research-article

Accelerometer-based transportation mode detection on smartphones

Authors:

Samuli Hemminki,

Sasu TarkomaAuthors Info & Claims

SenSys '13: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

Article No.: 13, Pages 1 - 14

https://doi.org/10.1145/2517351.2517367

Published: 11 November 2013 Publication History

Abstract

We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.

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Cited By

Mostafa SHarras KYoussef M(2025)Robust and Ubiquitous Mobility Mode Estimation Using Limited Cellular InformationIEEE Transactions on Vehicular Technology10.1109/TVT.2024.345420874:1(1310-1321)Online publication date: Jan-2025
https://doi.org/10.1109/TVT.2024.3454208
Erdelić MCarić TErdelić TŠemanjski I(2025)Enhancing transport mode classification benchmark by integrating spatial independence with multimodal datasetTravel Behaviour and Society10.1016/j.tbs.2024.10092938(100929)Online publication date: Jan-2025
https://doi.org/10.1016/j.tbs.2024.100929
Molina-Campoverde JRivera-Campoverde NMolina Campoverde PBermeo Naula A(2024)Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPSSensors10.3390/s2412388424:12(3884)Online publication date: 15-Jun-2024
https://doi.org/10.3390/s24123884
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Index Terms

Accelerometer-based transportation mode detection on smartphones
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

SenSys '13: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

November 2013

443 pages

ISBN:9781450320276

DOI:10.1145/2517351

General Chair:
Chiara Petrioli
University of Rome `La Sapienza'
,
Program Chairs:
Landon Cox
Duke University
,
Kamin Whitehouse
University of Virginia

Copyright © 2013 ACM.

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Published: 11 November 2013

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SenSys '13: The 11th ACM Conference on Embedded Network Sensor Systems

November 11 - 15, 2013

Roma, Italy

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SenSys '13 Paper Acceptance Rate 21 of 123 submissions, 17%;

Overall Acceptance Rate 174 of 867 submissions, 20%

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Cited By

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https://doi.org/10.1109/TVT.2024.3454208
Erdelić MCarić TErdelić TŠemanjski I(2025)Enhancing transport mode classification benchmark by integrating spatial independence with multimodal datasetTravel Behaviour and Society10.1016/j.tbs.2024.10092938(100929)Online publication date: Jan-2025
https://doi.org/10.1016/j.tbs.2024.100929
Molina-Campoverde JRivera-Campoverde NMolina Campoverde PBermeo Naula A(2024)Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPSSensors10.3390/s2412388424:12(3884)Online publication date: 15-Jun-2024
https://doi.org/10.3390/s24123884
Ortiz BGupta VKumar RJalin ACao XZiegenbein CSinghal AChoi STewari M(2024)Data Preprocessing Techniques for Artificial Intelligence (AI)/Machine Learning (ML)-Readiness: Systematic Review of Wearable Sensor Data in Cancer Care (Preprint)JMIR mHealth and uHealth10.2196/59587Online publication date: 16-Apr-2024
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Mostafa SYoussef MHarras K(2024)ModeSense: Ubiquitous and Accurate Transportation Mode Detection using Serving Cell Tower InformationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691250(184-195)Online publication date: 29-Oct-2024
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Hwang SLeng ZOh SKim KPlötz TKostakos VKay JHoang T(2024)More Data for People with Disabilities! Comparing Data Collection Efforts for Wheelchair Transportation Mode DetectionProceedings of the 2024 ACM International Symposium on Wearable Computers10.1145/3675095.3676617(82-88)Online publication date: 5-Oct-2024
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Yao QLiu YSun XDong XJi XMa JLuo BLiao XXu JKirda ELie D(2024)Watch the Rhythm: Breaking Privacy with Accelerometer at the Extremely-Low Sampling Rate of 5HzProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690370(1776-1790)Online publication date: 2-Dec-2024
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Hwang SHeo JCho YMoon JLee YKim HCha JKim K(2024)Transportation Mode Detection Technology to Predict Wheelchair Users' Life Satisfaction in Seoul, South KoreaProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435068:1(1-20)Online publication date: 6-Mar-2024
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