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 ...

Please provide your email address to receive an email when new articles are posted on . Machine learning can use patient-reported outcomes to identify low disease activity in rheumatoid arthritis.

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

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 ...

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 ...

School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, P. R. China Guangdong Provincial Key Laboratory of Sensing ...

Abstract: This paper explores human activity recognition (HAR) using machine learning models to classify activities based on sensor data with high precision. The study leverages the UCI HAR dataset, ...

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 ...