终的识别结果。此外，Myo 臂章由八通道表面肌电（sEMG）传感器和惯性测量单元（IMU）组成，这些异质信号可以融合在一起，以达到更好的识别精度。我们以篮球为例来验证所提出的训练系统，实验结果表明，所提出的考虑多模态数据的 DBN 特征的分层方案优于其他方法。并进一步使用相应的大动作手势和细微动作手势分类器得到最终的识别结果。此外，Myo 臂章由八通道表面肌电（sEMG）传感器和惯性测量单元（IMU）组成，这些异质信号可以融合在一起，以达到更好的识别精度。我们以篮球为例来验证所提出的训练系统，实验结果表明，所提出的考虑多模态数据的 DBN 特征的分层方案优于其他方法。并且可以融合这些异构信号以达到更好的识别精度。我们以篮球为例来验证所提出的训练系统，实验结果表明，所提出的考虑多模态数据的 DBN 特征的分层方案优于其他方法。并且可以融合这些异构信号以达到更好的识别精度。我们以篮球为例来验证所提出的训练系统，实验结果表明，所提出的考虑多模态数据的 DBN 特征的分层方案优于其他方法。