Published December 14, 2020 | Version 1
Dataset Restricted

Driving data for simulated sleepiness, real sleep deprivation and normal controls.

Authors/Creators

  • 1. Royal Papworth Hospital

Description

The data set is an output of the Track and Know project to be shared with the scientific community. The data set contains the output of a monitoring app recorded during a series of journeys organised in two sub sets.

The first subset of data was recorded from journeys made on different days around a

circular route by a single driver. On different iterations of the journey the driver determined to drive either; as carefully as possible, normally or poorly. The intention of the poor driving was to imitate sleepy driving with harsh breaking, cornering and acceleration and deliberate lane drifting. The data set is designed to allow the development of algorithms to detect different driving behaviours

The second data set was generated by 3 volunteers who were engaged in shift work. The journeys consist of trips to work and home at different times of day and other journeys not related to work. The intention of the data set is to allow comparisons to be made between journeys undertaken by the drivers when sleep replete and sleep deprived (after working a night shift).

The data are enriched with weather information pertaining to the date, time and location of each journey.

Notes

Contains Personally Identifiable Information

Files

Restricted

The record is publicly accessible, but files are restricted. Log in to check if you have access.

Request access

If you would like to request access to these files, please fill out the form below.

You need to satisfy these conditions in order for this request to be accepted:

In order to access the data please submit a request to ian.smith38@nhs.net . In the request please specify your institution (if any) and the purpose proposed for using the dataset. An agreement has to be signed about the care of the retention of this data as it contains PII.

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Additional details

Funding

European Commission
Track and Know - Big Data for Mobility Tracking Knowledge Extraction in Urban Areas 780754