{'fraction_non_alphanumeric': 0.03140495867768595, 'fraction_numerical': 0.0049586776859504135, 'mean_word_length': 5.407407407407407, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Research on sEMG–based gesture recognition using the Attention-based LSTM-CNN with Stationary Wavelet Packet Transform

Surface electromyography signal (sEMG) has the characteristics of flexible signal acquisition and rapid response as a control source for gesture recognition so it is widely used in the fields of human-computer interaction and prosthesis control. For the gesture recognition method, this study proposes a method to decompose the time-frequency domain information of the signal using the stationary wavelet packet transform (SWPT). This method improves the processing speed and recognition accuracy compared with previous methods, such as the continuous wavelet transform (CWT), wavelet packet transform (WPT), etc. The time consumption is only 13% of CWT and 70% of WPT. Then, a novel model consisting of the convolutional neural network (CNN), the long and short-term memory network (LSTM), and the hybrid attention mechanism is built to improve the recognition accuracy. Finally, a test is conducted to evaluate the effectiveness of this model in the public dataset Ninapro’s DB1, and it achieved 6% improvement, compared to the previous related studies in the equal amount of calculation.