Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress

Abstract

Continuous wearable sensor data in high resolution contain physiological and behavioral information that can be utilized to predict human health and wellbeing, establishing the foundation for developing early warning systems to eventually improve human health and wellbeing. We propose a deep neural network framework, the Locally Connected Long Short-Term Memory Denoising AutoEncoder (LC-LSTM-DAE), to automatically extract features from passively collected raw sensor data and perform personalized prediction of self-reported mood, health, and stress scores with high precision. We enabled personalized learning of features by finetuning the general representation model with participant-specific data. The framework was evaluated using wearable sensor data and wellbeing labels collected from college students (total 6391 days from N=239). Sensor data include skin temperature, skin conductance, and acceleration; wellbeing labels include self-reported mood, health and stress scored 0 - 100. Compared to the prediction performance based on hand-crafted features, the proposed framework achieved higher precision with a smaller number of features. We also provide statistical interpretation and visual explanation to the automatically learned features and the prediction models. Our results show the possibility of predicting self-reported mood, health, and stress accurately using an interpretable deep learning framework, ultimately for developing real-time health and wellbeing monitoring and intervention systems that can benefit various populations.