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We propose an accurate chronic stress estimation system that utilizes personalized models based on correlation maximization between physiological features and ground truth, which helps determine physiological features effective for the estimation. The personalized models are trained using features respectively found for each individual classes among which the relationships between features and ground truth differ. Which class a new user belongs to can be estimated from the results of a personality questionnaire, as well as by means of conventional methods. W.r.t. evaluation data, with the cooperation of 168 subjects, 599 sets of 1-month wearable-sensor data and ground-truth Perceived Stress Scale (PSS) data were collected, along with the Big Five Personality Traits for each subject. In chronic stress estimation evaluations using this above data, we have confirmed that the proposed classification system achieved 69.1&#x0025; estimation accuracy in terms of increase/decrease in PSS, as compared to 59.3&#x0025; and 56.8&#x0025; achieved, respectively, with two conventional methods, one employing no classification and the other employing <tex>${\boldsymbol{k}}$</tex> -means clustering.

Accurate Chronic Stress Estimation with Personalized Models Based on Correlation Maximization