The validity and reasonableness of emotional data are the key issues in the cognitive affective computing research. Effects of the emotion recognition are decided by the quality of selected data directly. Therefore, it is an important part of affective computing research to build affective computing database with good performance, so that it is the hot spot of research in this field. In this paper, the performance of two classical cognitive affective computing databases, the Massachusetts Institute of Technology (MIT) cognitive affective computing database and Germany Augsburg University emotion recognition database were compared, their data structure and data types were compared respectively, and emotional recognition effect based on the data were studied comparatively. The results indicated that the analysis based on the physical parameters could get the effective emotional recognition, and would be a feasible method of pressure emotional evaluation. Because of the lack of stress emotional evaluation data based on the physiological parameters domestically, there is not a public stress emotional database. We hereby built a dataset for the stress evaluation towards the high stress group in colleges, candidates of postgraduates of Ph.D and master as the subjects. We then acquired their physiological parameters, and performed the pressure analysis based on this database. The results indicated that this dataset had a certain reference value for the stress evaluation, and we hope this research can provide a reference and support for emotion evaluation and analysis.
To solve the defect which is recognizing but not rating the stress, or rating but not considering the influence of the previous stress state to the current state of the existing affective stress evaluation method, this paper proposes an approach of affective stress rating model on electrocardiogram (ECG). An affective stress rating algorithm based on hidden Markov model (HMM) was established with the theory of affective computing. The individual's affective stress was rated using this affective rating model combining the investigation questionnaire. Features like complexity and approximate entropy of ECG were used in the model, and a matching process suggested that it improved the accuracy of affective stress rating. The result of the experiment illustrated that the model considering the environmental factors and the influence of previous stress state to the current state was an effective method in affective stress rating, and the accuracy of rating was improved by this affective stress rating method.