Negative emotion recognition based on physiological signals using a CNN-LSTM model

Negative emotions can lead to a variety of physiological and psychological problems. Identifying and interpreting negative emotions can help people and specialists to deal with their effects on the human body. This paper introduces a deep learning-based method for recognizing negative emotions utilizing physiological signals. It is based on extracting a set of 123 features from raw signals and organizing them into 2D feature maps. A hybrid CNN-LSTM model is then employed to classify these maps, simultaneously learning and integrating integral and sequential features to output emotional feedback. Feature fusion is incorporated during training to improve the method’s accuracy and reduce the data complexity. The approach has been validated using WEMAC and WESAD datasets, achieving F1-scores of 85.15% and 91.44% and accuracies of 85.03% and 90.98%, respectively. Furthermore, the feasibility of this method for real-world applications is demonstrated through deployment on the Coral Edge TPU, an embedded device, indicating its potential for run-time decision-making on the computing edge.