Classification of emotional stress and physical stress using a multispectral based deep feature extraction model.

A classification model (Stress Classification-Net) of emotional stress and physical stress is proposed, which can extract classification features based on multispectral and tissue blood oxygen saturation (StO) characteristics. Related features are extracted on this basis, and the learning model with frequency domain and signal amplification is proposed for the first time. Given that multispectral imaging signals are time series data, time series StO is extracted from spectral signals. The proper region of interest (ROI) is obtained by a composite criterion, and the ROI source is determined by the universality and robustness of the signal. The frequency-domain signals of ROI are further obtained by wavelet transform. To fully utilize the frequency-domain characteristics, the multi-neighbor vector of locally aggregated descriptors (MN-VLAD) model is proposed to extract useful features. The acquired time series features are finally put into the long short-term memory (LSTM) model to learn the classification characteristics. Through SC-NET model, the classification signals of emotional stress and physical stress are successfully obtained. Experiments show that the classification result is encouraging, and the accuracy of the proposed algorithm is over 90%.