Toothbrush Position Tracking With Sensor Fusion Under Noisy Oral Imaging
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Solution Overview
Problem
Existing toothbrush technologies face challenges in accurately tracking the location and motion within the oral cavity, and image and video capture is hindered by toothpaste foam, saliva, and lens fogging, leading to noisy data sets that are cumbersome for users and dental professionals to analyze.
Innovation Solution
An oral care system utilizing deep machine learning neural networks to analyze sensor data from motion, orientation, and image sensors to determine the location and orientation of the toothbrush within the mouth, control brushing routines, provide feedback, and assess cleaning element wear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep machine learning neural networks are used to analyze sensor data and determine toothbrush location and orientation, then tracking accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides the complex tracking problem into separate functional modules: motion sensors capture raw movement data, orientation sensors capture angular data, and image sensors capture visual feedback. Each sensor type processes its own data stream independently before integration, reducing the complexity burden on any single component while maintaining high tracking accuracy through coordinated analysis.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw sensor data into meaningful tracking information. This intermediary layer includes algorithms that convert accelerometer and gyroscope readings into standardized orientation angles, and that fuse multiple sensor inputs into coherent location estimates. This mediation simplifies the overall system architecture by creating clear data flow stages.
2Adaptability or versatility
If image sensors are used to capture oral cavity data, then oral health analysis capability is improved, but data quality deteriorates due to foam, saliva, and fogging
Solution Approach 1:
The system merges data from multiple sensor types including motion sensors, orientation sensors, and image sensors to compensate for the limitations of individual sensors. By combining data streams and using sensor fusion algorithms, the system maintains reliable oral health analysis capability even when image quality is degraded by foam, saliva, or condensation on the lens.
Solution Approach 2:
The system incorporates feedback mechanisms where processed image data and sensor data are continuously analyzed to adjust capture parameters in real-time. When the system detects poor image quality conditions such as foam coverage or lens fogging, it can adjust capture frequency, trigger additional captures when conditions improve, or rely more heavily on sensor-based tracking data to maintain analysis reliability.
3Manufacturing precision
If comprehensive sensor data collection is implemented, then oral care effectiveness is improved, but data handling complexity increases
Solution Approach 1:
The system extracts and processes only the most relevant features from comprehensive sensor data collections. Instead of analyzing all raw data points, the system identifies and focuses on key parameters such as brushing zone coverage, pressure application patterns, and orientation consistency. This selective extraction maintains high oral care effectiveness while reducing the computational burden of data handling.
Solution Approach 2:
The system performs preliminary data processing and filtering during the data collection phase itself. Raw sensor data undergoes initial validation, noise filtering, and relevance assessment before being stored or transmitted for further analysis. This preliminary action reduces the volume and complexity of data that requires subsequent processing, while preserving all information necessary for effective oral care assessment.
Data Source
AI summary
In one embodiment, an oral care system is disclosed that includes an oral care device and at least one programmable processor. The oral care device includes a head having a reference face, a light source, at least one orientation sensor generating orientation data corresponding to orientation measurements of the reference face during a freeform oral care routine, and at least one optical sensor generating optical sensor data representing optical feedback resulting from the light from the light source being incident upon sections of the oral cavity. The processor, based on the orientation data, determines which optical sensor data corresponds with which of the sections of the oral cavity. For each section of the oral cavity, based on the corresponding optical sensor data, the processor determines an oral care characteristic.