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Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network
Joint Authors
Kong, Jianlei
Yan, Lei
Zhen, Tao
Wang, Lian-Ming
Zhou, Xiao-Lei
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-08
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Human gait phase recognition is a significant technology for rehabilitation training robot, human disease diagnosis, artificial prosthesis, and so on.
The efficient design of the recognition method for gait information is the key issue in the current gait phase division and eigenvalues extraction research.
In this paper, a novel voting-weighted integrated neural network (VWI-DNN) is proposed to detect different gait phases from multidimensional acceleration signals.
More specifically, it first employs a gait information acquisition system to collect different IMU sensors data fixed on the human lower limb.
Then, with dimensionality reduction and four-phase division preprocessing, key features are selected and merged as unified vectors to learn common and domain knowledge in time domain.
Next, multiple refined DNNs are transferred to design a multistream integrated neural network, which utilizes the mixture-granularity information to exploit high-dimensional feature representative.
Finally, a voting-weighted function is developed to fuse different submodels as a unified representation for distinguishing small discrepancy among different gait phases.
The end-to-end implementation of the VWI-DNN model is fine-tuned by the loss optimization of gradient back-propagation.
Experimental results demonstrate the outperforming performance of the proposed method with higher classification accuracy compared with the other methods, of which classification accuracy and macro-F1 is up to 99.5%.
More discussions are provided to indicate the potential applications in combination with other works.
American Psychological Association (APA)
Yan, Lei& Zhen, Tao& Kong, Jianlei& Wang, Lian-Ming& Zhou, Xiao-Lei. 2020. Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network. Complexity،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1142076
Modern Language Association (MLA)
Yan, Lei…[et al.]. Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network. Complexity No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1142076
American Medical Association (AMA)
Yan, Lei& Zhen, Tao& Kong, Jianlei& Wang, Lian-Ming& Zhou, Xiao-Lei. Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network. Complexity. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1142076
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142076