Version 1
: Received: 22 April 2024 / Approved: 22 April 2024 / Online: 23 April 2024 (11:54:19 CEST)
How to cite:
Piaseczna, N.; Doniec, R.; Duraj, K.; Sieciński, S.; Jędrychowski, M.; Tkacz, E.; Grzegorzek, M. Distinguishing Drivers via Wearable Sensor Data and Machine Learning. Preprints2024, 2024041424. https://doi.org/10.20944/preprints202404.1424.v1
Piaseczna, N.; Doniec, R.; Duraj, K.; Sieciński, S.; Jędrychowski, M.; Tkacz, E.; Grzegorzek, M. Distinguishing Drivers via Wearable Sensor Data and Machine Learning. Preprints 2024, 2024041424. https://doi.org/10.20944/preprints202404.1424.v1
Piaseczna, N.; Doniec, R.; Duraj, K.; Sieciński, S.; Jędrychowski, M.; Tkacz, E.; Grzegorzek, M. Distinguishing Drivers via Wearable Sensor Data and Machine Learning. Preprints2024, 2024041424. https://doi.org/10.20944/preprints202404.1424.v1
APA Style
Piaseczna, N., Doniec, R., Duraj, K., Sieciński, S., Jędrychowski, M., Tkacz, E., & Grzegorzek, M. (2024). Distinguishing Drivers via Wearable Sensor Data and Machine Learning. Preprints. https://doi.org/10.20944/preprints202404.1424.v1
Chicago/Turabian Style
Piaseczna, N., Ewaryst Tkacz and Marcin Grzegorzek. 2024 "Distinguishing Drivers via Wearable Sensor Data and Machine Learning" Preprints. https://doi.org/10.20944/preprints202404.1424.v1
Abstract
This article uses machine learning analysis of wearable sensor data to explore the topic of driver differentiation. The study focuses on using detailed analyzes of data collected from wearable sensors to identify patterns that differentiate skilled drivers from inexperienced ones. On a predetermined driving route, participants experienced a variety of driving conditions, including parking, navigating cities, driving on highways and driving through residential neighborhoods. The gathered data was analyzed using machine learning techniques, yielding a classification accuracy of 94% overall. The results highlight important differences in sensor data between inexperienced and experienced drivers, providing insight into possible uses for improving safety protocols, driver education, and personalized feedback systems. The article fosters improvements in traffic safety and driver education by offering insightful information on the link between driver behavior analysis and machine learning.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
