A new computational model of the brain based closely on its biology and physiology has not only learned a simple visual ...

This project implements a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for human activity recognition using sensor data from ...

Intermittent energy restriction for weight loss leads to coordinated changes across the brain, gut, and microbiome axis.

In a lab rack that looks more like a high-end audio system than a server, clusters of human brain cells are quietly learning ...

Abstract: Activity recognition is the task of identifying human actions and physical activities. It can have applications in broad areas including medical, sports and fitness, healthcare, behaviour ...

Artificial intelligence systems designed to physically imitate natural brains can simulate human brain activity before being trained, according to new research from Johns Hopkins University. “The work ...

Abstract: Existing Zero-Shot Learning (ZSL) approaches for sensor-based Human Activity Recognition (HAR) often rely on external semantic information—such as attribute annotations or textual ...

In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Zahra Ghorrati is developing frameworks for human activity ...

Introduction: Advancements in sensing technologies have enabled the integration of inertial sensors, such as accelerometers and gyroscopes, into everyday devices like smartphones and wearables. These ...

Contactless Human Activity Recognition (HAR) has played a critical role in smart healthcare and elderly care homes to monitor patient behavior, detect falls or abnormal activities in real time. The ...

ABSTRACT: With technological advancements and increasing user demands, human action recognition plays a pivotal role in the field of human-computer interaction. Among various sensing devices, WiFi ...