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Deep CNN-BiLSTM Model for Transportation Mode Detection Using Smartphone Accelerometer and Magnetometer

Tang, Qinrui and Jahan, Kanwal and Roth, Michael (2022) Deep CNN-BiLSTM Model for Transportation Mode Detection Using Smartphone Accelerometer and Magnetometer. 2022 IEEE Intelligent Vehicles Symposium (IV), Aachen, Germany. doi: 10.1109/IV51971.2022.9827275.

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Official URL: https://ieeexplore.ieee.org/document/9827275

Abstract

Transportation mode detection from smartphone data is investigated as a relevant problem in the multi-modal transportation systems context. Neural networks are chosen as a timely and viable solution. The goal of this paper is to solve such a problem with a combination model of Convolutional Neural Network (CNN) and Bidirectional-Long short-term memory (BiLSTM) only processing accelerometer and magnetometer data. The performance in terms of accuracy and F1 score on the Sussex-Huawei Locomotion-Transportation (SHL) challenge 2018 dataset is comparable to methods that require the processing of a wider range of sensors. The uniqueness of our work is the light architecture requiring less computational resources for training and consequently a shorter inference time.

Item URL in elib:https://elib.dlr.de/188137/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:Deep CNN-BiLSTM Model for Transportation Mode Detection Using Smartphone Accelerometer and Magnetometer
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Tang, Qinruiqinrui.tang (at) dlr.dehttps://orcid.org/0000-0003-1327-1283
Jahan, KanwalKanwal.Jahan (at) dlr.deUNSPECIFIED
Roth, MichaelM.Roth (at) dlr.dehttps://orcid.org/0000-0002-4812-346X
Date:June 2022
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI:10.1109/IV51971.2022.9827275
Status:Published
Keywords:mode detection, self-supervised learning, deep learning, SHL dataset, BiLSTM, batch normalization
Event Title:2022 IEEE Intelligent Vehicles Symposium (IV)
Event Location:Aachen, Germany
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - VMo4Orte - Vernetzte Mobilität für lebenswerte Orte
Location: Berlin-Adlershof , Braunschweig
Institutes and Institutions:Institute of Transportation Systems
Deposited By: Tang, Qinrui
Deposited On:09 Sep 2022 15:16
Last Modified:09 Sep 2022 15:16

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