Inferring transportation mode and human activity from mobile sensing in daily life

Jonathan Liono, Zahraa S. Abdallah*, A. K. Qin, Flora D. Salim

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems
Subtitle of host publicationComputing, Networking and Services, Mobiquitous 2018
PublisherAssociation for Computing Machinery (ACM)
Pages342-351
Number of pages10
ISBN (Electronic)9781450360937
DOIs
Publication statusPublished - 5 Nov 2018
Event15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Mobiquitous 2018 - New York, United States
Duration: 5 Nov 20187 Nov 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Mobiquitous 2018
CountryUnited States
CityNew York
Period5/11/187/11/18

Keywords

  • Context modelling
  • Human activity recognition
  • Transportation mode
  • Ubiquitous computing

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