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.

Current methods for continuous respiration monitoring such as respiratory inductive or optoelectronic plethysmography are limited to clinical or research settings; most wearable systems reported only measures respiration rate. Here we introduce a wearable sensor capable of simultaneously measuring both respiration rate and volume with high fidelity. Our disposable respiration sensor with a Band-Aid© like formfactor can measure both respiration rate and volume by simply measuring the local strain of the ribcage and abdomen during breathing. We demonstrate that both metrics are highly correlated to measurements from a medical grade continuous spirometer on participants at rest. Additionally, we also show that the system is capable of detecting respiration under various ambulatory conditions. Because these low-powered piezo-resistive sensors can be integrated with wireless Bluetooth units, they can be useful in monitoring patients with chronic respiratory diseases in everyday settings.

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geo-spatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework.