Large Language Models (LLMs) have seen significant use in domains such as natural
language processing and computer vision. Going beyond text, image and graphics, LLMs …

Sensor-based human activity recognition (HAR) has been an active research area, owing to
its applications in smart environments, assisted living, fitness, healthcare, etc. Recently …

One of the primary challenges in the field of human activity recognition (HAR) is the lack of
large labeled datasets. This hinders the development of robust and generalizable models …

In human activity recognition (HAR), the limited availability of annotated data presents a
significant challenge. Drawing inspiration from the latest advancements in generative AI …

Context-aware Human Activity Recognition (HAR) is a hot research area in mobile
computing, and the most effective solutions in the literature are based on supervised deep …

The combination of increased life expectancy and falling birth rates is resulting in an aging
population. Wearable Sensor-based Human Activity Recognition (WSHAR) emerges as a …

Due to the scarcity of labeled sensor data in HAR, prior research has turned to video data to
synthesize Inertial Measurement Units (IMU) data, capitalizing on its rich activity annotations …

In this paper, we are using comprehensively review the ways in which Large Language
Models (LLMs) advance activity recognition systems, discuss the challenges of …

In wearable human activity recognition (HAR), the generation and utilization of virtual IMU
data has recently gained attention. The use of virtual data can improve the robustness …

Over the last decade, smart mobile devices have become ubiquitous, bringing about
significant lifestyle changes worldwide. Mobile sensing, which involves obtaining and …