Ecological Momentary Assessment (EMA) is a data collection method that consists of asking individuals to answer questions pertaining to their behavior, feelings, and experiences in everyday life. While EMA provides benefits compared to retrospective self-reports, the frequency of prompts throughout the day can be burdensome. Leveraging advances in speech recognition and the popularity of conversational assistants, we study the usability of an EMA interface specifically aimed at minimizing the interruption burden caused by EMA… Read more

A difficulty in human activity recognition (HAR) with wearable sensors is the acquisition of large amounts of annotated data for training models using supervised learning approaches. While collecting raw sensor data has been made easier with advances in mobile sensing and computing, the process of data annotation remains a time-consuming and onerous process. This paper explores active learning as a way to minimize the labor-intensive task of labeling data… Read more