Motion time series collected from low-power, always-on mobile and wearable devices such
as smartphones and smartwatches offer significant insights into human behavioral patterns …

Trajectory data is a valuable asset for service management and spatio-temporal mining in
transportation and logistics systems. However, due to equipment failure, network delay, and …

This paper introduces PhyMask, an adaptive masking paradigm designed to enhance the
efficiency and interpretability of Masked Autoencoders (MAEs) in analyzing IoT sensing …

Wearable devices are increasingly used as tools for biomedical research, as the continuous
stream of behavioral and physiological data they collect can provide insights about our …

Wearable human activity recognition faces challenges in cross-dataset generalization due to
variations in device configurations and activity types across datasets. We present GOAT, a …

In the many years since the inception of wearable sensor-based Human Activity Recognition
(HAR), a wide variety of methods have been introduced and evaluated for their ability to …

Deep learning (DL)-based Human Activity Recognition (HAR) using wearable inertial
measurement unit (IMU) sensors can revolutionize continuous health monitoring and early …

An address, a textual description of a geographical location, plays an important role in
location-based services such as instant delivery. However, abnormal addresses (ie, an …

This study explores Human Activity Recognition (HAR) using smartphone sensors to
address the challenges posed by position-dependent datasets. We propose a position …

This paper introduces a novel mobile sensing application-life journaling-designed to
generate semantic descriptions of users' daily lives. We present AutoLife, an automatic life …