While deep learning models have contributed to the advancement of sensor-based human
activity recognition (HAR), it usually requires large amounts of annotated sensor data to …

Physiological and behavioral data collected from wearable or mobile sensors have been
used to estimate self-reported stress levels. Since stress annotation usually relies on self …

Self-supervised learning (SSL) has emerged as a promising alternative to purely supervised
learning, since it can learn from labeled and unlabeled data using a pre-train-then-fine-tune …

In smart cities, pervasive sensing and wearable computing techniques are increasingly
being employed to monitor and recognize human activities through body sensor networks …

Recently, with the advancement of the Internet of Things (IoT), WiFi CSI-based HAR has
gained increasing attention from academic and industry communities. By integrating the …

Deep learning models have achieved significant success in human activity recognition,
particularly in assisted living and telemonitoring. However, training these models requires …

Human Activity Recognition (HAR) systems have made significant progress in recognizing
and classifying human activities using sensor data from a variety of sensors. Nevertheless …

Recent concepts in defense herald an increasing degree of automation of future military
systems, with an emphasis on accelerating sensing-to-decision loops at the tactical edge …

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 …

Advancements in machine learning, coupled with Wi-Fi sensing using channel state
information (CSI), have emerged as a powerful approach for human activity and gesture …