Abstract
When it comes to ride a motorcycle the drivers-centered road safety is quintessential; every year a remarkable number of accidents directly related to sleepiness and fatigue occur. With the objective of maximizing the security on a motorcycle, the reported system aims to prevent sleepiness related accidents and to attenuate the effects of a crash. The system was developed as the less intrusive as it could be, with sensors that allow the capture of reaction times to stimuli-response and collect acceleration values. To obviate the lack of data related to sleepiness during motorcycle riding, a machine learning system was developed, based on Artificial Immune Systems. This way, resourcing to a minimum amount of user input, a custom system is synthesized for each user, allowing to assess the sleepiness level of each subject differently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Facts and Stats - Drowsy Driving - Stay Alert, Arrive Alive https://drowsydriving.org/about/facts-and-stats/
Drowsy Driving—National Highway Traffic Safety Administration https://www.nhtsa.gov/risky-driving/drowsy-driving
Autoridade Nacional de Segurança Rodoviária - Relatórios de Sinistralidade http://www.ansr.pt/Estatisticas/RelatoriosDeSinistralidade/Pages/default.aspx
CrossHelmet X1 features: HUD, Bluetooth & much more https://www.crosshelmet.com/features/index.html
Argon Transform—Smart Augmented Reality Riding https://www.argontransform.com/
REALRIDER - The Motorcycle App - Safety - Routes http://www.realrider.com/
Lee, B.G., Chung, W.Y.: A smartphone-based driver safety monitoring system using data fusion. Sensors (Basel) 12(12), 17536–17552 (2012). https://doi.org/10.3390/s121217536
Foong, R., Ang, K.K., Quek, C., Guan, C., Wai, A.A.P.: An analysis on driver drowsiness based on reaction time and EEG band power. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015, pp. 7982–7985 (2015). https://doi.org/10.1109/EMBC.2015.7320244
Sahayadhas, A, Sundaraj, K, Murugappan, M.: Detecting driver drowsiness based on sensors: a review. Sensors (Basel) 12(12), 16937–16953 (2012). https://doi.org/10.3390/s121216937
Sun, Y., Yu, X.B.: An innovative nonintrusive driver assistance system for vital signal monitoring. IEEE J. Biomed. Health Inform. 18(6), 1932–1939 (2014). https://doi.org/10.1109/JBHI.2014.2305403
Penzel, T., Fietze, I., Schöbel, C., Veauthier, C.: Technology to detect driver sleepiness. Sleep Med. Clin. 14(4), 463–468 (2019). https://doi.org/10.1016/j.jsmc.2019.08.004
Mahajan, K., Velaga, N.R.: Effects of partial sleep deprivation on braking response of drivers in hazard scenarios. Accid. Anal. Prev. 142, 105545 (2020). https://doi.org/10.1016/j.aap.2020.105545
Choudhary, A.K., Kishanrao, S.S., Dadarao Dhanvijay, A.K., Alam, T.: Sleep restriction may lead to disruption in physiological attention and reaction time. Sleep Sci. 9(3), 207–211 (2016). https://doi.org/10.1016/j.slsci.2016.09.001
Lisper, H.O., Kjellberg, A.: Effects of 24-hour sleep deprivation on rate of decrement in a 10-minute auditory reaction time task. J. Exp. Psychol. 96(2), 287–290 (1972). https://doi.org/10.1037/h0033615
Sahayadhas, A., Sundaraj, K., Murugappan, M.: Drowsiness detection during different times of day using multiple features. Australas. Phys. Eng. Sci. Med. 36, 243–250 (2013). https://doi.org/10.1007/s13246-013-0200-6. supported by the Australasian College of Physical Scientists in Medicine and the Australasian Association of Physical Sciences in Medicine
Fernandes, D.A., Freire, M.M., Fazendeiro, P.A., Inácio, P.R.: Applications of artificial immune systems to computer security: a survey. J. Inf. Secur. Appl. 35, 138–159 (2017). http://hdl.handle.net/10400.6/8203, https://doi.org/10.1016/j.jisa.2017.06.007
Adafruit LIS3DH Triple-Axis Accelerometer Breakout https://learn.adafruit.com/adafruit-lis3dh-triple-axis-accelerometer-breakout/overview
SciPy—SciPy v1.5.2 Reference Guide https://docs.scipy.org/doc/scipy/reference/index.html
Baulk, S.D., Reyner, L.A., Horne, J.A.: Driver sleepiness—evaluation of reaction time measurement as a secondary task. Sleep 24(6), 695–698 (2001)
Acknowledgments
This work was supported by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competências em Cloud Computing, co-financed by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica - Programas Integrados de IC&DT. This work was also funded by FCT/MCTES through the project UIDB/50008/2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sales, D., Prata, P., Fazendeiro, P. (2021). Smart Helmet: An Experimental Helmet Security Add-On. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_86
Download citation
DOI: https://doi.org/10.1007/978-3-030-80126-7_86
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80125-0
Online ISBN: 978-3-030-80126-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)