Mobile and wearable devices have enabled numerous applications, including activity
tracking, wellness monitoring, and human–computer interaction, that measure and improve …

Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts
between training and testing phases without additional data acquisition or labeling cost; only …

Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous
measurement of activities of daily living, making them especially well-suited for health …

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area,
especially due to the spread of electronic devices such as smartphones, smartwatches and …

Advancements in smartphone sensor technologies have significantly enriched the field of
human activity recognition, facilitating a wide array of applications from health monitoring to …

Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to
unlock an entirely new class of smart applications. However, continued progress is limited …

Human activity recognition (HAR) plays a pivotal role in various domains, including
healthcare, sports, robotics, and security. With the growing popularity of wearable devices …

The ubiquity of smartphones and the growth of computing resources, such as connectivity,
processing, portability, and power of sensing, have greatly changed people's lives. Today …

Scientific advances build on reproducible researches which need publicly available
benchmark data sets. The computer vision and speech recognition communities have led …