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

要旨

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.

著者
Yugyeong Jung
KAIST, Daejeon, Korea, Republic of
Hei Yiu Law
Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of
Hadong Lee
Seoul National University, Seoul, Seoul, Korea, Republic of
Junmo Lee
KAIST, Daejeon, Korea, Republic of
Bongshin Lee
Yonsei University, Seoul, Korea, Republic of
Uichin Lee
KAIST, Daejeon, Korea, Republic of
DOI

10.1145/3706598.3713314

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713314

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Playing with Data

Annex Hall F206
7 件の発表
2025-04-30 20:10:00
2025-04-30 21:40:00
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