Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:5940-5943. doi: 10.1109/EMBC46164.2021.9629566.
The success of pattern recognition based upper-limb prostheses control is linked to their ability to extract appropriate features from the electromyogram (EMG) signals. Traditional EMG feature extraction (FE) algorithms fail to extract spatial and inter-temporal information from the raw data, as they consider the EMG channels individually across a set of sliding windows with some degree of overlapping. To tackle these limitations, this paper presents a method that considers the spatial information of multi-channel EMG signals by utilising dynamic time warping (DTW). To satisfy temporal considerations, inspired by Long Short-Term Memory (LSTM) neural networks, our algorithm evolves the DTW feature representation across long and short-term components to capture the temporal dynamics of the EMG signal. As such the contribution of this paper is the development of a recursive spatio-temporal FE method, denoted as Recursive Temporal Warping (RTW). To investigate the performance of the proposed method, an offline EMG pattern recognition study with 53 movement classes performed by 10 subjects wearing 8 to 16 EMG channels was considered with the results compared against several conventional as well as deep learning-based models. We show that the use of the RTW can reduce classification errors significantly, paving the way for future real-time implementation.
基于模式识别的上肢假肢控制的成功与否与其从肌电图 (EMG) 信号中提取适当特征的能力密切相关。传统的 EMG 特征提取 (FE) 算法无法从原始数据中提取空间和时变信息，因为它们在一组具有一定重叠度的滑动窗口中分别考虑 EMG 通道。为了解决这些限制，本文提出了一种利用动态时间规整 (DTW) 来考虑多通道 EMG 信号空间信息的方法。为了满足时间方面的考虑，受长短时记忆 (LSTM) 神经网络的启发，我们的算法通过跨越长期和短期分量来演变 DTW 特征表示，以捕获 EMG 信号的时间动态。因此，本文的贡献在于开发了一种递归时空 FE 方法，称为递归时间规整 (RTW)。为了研究所提出方法的性能，考虑了一项由 10 名佩戴 8 到 16 个 EMG 通道的受试者进行的 53 个运动类别的离线 EMG 模式识别研究，并将结果与几种传统的和基于深度学习的模型进行了比较。我们表明，使用 RTW 可以显著降低分类错误率，为未来的实时实现铺平了道路。
