Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network
Joint Authors
Hou, Chunning
Alrobassy, Hala
Zeng, Xiangyan
Hong, Zhao
Source
Journal of Computer Networks and Communications
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-03
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
Smartphones have been used for recognizing different transportation states.
However, current studies focus on the speed of the object, which only relies on the GPS sensor rather than considering other suitable sensors and actual application factors.
In this study, we propose a novel method that considers these factors comprehensively to enhance transportation state recognition.
The deep Bi-LSTM (bidirectional long short-term memory) neural network structure, the crowd-sourcing model, and the TensorFlow deep learning system are used to classify the transportation states.
Meanwhile, the data captured by the accelerometer and gyroscope sensors of smartphone is used to test and adjust the deep Bi-LSTM neural network model, making it easy to transfer the model into smartphones and conduct real-time recognition.
The experimental results show that this study achieves transportation activity classification with an accuracy of up to 92.8%.
The model of the deep Bi-LSTM neural network can be used for other time-series fields such as signal recognition and action analysis.
American Psychological Association (APA)
Hong, Zhao& Hou, Chunning& Alrobassy, Hala& Zeng, Xiangyan. 2019. Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network. Journal of Computer Networks and Communications،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1172317
Modern Language Association (MLA)
Hong, Zhao…[et al.]. Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network. Journal of Computer Networks and Communications No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1172317
American Medical Association (AMA)
Hong, Zhao& Hou, Chunning& Alrobassy, Hala& Zeng, Xiangyan. Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network. Journal of Computer Networks and Communications. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1172317
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1172317