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Bruxism Detector

This small device + software suite helps you monitor jaw muscle activity and provide biofeedback to signal the user, while trying to track down its triggers.

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Call to action. Bruxism Detector Application is at risk!

Starting September 2026, Google will require a centralized registration in order to develop apps.
You will no longer be able to install the Bruxism Detector app via this website.
Find more information on the issue and what you can do about it at keepandroidopen.org


IMPORTANT

This is not a medical device. For educational/research purposes only. Not intended to diagnose or treat any condition. I am distributing this guide and software to you freely and with no liability whatsoever.
Responsibility about the device and its correct usage is entirely yours.

The detection system output needs to be validated.
Please contact me if you have proper equipment to do so.

SAFETY NOTICE

To reduce risk of electrocution, NEVER connect electrodes to your body when the circuit is connected to mains power in any way. Through the charger, a laptop, your desktop, etc.

In simple terms, you should only wear electrodes when your circuit is attached to a battery and not to the wall.

No, attaching to your power bank while it's charging from the wall also isn't okay.



A full assembly of the modular device, featuring:
- The core module
- Wall mount
- Phone mount
- Battery module.


Let's keep in touch!

Use discussions for support and feature development

Use issues for problems within the code.



📖 Documentation & Guides

Depending on your technical skills, choose the path that suits you best:

For beginners. Start here. A step-by-step, plug-and-play guide covering:

  • Bill of Materials and 3D Printed enclosures
  • Hardware assembly and electrode placement
  • 1-Click software flashing (No coding required)
  • Android App setup, automatic tuning, and wearables integration
  • Daytime reflex training

For developers, desktop users, and tinkerers. Read the legacy technical documentation for:

  • Manual SVM model training via Python
  • Desktop logging and graph generation via Processing (Java)
  • Manual code compilation via Arduino IDE
  • Customizing melodies and detection thresholds via C++ headers

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