![](/images/graphics-bg.png)
Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network
Joint Authors
Shi, Xin
Qin, Pengjie
Zhu, Jiaqing
Xu, Shuyuan
Shi, Weiren
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-27
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
Exoskeleton robot is a typical application to assist the motion of lower limbs.
To make the lower extremity exoskeleton more flexible, it is necessary to identify various motion intentions of the lower limbs of the human body.
Although more sEMG sensors can be used to identify more lower limb motion intention, with the increase in the number of sensors, more and more data need to be processed.
In the process of human motion, the collected sEMG signal is easy to be interfered with noise.
To improve the practicality of the lower extremity exoskeleton robot, this paper proposed a wavelet packet transform- (WPT-) based sliding window difference average filtering feature extract algorithm and the unscented Kalman neural network (UKFNN) recognition algorithm.
We established an sEMG energy feature model, using a sliding window difference average filtering method to suppress noise interference and extracted stable feature values and using UKF filtering to optimize the neural network weights to improve the adaptability and accuracy of the recognition model.
In this paper, we collected the sEMG signals of three muscles to identify six lower limb motion intentions.
The average accuracy of 94.83% is proposed in this paper.
Experiments show that the algorithm improves the accuracy and anti-interference of motion intention recognition of lower limb sEMG signals.
The algorithm is superior to the backpropagation neural network (BPNN) recognition algorithm in the lower limb motion intention recognition and proves the effectiveness, novelty, and reliability of the method in this paper.
American Psychological Association (APA)
Shi, Xin& Qin, Pengjie& Zhu, Jiaqing& Xu, Shuyuan& Shi, Weiren. 2020. Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1196117
Modern Language Association (MLA)
Shi, Xin…[et al.]. Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network. Mathematical Problems in Engineering No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1196117
American Medical Association (AMA)
Shi, Xin& Qin, Pengjie& Zhu, Jiaqing& Xu, Shuyuan& Shi, Weiren. Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1196117
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196117