Modeling Work Stress Using Heart Rate and Stress Coping Profiles

Automated mental health analysis of stress could lead towards diagnosis tools that can be used in environments such as clinics, schools and corporations. However, attempts at building general models are often limited by the subjectivity of physiological stress responses. This work aims to discover the effects of combining data from physiological signals and psychological context from work activities when building a machine-learned model of mental stress. A software application was built to guide subjects through a monitoring process which allowed pre and post-assessment of psychological context through various stress-related annotation modules including the Cohen Stress Scale and the COPE inventory. Meanwhile, wearable sensors tracked physiological data in the form of heart beats. Tests were performed on this data by building supervised and unsupervised machine-learned models. Results show a general increase in classification performance when psychological context data is integrated into the models. Furthermore, models present similar performance using either questionnaire answers or coping profile scores.

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