Toward End-to-end Prediction of Future Wellbeing using Deep Sensor Representation Learning

Wearable sensors can capture continuous, high resolution physiological and behavioral data that can be utilized to develop early health and wellbeing detection and lead to early warning, intervention, and recommendation systems to improve health and wellbeing. We have built and evaluated an end-to-end wellbeing prediction framework that pipelines raw wearable sensor data into an unsupervised autoencoder-based representation learning model and a supervised wellbeing regression model. We trained and evaluated the framework using the wearable sensor dataset and wellbeing labels collected from college students (total 6391 days from N=252). Wearable data include skin temperature, skin conductance, and acceleration; the wellbeing labels include self-reported alertness, happiness, energy, health, and calmness scored 0 - 100. We compared the performance of our framework with the performance of wellbeing regression models based on hand-crafted features. Our results showed that the proposed framework can automatically extract features from the current day's 24-hour multi-channel data and predict wellbeing scores for next day with mean absolute errors of 14-16. This result shows the possibility of predicting wellbeing accurately using an end-to-end framework, ultimately for developing real-time health and wellbeing monitoring and intervention systems.