Electric toothbrush monitors brushing habits using motor signals
FreeBrush: Motor Signals-Based Toothbrushing Monitoring System for Electric Toothbrushes
Human-Computer Interaction
Summary
Many people don’t brush their teeth evenly, spending too little or too much time on some spots. The authors found that the electric toothbrush’s own motor signals change when it brushes different parts of the mouth. They built a system called FreeBrush that can tell which tooth surface is being brushed and for how long, just by looking at these motor signals. This helps users know if they are missing spots or brushing too much, without needing extra sensors.
What this means in practice
- •For electric toothbrush manufacturers: Integrate motor-signal analysis to track brushing surfaces and durations without adding external sensors.$Commercial implications: Enables sensorless brushing monitoring features that can be incorporated into next-generation toothbrushes for personalized oral care.
- •For mobile health app developers: Use intrinsic brush motor data to provide real-time feedback on user brushing technique and coverage.
Authors
Pinhao Lin, Xuanyu Liu, Guoheng Ma, Haoxian Liu, Zongqi Yang, Jin Zhang
Abstract
Oral health is vital for overall well-being, yet many users brush different tooth surfaces for insufficient or excessive durations. Toothbrushing monitoring systems can help improve brushing habits by reminding users of under-brushed and over-brushed surfaces, but existing approaches often rely on external sensors, increasing cost and reducing practicality. We observe that the intrinsic motor signals of electric toothbrushes can reflect the brushing surfaces and durations. When brushing different tooth surfaces, the brush head experiences different constraints from the surrounding oral cavity, changing the motor's driving currents and position feedback. Based on this observation, we propose FreeBrush, a toothbrushing monitoring system that uses the intrinsic motor signals of electric toothbrushes to recognize brushed tooth surfaces and estimate brushing durations without any external sensors. We prototype the system on a commercial off-the-shelf electric toothbrush and conduct a comprehensive evaluation with 18 participants. FreeBrush achieves 92.8% accuracy for 16-surface recognition and 92.0% accuracy for brushing duration estimation. The system further shows robustness across different toothpastes, brush heads, vibration modes, usage scenarios, and long-term use, and is capable of running on edge devices. By recognizing brushed tooth surfaces and estimating the brushing duration, FreeBrush can help users identify under-brushed and over-brushed surfaces and build balanced brushing habits.