EMG Mouthguard Bite-Force TMJ Modeling Science Fair

ISEF Category: Biomedical Engineering

Subcategory: Biomechanics  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

Chewing gum sounds harmless until you realize each chew loads the jaw joint hundreds of times an hour. People with TMJ disorders feel it. A 3D-printed mouthguard with a surface EMG sensor on the masseter lets you map exactly how stiffness drives muscle activation. A Hill-type muscle model then turns that data into a TMJ overload score.

What Is It?

EMG (electromyography) reads electrical activity from muscle. Surface electrodes on the cheek pick up signals from the masseter, the main chewing muscle. The MyoWare board makes this safe and cheap.

A mouthguard housing the electrodes and a small accelerometer can be 3D-printed in dental resin. With consistent placement, EMG signals become comparable trial to trial.

A Hill-type model relates muscle activation to force, velocity, and length. Fitting it to chewing data for gums of different stiffness yields parameters you can compare to published jaw-muscle properties. TMJ overload is identified by sustained force above a published threshold.

Why This Is a Good Topic

Oral-biomechanics work is rare at ISEF and easy to control. Hardware is inexpensive and the variable is everyday. You will learn EMG processing, model fitting, and subject-protocol design.

Research Questions

• How does gum stiffness change peak masseter EMG amplitude?
• What is the effect of chewing rate on cumulative TMJ load?
• Does the Hill-type fit predict EMG during held-stiffness trials?
• To what extent does jaw asymmetry shift load distribution?
• Which gum stiffness crosses the published TMJ threshold?
• How does fatigue change activation patterns?
• What is the effect of mouthguard fit on signal quality?

Basic Materials

• MyoWare EMG sensor and electrodes.
• Microcontroller (ESP32 or Arduino).
• 3D printer and dental-grade resin (or PETG).
• Various gum brands of measured stiffness.
• Smartphone for video sync.
• Informed-consent form.

Advanced Materials

• Clinical EMG amplifier.
• Force-instrumented bite plate.
• Mocap to track jaw motion.
• Clinical mentor.

Software & Tools

• Python (NumPy and SciPy): Processes EMG and fits Hill-type model.
• Arduino IDE: Programs the data logger.
• OpenSim: Cross-checks jaw kinematics.
• OpenSCAD: Designs the mouthguard housing.

Experiment Steps

1. Calibrate EMG against maximal voluntary contraction per subject.
2. Lock electrode placement using a marker template.
3. Decide gum stiffness levels and randomized order.
4. Run baseline rest, calibration, and trial blocks per session.
5. Fit Hill-type parameters on training trials and validate on held-out.
6. Compare load summaries to published TMJ thresholds.

Common Pitfalls

• Re-positioning electrodes between trials and resetting amplitude.
• Letting subjects chew at self-selected rates without metronome.
• Skipping baseline rest, biasing the EMG envelope.
• Ignoring jaw asymmetry by recording only one side.
• Treating raw EMG as muscle force.

What Makes This Competitive

A class-level project just records EMG. A competitive entry calibrates EMG amplitude against percent maximal voluntary contraction, runs randomized gum-stiffness order with multiple subjects, and validates the Hill-type fit by cross-validation. Comparing parameters across age groups adds a clinical hook.

Project Variations

• Compare bite force across day and night to test bruxism patterns.
• Add a vibration biofeedback cue and measure activation reduction.
• Run the protocol across braces vs. no-braces subjects.

Learn More

• PubMed: Search EMG masseter TMJ reviews.
• NIH PubMed Central: Open-access dental biomechanics papers.
• MyoWare documentation: Free wiring guides.
• OpenSim documentation: Free jaw-model tutorials.
• MIT OpenCourseWare: Course 6.555 Biomedical Signal and Image Processing.