Gjoreski, Hristijan, Ciliberto, Mathias, Ordoñez Morales, Francisco Javier, Roggen, Daniel, Mekki, Sami and Valentin, Stefan (2017) A versatile annotated dataset for multimodal locomotion analytics with mobile devices. In: The 15th ACM Conference on Embedded Networked Sensor Systems (SenSys 2017), Sunday 5th - Wednesday 8th November 2017, Delft, The Netherlands.

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Abstract

We explain how to obtain a highly versatile and precisely annotated dataset for the multimodal locomotion of mobile users. After presenting the experimental setup, data management challenges and potential applications, we conclude with the best practices for assuring data quality and reducing loss. The dataset currently comprises 7 months of measurements, collected by smartphone’s sensors and a body-worn camera, while the 3 participants used 8 different modes of transportation. It comprises 950 GB of sensor data, which corresponds to 750 hours of labelled data. The obtained data will be useful for a wide range of research questions related to activity recognition, and will be made available to the community.

Item Type:	Conference or Workshop Item (Poster)
Keywords:	dataset, activity recognition, smartphone, transportation
Schools and Departments:	School of Engineering and Informatics > Engineering and Design
Research Centres and Groups:	Sensor Technology Research Centre
Subjects:	Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Related URLs:	https://dl.acm.org/citation.cfm?id=31316...
Depositing User:	Hristijan Gjoreski
Date Deposited:	19 Sep 2017 09:29
Last Modified:	31 May 2018 15:07
URI:	http://srodev.sussex.ac.uk/id/eprint/70230

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