Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches
This work addresses the challenge of practical context recognition for applications like health monitoring and aging care, though it is incremental as it builds on existing methods with a new dataset.
The study tackled the problem of recognizing detailed human behavioral contexts in real-world settings using sensor data from personal smartphones and smartwatches, collecting over 300k minutes of labeled data from 60 subjects to validate context recognition in unscripted environments.
The ability to automatically recognize a person's behavioral context can contribute to health monitoring, aging care and many other domains. Validating context recognition in-the-wild is crucial to promote practical applications that work in real-life settings. We collected over 300k minutes of sensor data with context labels from 60 subjects. Unlike previous studies, our subjects used their own personal phone, in any way that was convenient to them, and engaged in their routine in their natural environments. Unscripted behavior and unconstrained phone usage resulted in situations that are harder to recognize. We demonstrate how fusion of multi-modal sensors is important for resolving such cases. We present a baseline system, and encourage researchers to use our public dataset to compare methods and improve context recognition in-the-wild.