Left or right? Detecting driver’s head movement on the road
Shojaeifard, Leyla; Islam, Adnanul; Shaheen, Hassan; Schroderus, Vappu; Peltonen, Ella (2024-03-22)
ACM
22.03.2024
Shojaeifard, L., Islam, A., Shaheen, H., Schroderus, V., & Peltonen, E. (2023). Left or right? Detecting driver’s head movement on the road. Proceedings of the International Conference on the Internet of Things, 90–97. https://doi.org/10.1145/3627050.3627067
https://creativecommons.org/licenses/by/4.0/
© 2023 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
© 2023 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202404052561
https://urn.fi/URN:NBN:fi:oulu-202404052561
Tiivistelmä
Abstract
The Internet of Things is enabling innovations in the automotive industry by expanding the capabilities of vehicles by connecting them with the cloud. One important application domain is traffic safety, which can benefit from monitoring the driver’s condition on how safely they are handling the vehicle. By detecting drowsiness, inattentiveness, and distraction of the driver, it is possible to react before accidents happen. This paper uses accelerometer and gyroscope data collected using an ear-worn sensor to classify the orientation of the driver’s head in a moving vehicle. We show that lightweight machine learning algorithms such as Random Forest and K-Nearest Neighbor can be used to reach accurate classifications even without applying any noise reduction to the signal data. Data cleaning and transformation approaches are studied to see how they give deeper insights into the classification problem. This study paves the way for the development of driver monitoring systems capable of reacting to anomalous driving behaviour before traffic accidents can happen.
The Internet of Things is enabling innovations in the automotive industry by expanding the capabilities of vehicles by connecting them with the cloud. One important application domain is traffic safety, which can benefit from monitoring the driver’s condition on how safely they are handling the vehicle. By detecting drowsiness, inattentiveness, and distraction of the driver, it is possible to react before accidents happen. This paper uses accelerometer and gyroscope data collected using an ear-worn sensor to classify the orientation of the driver’s head in a moving vehicle. We show that lightweight machine learning algorithms such as Random Forest and K-Nearest Neighbor can be used to reach accurate classifications even without applying any noise reduction to the signal data. Data cleaning and transformation approaches are studied to see how they give deeper insights into the classification problem. This study paves the way for the development of driver monitoring systems capable of reacting to anomalous driving behaviour before traffic accidents can happen.
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