Users' ability to accurately recall frequent, habitual activities is fundamental to a number of
disciplines, from health sciences to machine learning. However, few, if any, studies exist that …

Besides passive sensing, ecological momentary assessments (EMAs) are one of the primary
methods to collect in-the-moment data in ubiquitous computing and mobile health. While …

Previous work suggests that Quantified-Self applications can retain long-term usage with
motivational methods. These methods often require intermittent attention requests with …

In situ self-report is widely used in human-computer interaction, ubiquitous computing, and
for assessment and intervention in health and wellness. Unfortunately, it remains limited by …

Sedentary behavior such as sitting is associated with severe health issues. We suggest that
the sedentary behavior problem can be considered as a prospective memory task …

We report on ongoing research into how to statistically represent the experiences of a
wearable computer user for the purposes of day-to-day behavior prediction. We combine …

Manual tracking of health behaviors affords many benefits, including increased awareness
and engagement. However, the capture burden makes long-term manual tracking …

We consider a novel problem called Activity Prediction, where the goal is to predict the future
activity occurrence times from sensor data. In this paper, we make three main contributions …

We introduce a mobile app for collecting in-the-wild data, including sensor measurements
and self-reported labels describing people's behavioral context (eg, driving, eating, in class …

An in-depth understanding of human activity is essential for building personalized systems
like Google Now to support users in everyday life. For that understanding, a comprehensive …