CASP: context-aware stress prediction system

Abstract

In this paper, we propose a mobile-based context-aware acute stress prediction system (CASP) that predicts a user’s stress status based on their current contextual data. The system consists of a context-aware stress prediction algorithm, and an early stage stress intervention method. In the learning phase, the context-aware stress detection algorithm uses ECG signals to identify the user’s stress status. With the aid of machine learning algorithms and cloud computing services, the stress prediction algorithm produces adaptive and personalized prediction models based on the user’s context gathered from their smartphone. The prediction models are able to adapt the changing nature of both the user’s stress status and the surrounding environment. Our evaluation results show that the CASP system is able to predict the stress status of a user using the current contextual data with an average accuracy of 78.3% as measured from ground truth data collected using biofeedback sensors.