research-article

Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life

Authors:

Jonathan Liono,

Zahraa S. Abdallah,

Flora D. SalimAuthors Info & Claims

MobiQuitous '18: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 342 - 351

https://doi.org/10.1145/3286978.3287006

Published: 05 November 2018 Publication History

Abstract

In this paper, we focus on simultaneous inference of transportation modes and human activities in daily life via modelling and inference from multivariate time series data, which are streamed from off-the-shelf mobile sensors (e.g. embedded in smartphones) in real-world dynamic environments. The transportation mode will be inferred from the structured hierarchical contexts associated with human activities. Through our mobile context recognition system, an accurate and robust solution can be obtained to infer transportation mode, human activity and their associated contexts (e.g. whether the user is in moving or stationary environment) simultaneously. There are many challenges in analysing and modelling human mobility patterns within urban areas due to the ever-changing environments of mobile users. For instance, a user could stay at a particular location and then travel to various destinations depending on the tasks they carry within a day. Consequently, there is a need to reduce the reliance on location-based sensors (e.g. GPS), since they consume a significant amount of energy on smart devices, for the purpose of intelligent mobile sensing (i.e. automatic inference of transportation mode, human activity and associated contexts). Nevertheless, our system is capable of outperforming the simplistic approach that only considers independent classifications of multiple context label sets on data streamed from low-energy sensors.

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Index Terms

Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Human-centered computing
  1. Ubiquitous and mobile computing

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MobiQuitous '18: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

November 2018

490 pages

ISBN:9781450360937

DOI:10.1145/3286978

General Chairs:
Henning Schulzrinne
Columbia University, USA
,
Pan Li
Case Western Reserve University, USA

Copyright © 2018 ACM.

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].

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Published: 05 November 2018

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MobiQuitous '18: Computing, Networking and Services

November 5 - 7, 2018

NY, New York, USA

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https://dl.acm.org/doi/10.1145/3656470
Nepal SLiu WPillai AWang WVojdanovski VHuckins JRogers CMeyer MCampbell A(2024)Capturing the College ExperienceProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435018:1(1-37)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643501
Sarwar AAlmadani AAgu E(2024)Few-shot meta-learning for pre-symptomatic detection of Covid-19 from limited health tracker dataSmart Health10.1016/j.smhl.2024.10045932(100459)Online publication date: Jun-2024
https://doi.org/10.1016/j.smhl.2024.100459
Fryan LAlazzam M(2022)Survival Analysis of Oncological Patients Using Machine Learning MethodHealthcare10.3390/healthcare1101008011:1(80)Online publication date: 27-Dec-2022
https://doi.org/10.3390/healthcare11010080
Matthes PSpringer T(2022)Selecting Resource-Efficient ML Models for Transport Mode Detection on Mobile Devices2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)10.1109/IoTaIS56727.2022.9976004(135-141)Online publication date: 24-Nov-2022
https://doi.org/10.1109/IoTaIS56727.2022.9976004
Kang YRahaman MRen YSanderson MWhite RSalim F(2022)App usage on-the-move: Context- and commute-aware next app predictionPervasive and Mobile Computing10.1016/j.pmcj.2022.10170487(101704)Online publication date: Dec-2022
https://doi.org/10.1016/j.pmcj.2022.101704
do Nascimento LMachado GMaran Vde Oliveira J(2021)Context recognition and ubiquitous computing in smart cities: a systematic mappingComputing10.1007/s00607-020-00878-7103:5(801-825)Online publication date: 5-Jan-2021
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Friedrich BLübbe CHein A(2020)Analyzing the Importance of Sensors for Mode of Transportation ClassificationSensors10.3390/s2101017621:1(176)Online publication date: 29-Dec-2020
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Friedrich BLübbe CHein ATentori MWeibel NVan Laerhoven KAbowd GSalim F(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
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Zhao YWang XLi JZhang DYang Z(2019)CellTransProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33512833:3(1-26)Online publication date: 9-Sep-2019
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