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 …

Making a body-worn device wearable is a deceptively difficult challenge: it is not enough to
make a functional device and simply put it on the body; it must also work with the wearer to …

Sign Language is widely used by over 500 million Deaf and hard of hearing (DHH)
individuals in their daily lives. While prior works made notable efforts to show the feasibility …

The accurate assessment of human body posture through wearable technology has
significant implications for sports science, clinical diagnostics, rehabilitation, and VR …

In the field of emotion recognition, traditional methods often rely on motion capture
technologies to recognize human emotions by analyzing body motion. However, these …

Acoustic-based human gesture recognition (HGR) offers diverse applications due to the
ubiquity of sensors and touch-free interaction. However, existing machine learning …

Transportation mode detection (TMD) for wheelchair users is essential for applications that
facilitate enhancing accessibility and quality of life. Yet, the lack of extensive datasets from …

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 …

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 …

Deep-learning-based IMU (Inertial measurement unit)-based Human Activity Recognition
(HAR) has a problem of lack of large datasets. This problem would be solved if integrated …