Attention-Based Multihead Deep Learning Framework for Online Activity Monitoring With Smartwatch Sensors-Reference-Cited by-同舟云学术

Attention-Based Multihead Deep Learning Framework for Online Activity Monitoring With Smartwatch Sensors

Published:2023-10-15 Issue:20 Volume:10 Page:17746-17754
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Thakur Dipanwita¹^ORCID,Guzzo Antonella²^ORCID,Fortino Giancarlo²^ORCID

Affiliation:

1. Department of Computer Science, Banasthali Vidyapith, Jaipur, India

2. Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, Italy

Funder

WP9.6: “KRR Frameworks for Green-Aware AI” through the PNRR Project FAIR—Future AI Research

Spoke 9—Green-Aware AI through the NRRP MUR Program funded by the NextGenerationEU

PRIN COMMON-WEARS Project funded by the Italian Minister of Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10269651/10129193.pdf?arnumber=10129193

Reference35 articles.

1. Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques

2. Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges

3. Two-Stream Convolutional Network for Improving Activity Recognition Using Convolutional Long Short-Term Memory Networks

4. Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications

5. Combining CNN and LSTM for activity of daily living recognition with a 3D matrix skeleton representation

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