Non-contact measurement of mental stress via heart rate variability

Non-contact detection of mental stress based on physiological parameters has many potential application areas, such as measuring stress in athletic contest. Non-contact detection could measure mental stress without drawing the attention of subjects. And compared with questionnaire survey, mental stress measurement based on physiological parameters is more objective. In this paper, we introduced a non-contact method to measure mental stress via heart rate variability (HRV). We conducted an experiment with 29 participants at rest and under stress. And a mental arithmetic test was employed to induce stress. To extract HRV, we recorded videos on subjects’ faces by a color CCD camera. HRV was extracted from these videos by imaging photoplethysmography (IPPG). The results showed that HRV was significantly different between normal and stressed conditions. Then we performed significance test and independence test to select the features which could be used in mental stress measurement. Finally, nine features were used to measure mental stress. In order to establish a stress measurement model, support vector machines (SVM) was used to establish a binary classifier for stress detection and the accuracy of the model was 78.2%. Compared with other methods, our method took non-linear features of HRV into consideration. The method we proposed supports the application of non-contact mental stress detection.