Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network
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 cons...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2019/4967261 |
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| _version_ | 1832552037175263232 |
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| author | Hong Zhao Chunning Hou Hala Alrobassy Xiangyan Zeng |
| author_facet | Hong Zhao Chunning Hou Hala Alrobassy Xiangyan Zeng |
| author_sort | Hong Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b159b5da2e5f4cd6b2affbf95f988935 |
| institution | Kabale University |
| issn | 2090-7141 2090-715X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-b159b5da2e5f4cd6b2affbf95f9889352025-02-03T05:59:43ZengWileyJournal of Computer Networks and Communications2090-71412090-715X2019-01-01201910.1155/2019/49672614967261Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural NetworkHong Zhao0Chunning Hou1Hala Alrobassy2Xiangyan Zeng3School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, ChinaDepartment of Mathematics and Computer Science, Fort Valley State University, Fort Valley, GA 31030, USASmartphones 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.http://dx.doi.org/10.1155/2019/4967261 |
| spellingShingle | Hong Zhao Chunning Hou Hala Alrobassy Xiangyan Zeng Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network Journal of Computer Networks and Communications |
| title | Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network |
| title_full | Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network |
| title_fullStr | Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network |
| title_full_unstemmed | Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network |
| title_short | Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network |
| title_sort | recognition of transportation state by smartphone sensors using deep bi lstm neural network |
| url | http://dx.doi.org/10.1155/2019/4967261 |
| work_keys_str_mv | AT hongzhao recognitionoftransportationstatebysmartphonesensorsusingdeepbilstmneuralnetwork AT chunninghou recognitionoftransportationstatebysmartphonesensorsusingdeepbilstmneuralnetwork AT halaalrobassy recognitionoftransportationstatebysmartphonesensorsusingdeepbilstmneuralnetwork AT xiangyanzeng recognitionoftransportationstatebysmartphonesensorsusingdeepbilstmneuralnetwork |