NeuroShield

NeuroShield is a Smart helmet that blocks ignition until it’s worn, detects crashes in real time, and instantly calls for help—safety before, during, and after every ride.

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Inspiration

The idea for NeuroShield emerged from witnessing the persistently high rates of two-wheeler fatalities despite widespread helmet availability. We realized that simply owning a helmet isn’t enough—riders may skip using it or not receive timely assistance after a crash. By combining helmet-detection with automated crash alerts, we saw an opportunity to prevent accidents and bridge the critical gap between impact and response.

What it does

  • Ignition Interlock: Prevents the scooter from starting unless the helmet is properly worn and fastened.
  • Emergency Alerts: Upon crash detection, automatically dials emergency contacts via an onboard SIM module, sending GPS coordinates for rapid assistance.

How I built it

  1. Sensors & Hardware
  • Helmet-Presence: Capacitive sensor on the chin strap to confirm the helmet is on.
  • Crash and fall detection. Uses a gyroscope and tilt sensor to detect falls and calls the police
  • Sim module. Uses SIM800L module to call the police in case of a crash

  • Controller: ESP32 as the central hub, managing sensor inputs and actuation.

    1. Integration Steps

  • Wired all sensors and modules to the ESP32, ensuring clean power distribution.

  • Implemented helmet-presence logic to interlock the scooter’s relay-controlled ignition circuit.

  • Tuned the accelerometer thresholds to reliably distinguish between normal movement and serious impacts.

  • Implemented SIM module routines for auto-dial and GPS payload formatting.

    1. Testing & Calibration

  • Performed drop tests to validate fall detection without excessive false alarms.

  • Simulated ignition scenarios to confirm lockout reliability under varying conditions.

Challenges I ran into

  • Sensor Calibration: Balancing accelerometer sensitivity to catch true impacts while avoiding false positives from normal riding vibrations.
  • Power Management: Ensuring the SIM800L module and sensors could run from a compact LiPo pack without draining too quickly.
  • Mechanical Integration: Embedding electronics in the helmet without compromising comfort or safety certifications.
  • Reliable Communication: Handling SIM network drop-outs and ensuring emergency calls go through even in low-signal areas.

Accomplishments that I'm proud of

  • Delivered a working prototype that never allowed ignition without a worn helmet.
  • Won top 10 project award at CODE PI 2025
  • Won 1st place at ByteBash
  • Achieved 95% accuracy in fall detection across multiple test scenarios.
  • Demonstrated successful emergency call placement with live GPS coordinates in field tests.
  • Created a compact PCB and enclosure that fit snugly within a standard helmet shell.

What I learned

  • The critical importance of real-world testing for safety-critical IoT devices.
  • Techniques for low-power optimization on resource-constrained microcontrollers.
  • Best practices for modular firmware design to accommodate future feature additions.
  • How to navigate hardware-software trade-offs when embedding electronics into PPE.

What’s next for NeuroShield

  • Voice & App Integration: Add voice-command controls and a companion mobile app for ride history and settings.
  • Machine-Learning Crash Analysis: Use AI to better differentiate between falls and rough road bumps.
  • Manufacturing Prototype: Refine the design for injection molding and mass production, focusing on cost reduction.
  • Global Adoption: Adapt emergency-call workflows for different countries and integrate with local first-responder networks.
  • Sustainable Materials: Explore eco-friendly helmet composites and energy-harvesting options (solar/bike-dynamo).

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