LLANet: An Automatic Framework for Efficient Prediction of Lower Limb Activities Using sEMG Signals
Author:
Affiliation:
1. National Institute of Technology, Warangal, India
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/7782634/10494411/10502150.pdf?arnumber=10502150
Reference14 articles.
1. EMG physical action data set;Theodoridis,2011
2. Sarcopenia Recognition System Combined with Electromyography and Gait Obtained by the Multiple Sensor Module and Deep Learning Algorithm
3. EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm
4. Hybrid Deep Learning Approaches for sEMG Signal-Based Lower Limb Activity Recognition
5. Surface EMG signals and deep transfer learning-based physical action classification
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