Related Experiment Video
Updated: Mar 11, 2026
Temporomandibular Joint Pain Measurement by Bite Force and Von Frey Filament Assays in Mice
Published on: September 13, 2024
A hydrogel-based triboelectric noncontact-ionic pressure dual-function sensing system for measuring mandibular
Peng Wang1, Zhiheng Li2, Yujun Zhang3
1Shandong Provincial Key Laboratory of Sensor Technology and High Precision Weighing Instruments, School of Mechanical Engineering, University of Jinan, Jinan 250022, China; Department of Materials Science and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai 200433, China..
This study introduces a flexible sensor system that measures jaw movement and bite force during chewing. This technology accurately reconstructs chewing patterns, aiding in oral disease diagnosis.
Area of Science:
- Biomedical Engineering
- Materials Science
- Dental Technology
Background:
- Accurate measurement of mandibular dynamics and bite force is crucial for diagnosing and treating oral diseases.
- Existing systems often lack the flexibility or simultaneous measurement capabilities required for comprehensive occlusal analysis.
Purpose of the Study:
- To develop a flexible sensing system for simultaneous, non-contact measurement of mandibular movement trajectories and contact-based bite force.
- To integrate machine learning for high-accuracy dental health recognition based on occlusal data.
Main Methods:
- A dual-mode sensing system combining a triboelectric module (for movement) and an ionic supercapacitor module (for bite force).
- Utilizing machine learning algorithms to analyze captured occlusal trajectory data.
- Testing sensor stability, sensitivity, response time, and signal drift over extended cycles.
Main Results:
- The triboelectric unit demonstrated stable capture of dynamic occlusion trajectories with minimal signal attenuation (<5% after 10,000s).
- The ionic unit showed high sensitivity (4.97 kPa⁻¹) and rapid response (1s) for bite force measurement (10-40 kPa), with low drift (<10% after 1000 cycles).
- A dental health recognition model achieved 99.25% accuracy in identifying occlusal trajectories.
Conclusions:
- The developed flexible sensing system accurately captures mandibular movement and bite force during mastication.
- This system enables full reconstruction of occlusal relationships, providing valuable data for oral disease diagnosis and treatment.
- The integration of machine learning enhances the diagnostic potential of the captured biomechanical data.
