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Synapse
February 5, 20260 citations

IoT based Smart Helmet with Motorbike Unit for Rider’s Safety

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SRShobha Rani B RKCKeerthi K CMMMs. Divya M

Key Points

  • The aim is to enhance rider safety by integrating sensors in a smart helmet to detect alcohol use and alert emergency contacts in accidents.
  • Developed a smart helmet integrating various sensors for alcohol detection, helmet status, and rain detection.
  • Designed algorithms to correlate helmet usage with motorbike ignition.
  • Implemented real-time alert systems for emergencies.
  • Achieved an accuracy of 96% in detecting rider's alcohol consumption status.
  • Precision and recall rates were recorded at 96% and 97%, respectively, highlighting model effectiveness.
  • Successful integration of rain detection to enhance rider visibility.

Abstract

Studies shows that 87% of road accidents lead to major brain injuries when riders ride the bikes without helmets. When standard helmets are made intelligent by providing an enhanced safety to the rider, giving alerts on emergency contacts when accidents occur could reduce the accidents by 74%. Smart helmets not only alert during accidents but also helps in construction sites, mining areas supporting industrial workers and medical fields by providing real-time information saving peoples lives. In this paper, a similar smart helmet is developed by integrating various sensors to identify if the person has consumed alcohol, if the rider does not wear helmet and corelate it with bikes ignition ON or OFF. These key functionalities are achieved by employing suitable sensors in the helmet thereby alerting the rider or emergency contacts linked to alert about accidents occurred and current condition of the rider. In addition to this, another feature with helmet is to detect rain and act upon wiping the helmet visor making the riders visibility for safe driving. Rigorous testing was done to analyse the models performance achieving an accuracy of 96% with precision of 96%, recall of 97% and f1-score of 96.4% ensuring riders safety by detecting if rider is alcoholic or non-alcoholic and integrating it with ignition status.

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Cite This Study

R et al. (2025) studied this question.

synapsesocial.com/papers/6984345ff1d9ada3c1fb275fhttps://doi.org/10.1051/itmconf/20257901010/pdf
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