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