Sensors-Based Human Activity Recognition Using Hybrid Features and Deep Capsule Network-Reference-Cited by-同舟云学术

Sensors-Based Human Activity Recognition Using Hybrid Features and Deep Capsule Network

Published:2024-07-15 Issue:14 Volume:24 Page:23129-23139
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Ghafoor Hafiz Yasir¹^ORCID,Jahangir Rashid²^ORCID,Jaffar Arfan¹,Alroobaea Roobaea³^ORCID,Saidani Oumaima⁴^ORCID,Alhayan Fatimah⁴

Affiliation:

1. Faculty of Computer Science and Information Technology, Superior University, Lahore, Pakistan

2. Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Vehari, Pakistan

3. Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

4. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia

Funder

Princess Nourah bint Abdulrahman University Researchers Supporting

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10599846/10538216.pdf?arnumber=10538216

Reference46 articles.

1. An Adaptive Batch Size-Based-CNN-LSTM Framework for Human Activity Recognition in Uncontrolled Environment

2. A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

3. Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor

4. IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment

5. Usual and Unusual Human Activity Recognition in Video using Deep Learning and Artificial Intelligence for Security Applications