Lin Yuzhou, Palaniappan Ramaswamy, De Wilde Philippe, Li Ling
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:649-652. doi: 10.1109/EMBC44109.2020.9175156.
Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gestures many times in real life. This problem can be alleviated to a certain extent if sEMG from many other subjects could be used to train the classifier. In this paper, we propose a normalisation approach that allows implementing real-time subject-independent sEMG based hand gesture classification without training the deep learning algorithm subject specifically. We hypothesed that the amplitude ranges of sEMG across channels between forearm muscle contractions for a hand gesture recorded in the same condition do not vary significantly within each individual. Therefore, the min-max normalisation is applied to source domain data but the new maximum and minimum values of each channel used to restrict the amplitude range are calculated from a trial cycle of a new user (target domain) and assigned by the class label. A convolutional neural network (ConvNet) trained with the normalised data achieved an average 87.03% accuracy on our G. dataset (12 gestures) and 94.53% on M. dataset (7 gestures) by using the leave-one-subject-out cross-validation.
最近，基于深度学习算法的特定主题表面肌电图（sEMG）手势分类受到了广泛研究。然而，要求用户在现实生活中多次执行手势来获取训练数据是不切实际的。如果可以使用许多其他受试者的sEMG来训练分类器，这个问题可以在一定程度上得到缓解。在本文中，我们提出了一种归一化方法，该方法允许在不针对特定受试者训练深度学习算法的情况下，实现基于sEMG的实时独立于受试者的手势分类。我们假设，在相同条件下记录的手势的前臂肌肉收缩期间，各通道间sEMG的幅度范围在每个个体内不会有显著变化。因此，对源域数据应用最小-最大归一化，但用于限制幅度范围的每个通道的新最大值和最小值是根据新用户（目标域）的一个试验周期计算得出，并由类别标签指定。通过留一受试者交叉验证，使用归一化数据训练的卷积神经网络（ConvNet）在我们的G.数据集（12种手势）上平均准确率达到87.03%，在M.数据集（7种手势）上达到94.53%。
