Samadani Ali
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1-4. doi: 10.1109/EMBC.2018.8512531.
Electromyographic activities (EMG) generated during contraction of upper limb muscles can be mapped to distinct hand gestures and movements, posing them as a promising modality for prosthetic and cybernetic applications. This paper presents a comparative analysis between different recurrent neural network (RNN) configurations for EMG-based hand gesture classification. In particular, RNNs with recurrent units of long short-term memory (LSTM) and gated recurrent unit (GRU) are evaluated. Furthermore, the effects of an attention mechanism and varying learning rates are evaluated. Results show a classifier 1) with a bidirectional recurrent layer composed of LSTM units, 2) that applies the attention mechanism, and 3) trained with step-wise learning rate outperforms all other tested RNN classifiers.
上肢肌肉收缩时产生的肌电活动（EMG）可以映射到不同的手势和动作，使其成为假肢和控制论应用中一种很有前景的方式。本文对基于肌电的手势分类的不同循环神经网络（RNN）配置进行了比较分析。具体而言，评估了具有长短期记忆（LSTM）循环单元和门控循环单元（GRU）的RNN。此外，还评估了注意力机制和不同学习率的影响。结果表明，一个由LSTM单元组成的双向循环层、应用注意力机制且采用逐步学习率训练的分类器优于所有其他测试的RNN分类器。
