Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3341162.3344855acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

Transportation mode classification from smartphone sensors via a long-short-term-memory network

Published: 09 September 2019 Publication History

Abstract

This article introduce the architecture of a Long-Short-Term-Memory network for classifying transportation-modes via smartphone data and evaluates its accuracy. By using a Long-Short-Term-Memory with common preprocessing steps such as normalisation for classification tasks an F1-Score accuracy of 63.68 % was achieved with an internal test dataset. We participated as team "GanbareAMT" in the "SHL recognition challenge".

References

[1]
F. A. Gers, J. Schmidhuber, and F. Cummins. 1999. Learning to forget: continual prediction with LSTM. In 1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), Vol. 2. 850--855 vol.2.
[2]
Hristijan Gjoreski, Mathias Ciliberto, Lin Wang, Francisco Javier Ordonez Morales, Sami Mekki, Stefan Valentin, and Daniel Roggen. 2018. The university of sussex-huawei locomotion and transportation dataset for multimodal analytics with mobile devices,. IEEE Access 6 (23 July 2018), 42592--42604.
[3]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (1997), 1735--1780.
[4]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. CoRR abs/1412.6980 (2015).
[5]
K. Kunze and P. Lukowicz. 2014. Sensor Placement Variations in Wearable Activity Recognition. IEEE Pervasive Computing 13, 4 (Oct 2014), 32--41.
[6]
L. Wang, H. Gjoreski, M. Ciliberto, P. Lago, K. Murao, T. Okita, and D. Roggen. 2019. Summary of the Sussex-Huawei locomotion-transportation recognition challenge 2019. Proc. HASCA 2019.
[7]
L. Wang, H. Gjoreski, M. Ciliberto, S. Mekki, S. Valentin, and D. Roggen. 2019. Enabling Reproducible Research in Sensor-Based Transportation Mode Recognition With the Sussex-Huawei Dataset. IEEE Access 7 (2019), 10870--10891.
[8]
Tahmina Zebin, Matthew Sperrin, Niels Peek, and Alex Casson. 2018. Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks. In IEEE EMBC.
[9]
Yu Zheng, Hao Fu, Xing Xie, Wei-Ying Ma, and Quannan Li. 2011. Geolife GPS trajectory dataset - User Guide (geolife gps trajectories 1.1 ed.). https://www.microsoft.com/en-us/research/publication/geolife-gps-trajectory-dataset-user-guide/ Geolife GPS trajectories 1.1.

Cited By

View all
  • (2024)Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative StudyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3323535(1-21)Online publication date: 2024
  • (2024)Estimator: An Effective and Scalable Framework for Transportation Mode Classification Over TrajectoriesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.344565225:11(15562-15573)Online publication date: Nov-2024
  • (2024)Automatic classification of transportation modes using smartphone sensors: addressing imbalanced data and enhancing training with focal loss and artificial bee colony algorithmJournal of Optics10.1007/s12596-024-01703-653:5(4656-4670)Online publication date: 16-Mar-2024
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
September 2019
1234 pages
ISBN:9781450368698
DOI:10.1145/3341162
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. IMU
  2. LSTM
  3. classification
  4. inertial
  5. mode of transportation
  6. phones
  7. supervised machine learning

Qualifiers

  • Research-article

Funding Sources

  • Federal Ministry of Education and Research

Conference

UbiComp '19

Acceptance Rates

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)5
  • Downloads (Last 6 weeks)2
Reflects downloads up to 05 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative StudyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3323535(1-21)Online publication date: 2024
  • (2024)Estimator: An Effective and Scalable Framework for Transportation Mode Classification Over TrajectoriesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.344565225:11(15562-15573)Online publication date: Nov-2024
  • (2024)Automatic classification of transportation modes using smartphone sensors: addressing imbalanced data and enhancing training with focal loss and artificial bee colony algorithmJournal of Optics10.1007/s12596-024-01703-653:5(4656-4670)Online publication date: 16-Mar-2024
  • (2024)Smartphone detector examination for transportation mode identification utilizing imbalanced maximizing-area under the curve proximal support vector machineSignal, Image and Video Processing10.1007/s11760-024-03479-518:11(8361-8377)Online publication date: 13-Aug-2024
  • (2024)Integration von Wearables und Nutzung von digitalen Biomarkern zur Diagnostik und Therapie im GesundheitswesenHealth Data Management10.1007/978-3-658-43236-2_31(323-336)Online publication date: 13-Mar-2024
  • (2023)Research on Transportation Mode Recognition Based on Multi-Head Attention Temporal Convolutional NetworkSensors10.3390/s2307358523:7(3585)Online publication date: 29-Mar-2023
  • (2022)Transportation Mode Detection Using Temporal Convolutional Networks Based on Sensors Integrated into SmartphonesSensors10.3390/s2217671222:17(6712)Online publication date: 5-Sep-2022
  • (2022)MSCPT: Toward Cross-Place Transportation Mode Recognition Based on Multi-Sensor Neural Network ModelIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.311526423:8(12588-12600)Online publication date: Aug-2022
  • (2020)Analyzing the Importance of Sensors for Mode of Transportation ClassificationSensors10.3390/s2101017621:1(176)Online publication date: 29-Dec-2020
  • (2020)Combining LSTM and CNN for mode of transportation classification from smartphone sensorsAdjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers10.1145/3410530.3414350(305-310)Online publication date: 10-Sep-2020
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media