Personalized User Modelling for Context-Aware Lifestyle Recommendations to Improve Sleep

Abstract

Sleep is a significant contributor to leading a healthy lifestyle. Each day, most people go to sleep without any idea about how their night’s rest will be or how they can leverage their data to improve it. For an activity that humans spend near a third of their life doing, there is a surprising amount of mystery around it. Despite current research, creating personalized sleep models in real-world settings has been challenging. Existing literature provides several connections between daily activities and sleep quality. Unfortunately, these insights do not generalize well in many individuals. For these reasons, it is essential to create a data-driven personalized sleep model. This research proposes a sleep model that captures causal relationships between daily activities and sleep quality and presents the user with specific feedback recommendations to improve sleep quality. Using N-of-1 experiments on longitudinal user data and event mining, the model generates a probabilistic understanding between lifestyle choices (exercise, eating, circadian rhythm, environmental selection) and their respective impact on sleep quality. Our experimental results identified and quantified relationships while extracting confounding variables through a causal framework. We then utilize the generated model to provide lifestyle recommendations to optimize sleep outcomes in a context-aware health recommendation system.

Vaibhav Pandey

Ph.D. Candidate

My research interests include temporal data mining, causal inference, health informatics, and machine learning.