Abstract: Stress is the root cause of many diseases and unhealthy behaviors. Being able to monitor when and why a person is stressed could inform personal stress management as well as interventions when necessary. In this work, we present StressAware, an application on the Amulet wearable platform that classifies the stress level (low, medium, high) of individuals continuously and in real time using heart rate (HR) and heart-rate variability (HRV) data from a commercial heart-rate monitor. We developed our stress-detection model using a Support Vector Machine (SVM). We trained and tested our model using data from three sources and had the following preliminary results: PhysioNet, a public physiological database (94.5% accurate with 10-fold cross validation), a field study (100% accurate with 10-fold cross validation) and a lab study (64.3% accurate with leave-one-out cross-validation). Testing the StressAware app revealed a projected battery life of up to 12 days. Also, the usability feedback from subjects showed that the Amulet has a potential to be used by people for monitoring their stress levels. The results are promising, indicating that the app may be used for stress detection, and eventually for the development of stress-related intervention that could improve the health of individuals.
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