ABSTRACT
One of the factors for stress reduction among vehicle drivers is to be aware that stress is present. This project presents a biometric interface for stress detection in drivers, built with open source sensors and hardware. In two series of experiments, we induce stress in test subjects by making them drive progressively difficult scenarios in a simulator. Using the C4.5 classification algorithm, we classified the subjects' biometric data in order to determine whether the subject was stressed or not. In another series of experiments, we tested the efficacy of two driver feedback systems, a haptic one and a visual one. Identifying a stressful situation allows real-time feedback to drivers, so they can be aware of their stressed state, thus being able to take corrective actions on time, and avoid behavior leading to an accident.
- Adafruit. NeoPixel Ring. Retrieved July 13, 2018 from https://www.adafruit.com/product/1643Google Scholar
- Arduino. Arduino Uno Rev 3. Retrieved July 13, 2018 from https://store.arduino.cc/usa/arduino-uno-rev3Google Scholar
- J. Bakker, M. Pechenizkiy and N. Sidorova. What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data. 2011 IEEE 11th International Conference on Data Mining Workshops, Vancouver, BC, 2011, pp. 573--580 Google ScholarDigital Library
- Grove. Finger-clip heart rate sensor with shell. Retrieved July 13, 2018 from https://www.seeedstudio.com/Grove-Finger-clip-Heart-Rate-Sensor-with-shell-p-2420.htmlGoogle Scholar
- Grove. GSR sensor. Retrieved July 13, 2018 from https://www.seeedstudio.com/Grove-GSR-sensor-p-1614.htmlGoogle Scholar
- Logitech. G-Force G920 Steering Wheel. Retrieved July 13, 2018 from https://www.logitechg.com/ensg/drivingGoogle Scholar
- Ministerio del Interior de España. Otros factores de riesgo: El estrés. 2014. Retrieved July 13, 2018 from http://www.dgt.es/PEVI/documentos/catalogo_recursos/didacticos/did_adultas/estres.pdfGoogle Scholar
- OpenDS. Retrieved November 11, 2017 from https://www.opends.eu/software/featuresGoogle Scholar
- S. Ruggieri. Efficient C4.5 {classification algorithm}. In IEEE Transactions on Knowledge and Data Engineering, 14, 2, pp. 438--444, Mar/Apr 2002. Google ScholarDigital Library
- S. Sathyadevan and R.R. Nair. Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest. In Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi, 2015Google Scholar
- Sparkfun. LilyPad Vibe Board. Retrieved July 13, 2018 from https://www.sparkfun.com/products/11008Google Scholar
- Sparkfun. Load Sensor -- 50 kg. Retrieved July 13, 2018 from https://www.sparkfun.com/products/10245Google Scholar
- Alejandro P. Taddia, et al. Fortaleciendo el sector académico para reducir los siniestros de tránsito en América Latina: Investigaciones y casos de estudio en seguridad vial. 2014. Retrieved July 13, 2018 from https://publications.iadb.org/handle/11319/6476Google Scholar
- University of Waikato. WEKA 3: Data Mining Software in Java. Retrieved July 13, 2018 from https://www.cs.waikato.ac.nz/ml/weka/Google Scholar
- World Health Organization. World report on road traffic injury prevention. 2004. Retrieved August 10, 2018 from http://www.who.int/violence_injury_prevention/publications/road_traffic/world_report/enGoogle Scholar
Index Terms
- Biometric Interface for Driver's Stress Detection and Awareness
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