Air quality is important, varies across time and space, and is largely invisible. Pioneering past work deploying air quality monitors in residential environments found that study participants improved their awareness of and engagement with air quality. However, these systems fielded a single monitor and did not support user-specified annotations, inhibiting their utility. We developed MAAV-a system to Measure Air quality, Annotate data streams, and Visualize real-time PM 2.5 levelsto explore how participants engage with an air quality system addressing these challenges. MAAV supports collecting data from multiple air quality monitors, annotating that data through multiple modalities, and sending text message prompts when it detects a PM 2.5 spike. MAAV also features an interactive tablet interface for displaying measurement data and annotations. Through six long-term field deployments (20-47 weeks, mean 37.7 weeks), participants found these system features important for understanding the air quality in and around their homes. Participants gained new insights from between-monitor comparisons, reflected on past PM 2.5 spikes with the help of their annotations, and adapted their system usage as they familiarized themselves with their air quality data and MAAV. These results yield important insights for designing residential sensing systems that integrate into users' everyday lives.

Whether investigating research questions or designing systems, many researchers and designers need to engage users with their personal data. However, it is difficult to successfully design user-facing tools for interacting with personal data without first understanding what users want to do with their data. Techniques for raw data exploration, sketching, or physicalization can avoid the perils of tool development, but prevent direct analytical access to users' rich personal data. We present a new method that directly tackles this challenge: the data engagement interview. This interview method incorporates an analyst to provide real-time personal data analysis, granting interview participants the opportunity to directly engage with their data, and interviewers to observe and ask questions throughout this engagement. We describe the method's development through a case study with asthmatic participants, share insights and guidance from our experience, and report a broad set of insights from these interviews.

We design and build a system called EpiFi, which allows epidemiologists to easily design and deploy experiments in homes. The focus of EpiFi is reducing the barrier to entry for deploying and using an in-home sensor network. We present a novel architecture for in-home sensor networks con gured using a single con guration le and provide: a fast and reliable method for device discovery when installed in the home, a new mechanism for sensors to authenticate over the air using a subject's home WiFi router, and data reliability mechanisms to minimize loss in the network through a long-term deployment. We work collaboratively with pediatric asthma researchers to design three studies and deploy EpiFi in homes.

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