MOM- Smart Wearable Device for Continuous Monitoring of Fastened Helmet Use

Votes: 11
Views: 530

1.2 million people die every year from traffic accidents. Over 370.000 are motorcycle riders. It is estimated over 150,000 lives could be saved just by promoting a solution that encourages the use of the adequately fastened helmet (worn and firmly fastened), according to World Health Organization (WHO) in its last study.

MOM is a smart wearable device that can detect if the rider is using the helmet and if it is fastened. MOM is a novelty non-invasive embedded electronic system that uses tiny machine learning (Tiny ML), an inertial measurement unit (IMU) sensor, a microphone, and a smartphone app. The array of sensors continuously sends information, with the signals produced by wearing the helmet and the sound made when it is fastened to the microcontroller. Then it recognizes the pattern through machine learning, and transmits the state predicted to the app, finally feedback to the rider with audio, visual, luminous and haptic feedback either the helmet is not worn or it is unfastened. Our device is considered Technology-Readiness-level 4 (laboratory testing of prototype component). We estimate it could be manufactured for under $30 USD.

This technology can be extended to other potential users like bicycles, e-bicycle, and e-scooter riders. The implementation of MOM would encourage the proper use of the helmet saving lives every year all over the world.

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  • ABOUT THE ENTRANT

  • Name:
    Jheyson Villavisan
  • Type of entry:
    team
    Team members:
    PhD. Alba Ávila Bernal
  • Profession:
    Engineer/Designer
  • Number of times previously entering contest:
    2
  • Jheyson is inspired by:
    To integrate and use forefront technologies to improve safety culture on wearing protective garments on the road and in workplaces. Safety culture connects engineering professionals with a vision of sustainability that involves education through technology and solutions that are not unique but keep evolving with the new materials, electronics, and technologies.
  • Software used for this entry:
    Altium, Python, Matlab, Android Studio, Visual Studio Code, Autocad
  • Patent status:
    pending