Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
Joint Authors
Zhao, Yu
Yang, Rennong
Chevalier, Guillaume
Xu, Ximeng
Zhang, Zhenxing
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-30
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Human activity recognition (HAR) has become a popular topic in research because of its wide application.
With the development of deep learning, new ideas have appeared to address HAR problems.
Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) is proposed.
The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state).
Second, residual connections between stacked cells act as shortcut for gradients, effectively avoiding the gradient vanishing problem.
Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked) dimensions, aiming to enhance the recognition rate.
When testing with the Opportunity dataset and the public domain UCI dataset, the accuracy is significantly improved compared with previous results.
American Psychological Association (APA)
Zhao, Yu& Yang, Rennong& Chevalier, Guillaume& Xu, Ximeng& Zhang, Zhenxing. 2018. Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1208808
Modern Language Association (MLA)
Zhao, Yu…[et al.]. Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors. Mathematical Problems in Engineering No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1208808
American Medical Association (AMA)
Zhao, Yu& Yang, Rennong& Chevalier, Guillaume& Xu, Ximeng& Zhang, Zhenxing. Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1208808
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208808