A Probability-Based Prediction Framework for Stress Identification

In this paper, we propose a probability-based prediction framework for stress identification. The framework predicts the stress index of someone using vital signals and symptoms. The framework largely consists of learning stage and prediction stage. In learning stage, once vital signals and symptoms of stress group and normal group are available individually, a feature space is constructed. Vital signals and symptoms gathered from pre-identified subjects form the feature space. The framework constructs a matrix for each group which holding appearance frequencies of vital signals and symptom pair that is found in the feature space. Another matrix, SCAP matrix, is constructed using two matrices. It contains probabilities that indicate a certain vital signals-symptom pair may be found in stress group and the framework prepares weight vectors in order to direct the use of vital signals and symptoms in personalized manner. In prediction stage, a probability equation is devised based on SCAP matrix and the weight vectors. Once the vital signal and symptom pair of someone is available, the probability that indicates a person may belong to a stress group is computed using the probability equation. In this paper, we conduct a simple validation test for this framework using vital signal and symptom data obtained from 29 subjects. The method is revealed to have high potential to improve probability of stress identification further after more learning data is accumulated.