DataSentry: Building Missing Data Management System for In-the-Wild Mobile Sensor Data Collection through Multi-Year Iterative Design Approach

Mobile sensor data collection in people's daily lives is essential for understanding fine-grained human behaviors. However, in-the-wild data collection often results in missing data due to participant and system-related issues. While existing monitoring systems in the mobile sensing field provide an opportunity to detect missing data, they fall short in monitoring data across many participants and sensors and diagnosing the root causes of missing data, accounting for heterogeneous sensing characteristics of mobile sensor data. To address these limitations, we undertook a multi-year iterative design process to develop a system for monitoring missing data in mobile sensor data collection. Our final prototype, DataSentry, enables the detection, diagnosis, and addressing of missing data issues across many participants and sensors, considering both within-and between-person variability. Based on the iterative design process, we share our experiences, lessons learned, and design implications for developing advanced missing data management systems.

• Human-centered computing → Empirical studies in ubiquitous and mobile computing; Visualization systems and tools.