Over the years, multimodal mobile sensing has been used extensively for inferences
regarding health and well-being, behavior, and context. However, a significant challenge …

Understanding how social situations unfold in people's daily lives is relevant to designing
mobile systems that can support users in their personal goals, well-being, and activities. As …

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 …

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 …

Mental fatigue is a crucial aspect that has gained attention across various disciplines due to
its impact on overall well-being. While previous research has explored the use of wearable …

Wearable devices are evolving rapidly, with earables' popularity being on the rise as they
gain new and sophisticated sensing capabilities. Their growing complexity, however, also …

Energy expenditure (EE) estimation using physiological signs is becoming more important
as wearable devices proliferate. To estimating EE from wearable data, this research …

A range of behavioral and contextual factors, including eating and drinking behavior, mood,
social context, and other daily activities, can significantly impact an individual's quality of life …