Deep CNN-GRU Based Human Activity Recognition with Automatic Feature Extraction Using Smartphone and Wearable Sensors
Author:
Affiliation:
1. Institute of Information Technology, Jahangirnagar University,Dhaka,Bangladesh
2. School of Health and Rehabilitation Sciences, The University of Queensland St Lucia,Faculty of Health and Behavioural Sciences,QLD,Australia,4072
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10101485/10100742/10101550.pdf?arnumber=10101550
Reference27 articles.
1. Detecting Violent Arm Movements Using CNN-LSTM
2. Gate-variants of Gated Recurrent Unit (GRU) neural networks
3. Collecting complex activity datasets in highly rich networked sensor environments
4. Transition-Aware Human Activity Recognition Using Smartphones
5. Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
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2. Human motion activity recognition and pattern analysis using compressed deep neural networks;Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization;2024-04-04
3. A CNN-MLP Deep Model for Sensor-based Human Activity Recognition;2023 15th International Conference on Innovations in Information Technology (IIT);2023-11-14
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