一种基于单通道sEMG分解与LSTM神经网络相结合的手势识别方法

Gesture recognition by Single-Channel sEMG Decomposition and LSTM Network

在基于表面肌电(sEMG)信号的动作识别中,使用单通道传感器能够简化系统、减少识别延时,但也存在识别精度偏低的问题。为了提高识别精度,本文提出将单通道sEMG信号分解策略与长短期记忆(LSTM)循环神经网络识别相结合的方法。在该方法中,先将单通道sEMG信号分解成多通道运动单元动作电位序列(MUAPTs),然后提取MUAPTs的特征,最后将这些特征对LSTM分类模型进行训练。为了验证该方法的有效性,本文以手势动作识别为对象,对6名受试者分别建立了4种分类模型,包括基于未分解信号的支持向量机(SVM)、基于分解信号的SVM、基于未分解信号的LSTM、以及本文提出的基于分解信号的LSTM,并定义识别精度量化指标对这四种模型的分类结果进行评估。对于旋前方肌sEMG信号,在使用本文所提方法进行手势识别时,平均估计精度均能达到90%以上,比未分解的LSTM高18.7%,比分解信号的SVM高4.17%,比未分解信号的SVM高11.53%。实验结果验证了本文所提方法的有效性。

For motion recognition based on the surface electromyography(sEMG), reducing the channel number of sEMG electrodes could simplify the target hardware implementation, and improve the rapid response performance. However, it also has the disadvantage of coarse accuracy. In this study, we propose a sEMG recognition method by combining the single-channel sEMG decomposition and the long short-term memory(LSTM) recurrent neural networks. Firstly, the single-channel sEMG signals are decomposed into motor unit action potential trains(MUAPTs). Then, features are extracted from the MUAPTs, and set as inputs to train the LSTM classification model. Experiments are conducted on 6 candidates with respect to the gesture recognition scenario. Five gestures are considered as outputs of the model. Experimental results of the proposed method are extensively compared with those obtained by other three schemes, including support vector machine(SVM) with non-decomposition data, SVM with decomposed data, and LSTM with non-decomposition data. For the sEMG of Quadratipronator, the average classification accuracy is more than 90% using the proposed method. Compared with LSTM with non-decomposition data, SVM with decomposed data, and SVM with non-decomposition data, the accuracy of the proposed method is increased by 18.7%, 4.17%, and 11.53%, respectively. These results verify the efficacy of the proposed method.