Paper Info
Title
The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics With Mobile Devices.
Abstract
Scientific advances build on reproducible researches which need publicly available benchmark data sets. The computer vision and speech recognition communities have led the way in establishing benchmark data sets. There are much less data sets available in mobile computing, especially for rich locomotion and transportation analytics. This paper presents a highly versatile and precisely annotated large-scale data set of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users. The data set comprises seven months of measurements, collected from all sensors of four smartphones carried at typical body locations, including the images of a body-worn camera, while three participants used eight different modes of transportation in the south-east of the U.K., including in London. In total, 28 context labels were annotated, including transportation mode, participant's posture, inside/outside location, road conditions, traffic conditions, presence in tunnels, social interactions, and having meals. The total amount of collected data exceed 950 GB of sensor data, which corresponds to 2812 h of labeled data and 17 562 km of traveled distance. We present how we set up the data collection, including the equipment used and the experimental protocol. We discuss the data set, including the data curation process, the analysis of the annotations, and of the sensor data. We discuss the challenges encountered and present the lessons learned and some of the best practices we developed to ensure high quality data collection and annotation. We discuss the potential applications which can be developed using this large-scale data set. In particular, we present how a machine-learning system can use this data set to automatically recognize modes of transportations. Many other research questions related to transportation analytics, activity recognition, radio signal propagation and mobility modeling can be addressed through this data set. The full data set is being made available to the community, and a thorough preview is already published.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2858933
IEEE ACCESS
Keywords
Field
DocType
Activity recognition,context awareness,camera,intelligent transportation systems,GPS,GSM,locomotion dataset,multimodal sensors,pattern analysis,sensor fusion,supervised learning,transportation dataset,Wi-Fi
Mobile computing,Data collection,Data set,Activity recognition,Information retrieval,Computer science,Data curation,Mobile device,Global Positioning System,Analytics,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
26
PageRank 
References 
Authors
1.53
0
7
Name
Order
Citations
PageRank
Hristijan Gjoreski126829.81
Mathias Ciliberto2336.12
Lin Wang317715.50
Francisco Javier Ordóñez Morales4493.34
Sami Mekki5322.68
Stefan Valentin611712.40
Daniel Roggen71851137.05