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Human Activities

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Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.

PloS one
Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely. Thus, detecting the rumors and preventing their spr...

MSST-RT: Multi-Stream Spatial-Temporal Relative Transformer for Skeleton-Based Action Recognition.

Sensors (Basel, Switzerland)
Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN). The most important work is ST-GCN, which automatically learns both spatial and temporal patterns from skeleton sequ...

Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection.

Sensors (Basel, Switzerland)
Human action recognition has attracted considerable research attention in the field of computer vision, especially for classroom environments. However, most relevant studies have focused on one specific behavior of students. Therefore, this paper pro...

Daily Human Activity Recognition Using Non-Intrusive Sensors.

Sensors (Basel, Switzerland)
In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in t...

IPGN: Interactiveness Proposal Graph Network for Human-Object Interaction Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Human-Object Interaction (HOI) Detection is an important task to understand how humans interact with objects. Most of the existing works treat this task as an exhaustive triplet 〈 human, verb, object 〉 classification problem. In this paper, we decomp...

INIM: Inertial Images Construction with Applications to Activity Recognition.

Sensors (Basel, Switzerland)
Human activity recognition aims to classify the user activity in various applications like healthcare, gesture recognition and indoor navigation. In the latter, smartphone location recognition is gaining more attention as it enhances indoor positioni...

Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones.

Scientific reports
The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and ...

Human Interaction Understanding With Joint Graph Decomposition and Node Labeling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The task of human interaction understanding involves both recognizing the action of each individual in the scene and decoding the interaction relationship among people, which is useful to a series of vision applications such as camera surveillance, v...

ASNet: Auto-Augmented Siamese Neural Network for Action Recognition.

Sensors (Basel, Switzerland)
Human action recognition methods in videos based on deep convolutional neural networks usually use random cropping or its variants for data augmentation. However, this traditional data augmentation approach may generate many non-informative samples (...

Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification to provide healthcare of higher standards. The purpose of this stu...