Szczegóły publikacji
Opis bibliograficzny
Recognition of drivers' activity based on 1D convolutional neural network / Rafał J. Doniec, Szymon Sieciński, Konrad M. Duraj, Natalia J. Piaseczna, Katarzyna Mocny-Pachońska, Ewaryst J. Tkacz // Electronics [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2079-9292 . — 2020 — vol. 9 iss. 12 art. no. 2002, s. 1–17. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 15–17, Abstr. — Publikacja dostępna online od: 2020-11-25. — Sz. Sieciński - afiliacja: Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland
Autorzy (6)
- Doniec Rafał
- Sieciński Szymon
- Duraj Konrad
- Piaseczna Natalia
- Mocny-Pachońska Katarzyna
- Tkacz Ewaryst
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164563 |
|---|---|
| Data dodania do BaDAP | 2026-01-27 |
| Tekst źródłowy | URL |
| DOI | 10.3390/electronics9122002 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Electronics |
Abstract
Background and objective: Driving a car is a complex activity which involves movements of the whole body. Many studies on drivers’ behavior are conducted to improve road traffic safety. Such studies involve the registration and processing of multiple signals, such as electroencephalography (EEG), electrooculography (EOG) and the images of the driver’s face. In our research, we attempt to develop a classifier of scenarios related to learning to drive based on the data obtained in real road traffic conditions via smart glasses. In our approach, we try to minimize the number of signals which can be used to recognize the activities performed while driving a car. Material and methods: We attempt to evaluate the drivers’ activities using both electrooculography (EOG) and a deep learning approach. To acquire data we used JINS MEME smart glasses furnished with 3-point EOG electrodes, 3-axial accelerometer and 3-axial gyroscope. Sensor data were acquired on 20 drivers (ten experienced and ten learner drivers) on the same 28.7 km route under real road conditions in southern Poland. The drivers performed several tasks while wearing the smart glasses and the tasks were linked to the signal during the drive. For the recognition of four activities (parking, driving through a roundabout, city traffic and driving through an intersection), we used one-dimensional convolutional neural network (1D CNN). Results: The maximum accuracy was 95.6% on validation set and 99.8% on training set. The results prove that the model based on 1D CNN can classify the actions performed by drivers accurately. Conclusions: We have proved the feasibility of recognizing drivers’ activity based solely on EOG data, regardless of the driving experience and style. Our findings may be useful in the objective assessment of driving skills and thus, improving driving safety. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
