Opportunity++ is a precisely annotated dataset designed to support AI and machine learning
research focused on the multimodal perception and learning of human activities (eg, short …

The Activity Recognition Chain generally precludes the challenging scenario of recognizing
new activities that were unseen during training, despite this scenario being a practical and …

Human Activity Recognition (HAR) based on embedded sensor data has become a popular
research topic in ubiquitous computing, which has a wide range of practical applications in …

Traditional federated classification methods, even those designed for non-IID clients,
assume that each client annotates its local data with respect to the same universal class set …

Deep learning concepts have been successfully transferred from the computer vision task to
that of wearable human activity recognition (HAR) over the last few years. However, deep …

Sensor-based human activity recognition (HAR) identifies human activities using readings
from wearable devices. HAR has a variety of applications including healthcare, motion …

A species' genetic code or genome encodes valuable evolutionary, biological, and
phylogenetic information that aids in species recognition, taxonomic classification, and …

Current human activity recognition (HAR) techniques regard activity labels as integer class
IDs without explicitly modeling the semantics of class labels. We observe that different …

Sensor-based human activity recognition aims to recognize the activities performed by
people with the sensor readings. Most of existing works in this area rely on supervised …

Deep learning models are increasingly deployed on edge Internet of Things (IoT) devices.
However, these models typically operate under supervised conditions and fail to recognize …