Personalized Student Stress Prediction with Deep Multitask Network
This work addresses stress prediction for students using mobile sensor data, offering potential clinical applications, but it is incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of predicting students' stress levels from wearable sensor data by developing a personalized predictive model using auto-encoders and multitask learning, resulting in a 45.6% improvement in F1 score on the StudentLife dataset.
With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress suggests great clinical applications, yet such a task is extremely challenging. In this paper, we present a general platform for personalized predictive modeling of behavioural states like students' level of stress. Through the use of Auto-encoders and Multitask learning we extend the prediction of stress to both sequences of passive sensor data and high-level covariates. Our model outperforms the state-of-the-art in the prediction of stress level from mobile sensor data, obtaining a 45.6 % improvement in F1 score on the StudentLife dataset.