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Piezo-Based Auto Emergency Notification

This project develops a real-time crash detection and alert system using piezoelectric sensors to automatically notify emergency contacts with location data.

  • Hardware prototype featuring piezo sensor, GPS, and microcontroller-based control unit.

  • Location data (latitude and longitude) automatically uploaded following simulated vehicle impact

  • Simulation results of device spacing optimization using ANSYS software

  • Check current location via Google Maps

  • Send location, longitude, and time data to Google Spreadsheet

What it does

This system detects vehicle collisions using piezoelectric sensors and automatically sends emergency alerts with GPS data, helping ensure rapid assistance when the driver is unable to respond manually."


Your inspiration

This idea was inspired by the growing number of traffic accidents involving elderly and disabled drivers, especially those with hearing or mobility impairments. Many struggle to request help promptly after an accident due to communication or physical limitations, which can be critical during emergencies when the 'golden time' is missed. To address this, our team developed an automated system that detects vehicle collisions using piezoelectric sensors and instantly sends location-based emergency alerts—ensuring faster response and improved safety for those unable to call for help


How it works

The system uses a piezoelectric sensor attached to the vehicle to detect mechanical impact during a collision. When a significant shock is sensed, the microcontroller analyzes the signal to determine whether an accident has occurred. If confirmed, it immediately activates the GPS module to obtain location data and transmits an emergency alert message to pre-registered contacts (such as guardians or 119) via a wireless communication module. For user feedback, visual alerts (LED) are triggered for hearing-impaired users, while vibration motors provide haptic feedback for those with mobility impairments—ensuring intuitive recognition of the accident even without manual interaction.


Design process

The project began with the concept of enhancing vehicle safety for drivers with disabilities, particularly those with hearing or mobility impairments. Recognizing the critical need for automated emergency support during accidents, we planned a system that could detect collisions and send alerts without requiring manual input. In May, we completed the initial circuit design and built a prototype integrating a piezoelectric sensor, GPS module, and Wi-Fi communication using a microcontroller (Arduino Nano and Wemos D1 Mini). In June, we collected feedback from disabled individuals to improve the user interface, adding visual (LED) and haptic (vibration) feedback tailored to different disability types. Currently, we are integrating all modules into a complete system and conducting real-world testing to verify reliability. Future improvements include refining the shock detection algorithm to reduce false positives and optimizing power efficiency to ensure stable operation in various driving conditions.


How it is different

Unlike existing vehicle safety systems that require manual operation or are designed for the general population, our system offers a fully automated crash detection and emergency alert solution specifically tailored for people with hearing and mobility impairments. It uniquely combines piezoelectric sensing with real-time GPS tracking and wireless communication to operate without any user interaction. What sets our device apart is its dual feedback mechanism: visual alerts (LED) for hearing-impaired users and vibration feedback for those with mobility limitations. These customized responses ensure the system is not only automated but also accessible and inclusive. Furthermore, the system is compact, low-power, and cost-effective—making it suitable for integration into both new and existing vehicles.


Future plans

We plan to improve crash detection accuracy by refining signal processing and reducing false alarms through threshold tuning and pattern recognition. Hardware will be miniaturized for better integration and power efficiency. A mobile app will also be developed for real-time monitoring and alert customization. Finally, we aim to conduct pilot testing with elderly and disabled drivers and pursue commercialization with industry partners.


Awards


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