School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Sensors (Basel). 2020 Jul 17;20(14):3994. doi: 10.3390/s20143994.
Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.
表面肌电信号（sEMG）是一种生物电信号，记录肌肉活动强度的数据。大多数基于 sEMG 的手势识别都使用机器学习作为分类器，依赖于 sEMG 数据的特征提取。最近，基于深度学习的方法（如递归神经网络（RNN））为从原始数据中自动学习特征提供了一种选择。本文提出了一种新的手势预测方法，通过使用 RNN 模型从原始 sEMG 数据中学习并预测手势。使用 Myo 臂带记录了 13 名受试者的 21 个短期手部手势的 sEMG 信号，Myo 臂带是一种非侵入性、低成本、商用便携式设备。在手势开始时，经过训练的模型会对手部 sEMG 数据进行即时预测。实验结果表明，已知的数据时间步越多，所提出模型给出的即时预测精度越高。当使用 40 个时间步（200ms）的数据来预测手部手势时，预测精度达到了约 89.6%。这意味着可以在手部开始执行手势后延迟 200ms 进行预测，而无需等到手势结束。
