Accident prevention and safety assistance using IOT and machine learning

https://www.autocarpro.in/news-national/road-accidents-in-india-claimed-415-lives-injured-1-286-people-each-day-in-2018-44694

Lee BG et al (2015) Wristband-type driver vigilance monitoring system using smartwatch. IEEE Sens J 15(10):5624–5633

https://towardsdatascience.com/the-intuition-behind-facial-detection-the-viola-jones-Algorithm-29d9106b6999

Hossain MY (2018) IoT based real-time drowsy driving detection system for the prevention of road accidents. ICIIBMS. https://doi.org/10.1109/iciibms.2018.8550026

Dada EG et al (2017) Alcohol detection of drunk drivers with automatic car engine locking system. Nova J Eng Appl Sci 6(1):1–15

Gopinath V (2017) Avoiding drunken driving road accidents by using alcoholic sensors. In: IJEAS, vol 4, Issue 5

Teyeb I et al (2016) Towards a smart car seat design for drowsiness detection based on pressure distribution of the driver’s body. In: ICSEA 2016: the eleventh international conference on software engineering advances

Reyes-Muñoz Angelica et al (2016) Integration of body sensor networks and vehicular ad-hoc networks for traffic safety. Sensors 16:107

Archa A et al (2019) IoT based driver drowsiness detection and traffic collision avoidance system using raspberry pi. In: International journal of engineering sciences & research technology NACETEC’ 19 ISSN: 2277-9655

Sharma S (2018) IoT based car accident detection and notification algorithm for general road accidents. In: IJECE, vol 9(5)

Vural E et al (2007) Machine learning systems for detecting driver drowsiness. In: Proceedings, digital signal processing for in-vehicle and mobile systems, Istanbul, Turkey

Jacobé C et al (2019) Detection and prediction of driver drowsiness using artificial neural network models. Accid Anal Prev 126:95–104

Gupta H (2019) An IoT based air pollution monitoring system. In: ICSETS, vol 7

Gromera M et al (2019) ECG sensor for detection of driver’s drowsiness. In: 23rd International conference on knowledge-based and intelligent information & engineering systems, Elsevier B.V. https://doi.org/10.1016/j.procs.2019.09.366

Anitha C et al (2016) A two fold expert system for yawning detection. In: 2nd International conference on intelligent computing, communication & convergence (ICCC-2016), Elsevier. https://doi.org/10.1016/j.procs.2016.07.324

Alvi U et al (2020) A comprehensive study on IoT based accident detection systems for smart vehicles, vol 8, IEEE Access. https://doi.org/10.1109/access.2020.3006887

Bhatti F et al (2019) A novel internet of things-enabled accident detection and reporting system for smart city environments. Sensors 19:2071. https://doi.org/10.3390/s19092071

Dashora Chirag et al (2019) IoT based framework for the detection of vehicle accident, Cluster Computing. Springer Nature, Berlin

Nanda S et al (2018) An IOT based smart system for accident prevention and detection. In: IEEE

Kumar KN et al (2019) Automatic accident rescue system using IoT. In: Springer Nature 2019, International conference on computer networks and communication technologies. https://doi.org/10.1007/978-981-10-8681-6_52

Zantalis F et al (2019) A review of machine learning and IoT in smart transportation. Future Internet 11:94. https://doi.org/10.3390/fi11040094

Coronato A, Paragliola G (2017) A structured approach for the designing of safe AAL applications. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2017.04.058

Yea J et al (2011) Situation identification techniques in pervasive computing: a review. Pervas Mob Comput. https://doi.org/10.1016/j.pmcj.2011.01.004

Coronato A et al (2014) A situation-aware system for the detection of motion disorders of patients with Autism Spectrum Disorders. Expert Syst Appl Elsevier. https://doi.org/10.1016/j.eswa.2014.05.011

Duttagupta S et al (2016) Performance prediction of IoT application: an experimental analysis. In: IoT’16: Proceedings of the 6th international conference on the internet of things, pp 43–51. https://doi.org/10.1145/2991561.2991572

Banda G et al (2016) One IoT: an IoT protocol and framework for OEMs to make IoT-enabled devices forward compatible. J Reliable Intell Environ 2:131–144. https://doi.org/10.1007/s40860-016-0027-5

Kumar SAP et al (2018) A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance. J Reliab Intell Environ 4:199–209

Bacciu D et al (2017) On the need of machine learning as a service for the internet of things. In: IML ‘17: proceedings of the 1st international conference on internet of things and machine learning, vol 22, pp 1–8. https://doi.org/10.1145/3109761.3109783