An Efficient CNN-LSTM Approach for Smartphone Sensor-Based Human Activity Recognition System
Author:
Affiliation:
1. National Institute of Technology Silchar,Dept. of Computer Science & Engineering,Silchar,Assam,India,788010
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10037244/10037254/10037495.pdf?arnumber=10037495
Reference21 articles.
1. Introducing a New Benchmarked Dataset for Activity Monitoring
2. Daily activity recognition based on DNN using environmental sound and acceleration signals
3. w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices
4. Gender Recognition using in-built Inertial Sensors of Smartphone
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2. Enhanced Complex Human Activity Recognition System: A Proficient Deep Learning Framework Exploiting Physiological Sensors and Feature Learning;IEEE Sensors Letters;2023-11
3. An Efficient and Lightweight Deep Learning Model for Human Activity Recognition on Raw Sensor Data in Uncontrolled Environment;IEEE Sensors Journal;2023-10-15
4. Federated Learning and Genetic Mutation for Multi-Resident Activity Recognition;2023 IEEE 19th International Conference on e-Science (e-Science);2023-10-09
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