Human Activity Recognition (HAR) is a field that infers human activities from raw time-series
signals acquired through embedded sensors of smartphones and wearable devices. It has …

Due to the ever-growing powers in sensing, computing, communicating and storing, mobile
devices (eg, smartphone, smartwatch, smart glasses) become ubiquitous and an …

Accurate physical activity monitoring is essential to understand the impact of physical activity
on one's physical health and overall well-being. However, advances in human activity …

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative
representations without labeled data, and has reached comparable or even state-of-the-art …

Human activity recognition (HAR) is a key enabling technology for a wide range of emerging
applications. Although multimodal sensing systems are essential for capturing complex and …

The emergence of self-supervised learning in the field of wearables-based human activity
recognition (HAR) has opened up opportunities to tackle the most pressing challenges in the …

A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need
for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is …

Abstract Objectives Large language models (LLMs) have demonstrated remarkable
generalization and across diverse tasks, leading individuals to increasingly use them as …

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

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 …