Transfer Learning-Based Muscle Activity Decoding Scheme by Low-frequency sEMG for Wearable Low-cost Application

The surface electromyogram (sEMG) contains a wealth of motion information, which can reflect user's muscle motion intentions. The decoding based on sEMG has been widely used to provide a safe and effective human-computer interaction (HCI) method for neural prosthesis and exoskeleton robot contr...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.22804-22815
Main Authors: Li, Yurong, Zhang, Wenxuan, Zhang, Qian, Zheng, Nan
Format: Article
Language:English
Subjects:
Algorithms
Decoding
Deep learning
Discriminant analysis
Electromyography
EMG
Exoskeletons
Feature extraction
hand gesture recognition
Human-computer interface
Kernel
Kernel functions
Low cost
low-frequency sEMG
Machine learning
motor intention decoding
Muscles
Neural prostheses
Robot control
Support vector machines
Transfer learning
Wearable technology
White noise
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The surface electromyogram (sEMG) contains a wealth of motion information, which can reflect user's muscle motion intentions. The decoding based on sEMG has been widely used to provide a safe and effective human-computer interaction (HCI) method for neural prosthesis and exoskeleton robot control. The motor intention decoding based on low sampling frequency sEMG may promote the application of wearable low-cost EMG sensors in HCI. Therefore, a motor intention decoding scheme suitable for low frequency EMG signal is proposed in this paper, that is, transfer learning based on Alexnet. Moreover, the effects of different feature extraction methods and data augmentation with Gaussian white noise are fully analyzed. The proposed algorithm is evaluated with the NinaPro database 5. The highest accuracy can reach 70.4%±4.36% in 53 gestures identification of 10 subjects. Some classical machine learning algorithms such as support vector machine (SVM), linear discriminant analysis (LDA) and K Nearest Neighbor (KNN) are chosen to make comparison, where the SVM with Gaussian kernel function reaches to the maximum accuracy of 67.98%±4.56%. Two-way variance results show significant differences between each other. The experiment results show that the transfer learning is effective for decoding low-frequency sEMG for a large number of gestures.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3056412