Announcing the University of Sussex-Huawei Locomotion Dataset
Daniel Roggen
We are pleased to announce that a "preview" version of the University of Sussex-Huawei Locomotion (SHL) Dataset is now available to the community.
== University of Sussex-Huawei Locomotion (SHL) dataset ==
The SHL dataset is a versatile annotated dataset of modes of locomotion and transportation of mobile users.
It was recorded over a period of 7 months in 2017 by 3 participants engaging in 8 different modes of transportation in real-life setting in the United Kingdom. The dataset contains multi-modal data from a body-worn camera and from 4 smartphones, carried simultaneously at typical body locations.
It contains all the sensors data available on the phones (motion sensors, GPS, Wifi, Cells, etc) and the body-worn camera footage.
The complete SHL dataset contains 750 hours of labelled locomotion data: Car (88 h), Bus (107 h), Train (115 h), Subway (89 h), Walk (127 h), Run (21 h), Bike (79 h), and Still (127 h). As participants carried 4 smartphones, the amount of data acquired is effectively 4 times larger (3000 hours in total).
== SHL Dataset - Preview ==
The preview of the SHL dataset now available for download contains 59 hours of annotated recordings, corresponding to 227 hours of data for the 4 phone locations.
It includes three recording-days per user and data from all three users and all four phone locations.
== Applications ==
This dataset is suitable for several applications
* Activity recognition
* Satellite, wifi, cell coverage estimation and prediction depending on transportation modes and location
* Road condition analysis
* Activity discovery
* Probabilistic mobility models
* Localisation using sensor fusion
* Vision-based activity recognition, object recognition
== Accessing the dataset ==
For more information head over to http://www.shl-dataset.org
Contact daniel...@ieee.org for other enquiries.