Recent advances in sensor based human activity recognition (HAR) have exploited deep
hybrid networks to improve the performance. These hybrid models combine Convolutional …

Recent years have witnessed significant success of convolutional neural networks (CNNs)
in human activity recognition (HAR) using wearable sensors. Nevertheless, prior works have …

Human activity recognition using multiple sensors is a challenging but promising task in
recent decades. In this paper, we propose a deep multimodal fusion model for activity …

Deep convolutional neural networks (CNNs) achieve state-of-the-art performance in
wearable human activity recognition (HAR), which has become a new research trend in …

Human activity recognition (HAR) commonly employs wearable sensors to identify and
analyze the time series data collected by them, enabling the recognition of specific actions …

Human activity recognition (HAR) applications have received much attention due to their
necessary implementations in various domains, including Industry 5.0 applications such as …

Although human activity recognition (HAR) based on WiFi channel state information (CSI)
has been widely studied, as the CSI signal is susceptible to the external environment, a …

Together with the rapid development of the Internet of Things, human activity recognition
(HAR) using wearable inertial measurement units (IMUs) becomes a promising technology …

Non-intrusive sensor based human activity recognition (HAR) is utilized in a spectrum of
applications including fitness tracking devices, gaming, health care monitoring, and …

Human activity recognition (HAR) from inertial sensor data plays a pivotal role in various
domains, such as healthcare, sports, and smart environments. In this paper, we present a …