As smartphone penetration saturates, we are witnessing a new trend in personal mobile
devices-wearable mobile devices or simply wearables as it is often called. Wearables come …

Breakthroughs from the field of deep learning are radically changing how sensor data are
interpreted to extract the high-level information needed by mobile apps. It is critical that the …

Microphones are remarkably powerful sensors of human behavior and context. However,
audio sensing is highly susceptible to wild fluctuations in accuracy when used in diverse …

Sensor-equipped smartphones and wearables are transforming a variety of mobile apps
ranging from health monitoring to digital assistants. However, reliably inferring user behavior …

This paper proposes Mandheling, the first system that enables highly resource-efficient on-
device training by orchestrating mixed-precision training with on-chip Digital Signal …

On-device large language models (LLMs) are catalyzing novel mobile applications such as
UI task automation and personalized email auto-reply, without giving away users' private …

Despite the enormous interest in emotion classification from speech, the impact of noise on
emotion classification is not well understood. This is important because, due to the …

The lack of digital floor plans is a huge obstacle to pervasive indoor location based services
(LBS). Recent floor plan construction work crowdsources mobile sensing data from …

The use of smartphones and wearables as sensing devices has created innumerable
context inference apps including a class of workout tracking apps. Workout data generated …

Continual learning approaches help deep neural network models adapt and learn
incrementally by trying to solve catastrophic forgetting. However, whether these existing …