Radar-based heart rate analysis method and equipment
An analysis method and heart rate technology, applied in the computer field, can solve problems such as interference, insufficient sensitivity, and strong interference, and achieve the effects of improving equipment practicability, simplifying circuit design, and strong anti-interference
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[0050] The present invention provides the following embodiments, including:
[0051] The design of the fully convolutional neural network uses the deep learning framework tensorflow, the code is as follows:
[0052] input_layer = keras.layers.Input(input_shape)
[0053] / / convolutional layer
[0054] conv1=keras.layers.Conv1D(filters=128, kernel_size=8, padding='same')(input_layer)
[0055] / / batch normalization layer
[0056] conv1 = keras.layers.BatchNormalization()(conv1)
[0057] / / non-linear activation layer
[0058] conv1 = keras.layers.Activation(activation='relu')(conv1)
[0059] conv2=keras.layers.Conv1D(filters=256, kernel_size=5, padding='same')(conv1)
[0060] conv2 = keras.layers.BatchNormalization()(conv2)
[0061] conv2 = keras.layers.Activation('relu')(conv2)
[0062] conv3=keras.layers.Conv1D(128, kernel_size=3, padding='same')(conv2)
[0063] conv3 = keras.layers.BatchNormalization()(conv3)
[0064] conv3 = keras.layers.Activation('relu')(conv3)
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