Myoelectric gesture recognition method based on RNN-CNN architecture
A technology of gesture recognition and myoelectricity, which is applied in the field of physiological signal recognition, can solve problems such as improvement, long model training time, and affecting model accuracy
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[0028] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings.
[0029]An EMG gesture recognition method based on RNN-CNN architecture, the gesture recognition method is based on the time series characteristics of EMG signals. The fused feature map is further extracted, which mainly includes the following steps:
[0030] Step 1: Data preprocessing.
[0031] like figure 1 As shown in the figure, after the EMG signal is acquired from the EMG acquisition device, the signal cannot generally be used directly, and several steps need to be taken for signal processing. Assume that the sampling frequency of the EMG device is FHz, the sampling duration of each gesture is Tms, the number of channels of the device is C, and the data format of the EMG signal is one-dimensional format, and the unit is voltage unit. Data preprocessing includes four steps of noise reduction, signal synchronization, relabeling ...
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