Enhanced Complex Human Activity Recognition System: A Proficient Deep Learning Framework Exploiting Physiological Sensors and Feature Learning
Author:
Affiliation:
1. Department of Computer Science and Engineering, National Institute of Technology Silchar, Cachar, India
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7782634/10274874/10288050.pdf?arnumber=10288050
Reference11 articles.
1. ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition
2. Contrastive Accelerometer–Gyroscope Embedding Model for Human Activity Recognition
3. An Efficient and Lightweight Deep Learning Model for Human Activity Recognition on Raw Sensor Data in Uncontrolled Environment
4. Human Activity Recognition for People with Knee Abnormality Using Surface Electromyography and Knee Angle Sensors
5. An Adaptive Batch Size-Based-CNN-LSTM Framework for Human Activity Recognition in Uncontrolled Environment
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2. Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition;IEEE Sensors Letters;2024-08
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4. A Hybrid Residual CNN with Channel Attention Mechanism for Continuous User Identification Using Wearable Motion Sensors;2024 47th International Conference on Telecommunications and Signal Processing (TSP);2024-07-10
5. An Efficient and Optimized CNN-LSTM Framework for Complex Human Activity Recognition System Using Surface EMG Physiological Sensors and Feature Engineering;2024 IEEE Students Conference on Engineering and Systems (SCES);2024-06-21
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