Emo-LSTM: An Emotion Recognition Model of ECG Data Based on Long Short-Term Memory

In order to accurately monitor human emotions during work, we develop a deep learning model based on electrocardiogram (ECG) data. First, we set up an ECG data acquisition system and generate standard data set. Second, a model combining convolutional neural network (CNN), Long Short-Term Memory (LSTM) and attention mechanism is used for training and testing. Finally, the recognition results are evaluated using evaluation metrics such as Precision, Recall and F1 Score. Experimental results show that the Precision of the model to identify abnormal emotional states is 85.48%, the Recall is 92.16%, and the F1 score is 88.68%. The development of this technology provides a new tool for emotional monitoring in workplace, which promises to improve productivity and safety through timely interventions.