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 …

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 …

Deep Learning (DL) models have been widely deployed on IoT devices with the help of
advancements in DL algorithms and chips. However, the limited resources of edge devices …

Motion sensors embedded in wearable and mobile devices allow for dynamic selection of
sensor streams and sampling rates, enabling several applications, such as power …

The increasing availability of low-cost wearable devices and smartphones has significantly
advanced the field of sensor-based human activity recognition (HAR), attracting …

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

Deep clustering utilizes representation learning to learn features in an unsupervised setting.
Although successful, the current models rely on the assumption of the centralized dataset …

In recent years, emerging research on mobile sensing has led to novel scenarios that
enhance daily life for humans, but dynamic usage conditions often result in performance …

Recognizing food types through sensor signals for unseen users remains remarkably
challenging, despite extensive recent studies. The efficacy of prior machine learning …

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 …