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

Liono, J, Abdallah, Z, Qin, A and Salim, F 2018, 'Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life', in Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2018), New York City, United States, 5-7 November 2018, pp. 342-351.


Document type: Conference Paper
Collection: Conference Papers

Title Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life
Author(s) Liono, J
Abdallah, Z
Qin, A
Salim, F
Year 2018
Conference name MobiQuitous 2018
Conference location New York City, United States
Conference dates 5-7 November 2018
Proceedings title Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2018)
Publisher Association for Computer Machinery
Place of publication United States
Start page 342
End page 351
Total pages 10
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.
Subjects Ubiquitous Computing
Conceptual Modelling
Keyword(s) context modelling
human activity recognition
transportation mode
ubiquitous computing
DOI - identifier 10.1145/3286978.3287006
Copyright notice © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ISBN 9781450360937
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