Human Activity Recognition Using Graph Structures and Deep Neural Networks
Human activity recognition (HAR) systems are essential in healthcare, surveillance, and sports analytics, enabling automated movement analysis. This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities....
Saved in:
Main Author: | Abed Al Raoof K. Bsoul |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-12-01
|
Series: | Computers |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-431X/14/1/9 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bond Graph-Based Modelling and Control of Electromagnetic Levitation System Using Firefly Optimization
by: Ismail Dif, et al.
Published: (2023-05-01) -
Spatial-Temporal Fusion Graph Neural Networks With Mixed Adjacency for Weather Forecasting
by: Ang Guo, et al.
Published: (2025-01-01) -
3D Point Cloud from Millimeter-wave Radar for Human Action Recognition: Dataset and Method
by: Biao JIN, et al.
Published: (2025-02-01) -
Graph Convolutional Networks for multi-modal robotic martial arts leg pose recognition
by: Shun Yao, et al.
Published: (2025-01-01) -
Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm
by: Mu Panliang, et al.
Published: (2025-02-01)