| dc.date.accessioned |
2016-08-17T09:59:32Z |
und |
| dc.date.accessioned |
2017-10-24T12:24:18Z |
|
| dc.date.available |
2016-08-17T09:59:32Z |
und |
| dc.date.available |
2017-10-24T12:24:18Z |
|
| dc.date.issued |
2016-08-17T09:59:32Z |
|
| dc.identifier.uri |
http://radr.hulib.helsinki.fi/handle/10138.1/5692 |
und |
| dc.identifier.uri |
http://hdl.handle.net/10138.1/5692 |
|
| dc.title |
Learning to Detect Drowsiness in Drivers |
en |
| ethesis.discipline |
Computer science |
en |
| ethesis.discipline |
Tietojenkäsittelytiede |
fi |
| ethesis.discipline |
Datavetenskap |
sv |
| ethesis.discipline.URI |
http://data.hulib.helsinki.fi/id/1dcabbeb-f422-4eec-aaff-bb11d7501348 |
|
| ethesis.department.URI |
http://data.hulib.helsinki.fi/id/225405e8-3362-4197-a7fd-6e7b79e52d14 |
|
| ethesis.department |
Institutionen för datavetenskap |
sv |
| ethesis.department |
Department of Computer Science |
en |
| ethesis.department |
Tietojenkäsittelytieteen laitos |
fi |
| ethesis.faculty |
Matematisk-naturvetenskapliga fakulteten |
sv |
| ethesis.faculty |
Matemaattis-luonnontieteellinen tiedekunta |
fi |
| ethesis.faculty |
Faculty of Science |
en |
| ethesis.faculty.URI |
http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca |
|
| ethesis.university.URI |
http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97 |
|
| ethesis.university |
Helsingfors universitet |
sv |
| ethesis.university |
University of Helsinki |
en |
| ethesis.university |
Helsingin yliopisto |
fi |
| dct.creator |
Takalahti, Antti Tapani |
|
| dct.issued |
2016 |
|
| dct.language.ISO639-2 |
eng |
|
| dct.abstract |
Supervised machine learning consists of inferring a function from labelled examples. The examples used in this study are twenty second stretches of driving data and the labels are binary values of visually scored drowsiness.The ultimate goal is to predict future driving performance by looking only at the steering wheel angle and the position of the accelerator pedal over time.
Chapter two explains what drowsiness is, how tools such as electroencephalogram and electrooculogram reveal drowsiness, and what other tools have been used to study drowsiness.
Chapter three shows how a driver's drowsiness can be detected, and what the psychological aspects relevant to driving performance are. Some currently used methods are also described.
Chapter four shows how the Karolinska drowsiness score is derived from electroencephalogram and electrooculogram, and how this score is used to train the nearest neighbour classifier to detect drowsiness from individual segments of driving data and further to predict driving performance. |
en |
| dct.language |
en |
|
| ethesis.language.URI |
http://data.hulib.helsinki.fi/id/languages/eng |
|
| ethesis.language |
English |
en |
| ethesis.language |
englanti |
fi |
| ethesis.language |
engelska |
sv |
| ethesis.thesistype |
pro gradu-avhandlingar |
sv |
| ethesis.thesistype |
pro gradu -tutkielmat |
fi |
| ethesis.thesistype |
master's thesis |
en |
| ethesis.thesistype.URI |
http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis |
|
| dct.identifier.urn |
URN:NBN:fi-fe2017112251227 |
|
| dc.type.dcmitype |
Text |
|
