We see a growing interest in the Human-Computer Interaction community in learning about the tracking needs of people with specific needs and how they use existing tracking apps. The mismatch between the design of existing tracking tools and people’s tracking needs sheds a light on the benefits of designing customizable tracking tools. In this talk, I will present a flexible tracking approach for designing self-monitoring technology called "semi-automated tracking," which combines both manual and automated data collection methods. I will discuss promising avenues in personal informatics and personal health contexts where leveraging semi-automated tracking approach can help individuals—self-trackers, patients, and clinicians—achieve their diverse and unique tracking needs, preferences, and commitments.
Eun Kyoung Choe is an Assistant Professor in the College of Information Studies at the University of Maryland, College Park. Her primary research areas are in the fields of Human-Computer Interaction and Health Informatics. She examines the design and evaluation of personal informatics tools to empower individuals to make positive behavior changes through fully leveraging their personal data. She explores this topic in various contexts, including sleep and productivity, patient-clinician communication and data sharing, and personal data insights and visualization. She is a recipient of the NSF CRII and NSF CAREER awards, and the Google Anita Borg Memorial Scholarship. She received her PhD in Information Science from University of Washington, MS in Information Management and Systems from University of California, Berkeley, and BS in Industrial Design from KAIST, Korea. http://eunkyoungchoe.com