Dynamic Gesture Recognition Based on LSTM-CNN

Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

“…Meanwhile, we analyze the effect of adding CNN before the LSTM. We propose LCNN and CNN-LSTM models, which can directly input pre-processed EMG signals into the network [7]. In practical work, we verified that the performance of the LCNN model is better than CNN-LSTM.…”

“…However, it is difficult to improve the performance of gesture recognition based on sEMG by traditional methods. Nevertheless, the process of designing 2 of 15 and selecting features can be complicated and the combinations of features are diverse, leading to increasing of workload and dissatisfied results [7].…”

“…Using deep neural networks to distinguish sEMG signals has been proposed by researchers. Wu et al [7] proposed LCNN and CNN_LSTM models (These models can be thought of as autoencoders for automatic feature extraction. ), which do not require the process of traditional feature extraction.…”

“…Researchers such as Côté-Allard et al [8], who regarded the original sEMG signals as an image, constructed the ConvNet model to further improve the classification accuracy of sEMG signals. However, the LCNN and CNN_LSTM models proposed by Wu et al [7], and the ConvNet model used by Côté-Allard et al [8], contain a large number of parameters.…”

“…Literature [8] used the ConvNets to classify the characterizations of the sEMG signals extracted by short-time Fourier transform-based spectrogram and Continuous Wavelet Transform (CWT). Since sEMG signals correspond to the timing signal, we proposed to classify the sEMG signal by combining Long Short-Term Memory (LSTM) and CNN from our previous work [7]. The temporal information in the signal is retained and the ability of CNN to extract features is utilized.…”