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

Liono, Jonathan and Abdallah, Zahraa S. and Qin, A.K and Salim, Flora (2018) Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life. In: MobiQuitous 2018 - 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services.

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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 ac- curate 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 en- vironments of the mobile users. For instance, a user could stay at a particular location and then travel to various destinations depend- ing 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 de- vices, 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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN 978-1-4503-6093-7
11 September 2018Accepted
1 November 2018Published Online
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Zahraa Abdallah
Date Deposited: 30 Nov 2018 11:35
Last Modified: 22 Mar 2023 12:01

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