Recent years have seen significant advances in the sensing capabilities of smartphones,
enabling them to collect rich contextual information such as location, device usage, and …

The field of mobile and wearable computing is undergoing a revolutionary integration of
machine learning. Devices can now diagnose diseases, predict heart irregularities, and …

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have
been one of the key technologies that researchers and engineers have focused on, aiming …

Mood inference with mobile sensing data has been studied in ubicomp literature over the
last decade. This inference enables context-aware and personalized user experiences in …

Excessive alcohol consumption causes disability and death. Digital interventions are
promising means to promote behavioral change and thus prevent alcohol-related harm …

Smartphones enable understanding human behavior with activity recognition to support
people's daily lives. Prior studies focused on using inertial sensors to detect simple activities …

Machine learning models trained with passive sensor data from mobile devices can be used
to perform various inferences pertaining to activity recognition, context awareness, and …

Socially assistive robots can support people in making behavior changes by socially
engaging in or moderating certain behaviors, such as physical exercise and snacking …

While the task of automatically detecting eating events has been examined in prior work
using various wearable devices, the use of smartphones as standalone devices to infer …

The interplay between mood and eating has been the subject of extensive research within
the fields of nutrition and behavioral science, indicating a strong connection between the …