Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28 for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.
@cite_19: We have developed a system, allowing real-time monitoring of human gestures, which can be used for the automatic recognition of behavioural correlates of psychological stress. The system is based on a low-cost camera (Microsoft Kinect), which provides video recordings capturing the subject’s upper body activity. Motion History Images (MHIs) are calculated in real-time from these recordings. Appropriate algorithms are thereafter applied over the MHIs, enabling the real-time calculation of activity-related behavioural parameters. The system’s efficiency in real-time calculation of behavioural parameters has been tested in a pilot trial, involving monitoring of behavioural parameters during the induction of mental stress. Results showed that our prototype is capable to effectively calculate simultaneously eight different behavioural parameters in real-time. Statistical analysis indicated significant correlations between five of these parameters and self-reported stress. The preliminary findings suggest that our approach could potentially prove useful within systems targeting automatic stress detection, through unobtrusive monitoring of subjects.
@cite_54: It is argued that reliable acoustic profiles of speech under stress can only be found if different types of stress are clearly distinguished and experimentally induced. We report first results of a study with 100 speakers from three language groups, using a computer-based induction procedure that allows distinguishing cognitive load due to task engagement from psychological stress. Findings show significant effects of load, and partly of stress, for speech rate, energy contour, F0, and spectral parameters. It is further suggested that the mean results for the complete sample of speakers do not reflect the amplitude of stress effects on the voice. Future research should isolate and focus on speakers for whom the psychological stress induction has been successful.
However, these methods depend on sound quality, which is not granted in natural settings (e.g., crowded public places, noisy outdoor), and the correlation between speech and emotion is subjected to large individual differences @cite_54 . Hence, our performance of 72.28 to stress detection. Other studies focused on the video analysis of behavioural correlates of psychological stress @cite_19 . These systems, despite providing an unobtrusive method for stress monitoring, cannot be employed in a large variety of real world and mobile environments and pose privacy concerns related to the recording of people's behaviour.