Deep learning framework for subject-independent emotion detection using wireless signals
Fig 2
Proposed deep neural network architecture for emotion classification.
Time domain RF signal is processed through two convolutional-1D layers and an additional LSTM cell that captures the time dependency (section 1 in S1 File). The CW transformation is processed by two convolutional-2D layers (section 2.1 in S1 File). Each feature map in convolutional layers represents a unique extracted feature from the layer input. The features extracted from two distinct inputs of the model are then concatenated, leading to a broad learning capability. The detailed visualization of 32 and 64 features maps is presented in section 2.2 of S1 File.