An adaptive oral modeling toothbrush system

Abstract

This invention relates to the field of intelligent oral care technology. Developing good brushing habits is of great significance to promoting the construction of a healthy China; however, many people currently struggle to master proper brushing techniques, easily leading to oral health problems. Therefore, this invention discloses an intelligent toothbrush system based on effective trajectory filtering and adaptive topology modeling, including a toothbrush body, a posture sensing module, a pressure sensing module, a main control module, a storage module, a prompting module, and a communication module. The main control module filters, calculates, and normalizes the posture data to establish an oral reference system trajectory, and eliminates invalid movements through pressure thresholds. It constructs an adaptive oral topology model based on partition templates and historical trajectory clustering, quantitatively calculates cleaning coverage from multiple dimensions, identifies missed brushing, repeated brushing, and excessive pressure, and provides real-time guidance. This invention requires no image acquisition equipment, offers good privacy, is low-cost, and provides accurate assessments, helping users to standardize brushing behavior and improve oral health.

Description

Technical Field

[0001] This invention relates to the field of intelligent oral care technology, specifically to an intelligent toothbrush system based on effective trajectory selection and adaptive topology modeling. Background Technology

[0002] Oral health is a crucial foundation for overall health, and developing scientific and standardized brushing habits is of great significance for promoting the Healthy China initiative and improving the health level of the entire population. However, currently, many Chinese residents have problems such as improper brushing methods and difficulty in mastering the correct brushing techniques and pressure control. Most people cannot achieve comprehensive, even, and effective oral cleaning, which easily leads to various oral diseases such as tooth decay and periodontal disease, affecting their overall health.

[0003] Most existing smart toothbrushes only have basic functions such as timer reminders and simple zone timing, and cannot accurately identify the user's actual brushing area and the degree of cleaning coverage. At the same time, some high-end smart toothbrushes rely on cameras or image acquisition devices to achieve oral cavity area recognition, which not only increases hardware costs but also poses a risk of leakage of user oral privacy.

[0004] Furthermore, existing technologies typically cannot distinguish between effective brushing motions and ineffective movements such as air movement or idle oscillation, leading to significant errors in area coverage determination. The lack of a unified coordinate mapping mechanism between toothbrush posture data and fixed areas of the oral cavity results in low modeling accuracy. Therefore, a smart toothbrush system is needed that requires no visual data acquisition, can accurately identify effective brushing trajectories, and achieve adaptive oral cavity modeling. This system aims to guide users to develop standardized brushing behaviors, contributing to public oral health and the construction of a healthy China. Summary of the Invention

[0005] I. Purpose of the Invention The purpose of this invention is to provide an intelligent toothbrush system based on effective trajectory screening and adaptive topology modeling, so as to solve the problems of inaccurate missed brush identification, invalid trajectory interference, poor privacy, and insufficient model adaptation capability in the prior art.

[0006] II. Technical Solution To achieve the above objectives, the present invention adopts the following technical solution: A smart toothbrush system based on effective trajectory selection and adaptive topology modeling includes a toothbrush body, an attitude sensing module, a pressure sensing module, a main control module, a storage module, a prompting module, and a communication module.

[0007] The attitude sensing module is used to collect acceleration and angular velocity data in real time during the toothbrush's movement; the pressure sensing module is used to collect pressure data when the brush head contacts the teeth in real time.

[0008] The main control module first filters and denoises the original posture data and completes the three-dimensional pose calculation of the toothbrush; then it performs coordinate normalization processing through the tooth row space reference system, converts the local coordinate system of the toothbrush into the fixed coordinate system of the oral cavity, and generates the brushing motion trajectory.

[0009] The system combines pressure data to set an effective pressure threshold, and only the trajectory data corresponding to the pressure exceeding the preset threshold value is judged as a valid brushing action, while invalid motion trajectories such as aerial movement and idle swinging are eliminated.

[0010] The main control module constructs an oral cavity topology coverage model based on a preset oral cavity partition template and the clustering results of the user's historical brushing trajectory, and divides the oral cavity into multiple target cleaning areas.

[0011] Formula for calculating regional coverage: C_i = \alpha T_i^{\text{norm}} + \beta D_i^{\text{norm}} + \gamma P_i^{\text{norm}} + \delta M_i^{\text{norm}} in: - C_i represents the coverage rate of the i-th region; - T_i^{\text{norm}} is the normalized value of the effective stay time in the region; - D_i^{\text{norm}} is the normalized value of the region trajectory density; - P_i^{\text{norm}} is the normalized value of the regional average contact pressure; - M_i^{\text{norm}} is the normalized value of the brushing motion direction matching degree; - \alpha, \beta, \gamma, \delta are weighting coefficients that satisfy \alpha+\beta+\gamma+\delta=1.

[0012] Note: The original indices T_i, D_i, P_i, M_i need to be mapped to the [0,1] interval using the Min-Max normalization method before being substituted into the formula for calculation.

[0013] Spatial coordinate transformation model: P_o = R P_l + T in: - P_l represents the coordinates in the local coordinate system of the toothbrush; - R is the attitude rotation matrix; - T is the translation vector; - P_o represents the coordinates in the oral cavity reference coordinate system.

[0014] When a missed brushing area, a repeatedly brushed high-frequency area, or an area with excessive pressure is detected, the main control module drives the prompt module to output a prompt to brush again, a prompt to switch areas, or a prompt to reduce pressure. Beneficial effects

[0015] Compared with the prior art, the present invention has the following beneficial effects: No camera or image acquisition equipment is required, reducing hardware costs and avoiding the risk of privacy leaks; Effective brushing actions are screened based on pressure thresholds to improve the accuracy of area coverage determination; Precise mapping between toothbrush trajectory and oral cavity area is achieved through coordinate normalization; Adaptive optimization of oral topology model based on historical trajectory clustering; Multi-dimensional parameters are used to jointly evaluate cleaning effectiveness and improve the accuracy of missed brush identification; It has pressure overload protection capabilities, reducing the risk of gum damage; It supports data interaction with external terminals, facilitating data visualization and analysis. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the hardware structure of the system of the present invention; Figure 2 This is a schematic diagram of the initial autonomous modeling process of the present invention; Figure 3 This is a schematic diagram of the real-time data quantization matching and adaptive guidance process of the present invention; Figure 4 This is a schematic diagram of the dynamic calibration and iterative update process of the model in this invention; Figure 5 This is a schematic diagram of the coordinate normalization mapping of the present invention. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings, so as to facilitate understanding and implementation by those skilled in the art. The embodiments are only used to explain the present invention and are not intended to limit the scope of protection of the present invention.

[0018] The intelligent toothbrush system disclosed in this invention consists of a toothbrush body, an attitude sensing module, a pressure sensing module, a main control module, a storage module, a prompting module, and a communication module. The attitude sensing module uses a combination of a three-axis accelerometer and a three-axis gyroscope to collect real-time three-dimensional attitude and motion data of the toothbrush; the pressure sensing module is located at the brush head or brush neck to collect real-time brushing contact pressure signals. All sensor data is uploaded to the main control module in real-time for unified processing.

[0019] The main control module first performs filtering and noise reduction processing on the raw posture sensing data to remove jitter noise and invalid high-frequency interference signals. Then, it obtains the real-time three-dimensional pose sequence of the toothbrush in space through posture calculation. Based on the preset oral cavity tooth row space reference coordinate system, the local coordinate system coordinates of the toothbrush are uniformly transformed to the fixed oral cavity reference coordinate system to complete the coordinate normalization and obtain a stable and comparable brushing motion trajectory.

[0020] This invention sets a fixed effective pressure threshold, retaining only brushing time data with pressure greater than the threshold as valid trajectories, and automatically discarding invalid trajectory data such as toothbrush movement in the air, idle swinging, and position adjustment that do not produce a cleaning effect, thus greatly improving the accuracy of area recognition.

[0021] Based on a preset oral cavity partition template, the main control module combines the user's initial sampling trajectory with the clustering features of long-term historical brushing trajectories to adaptively generate a personalized oral cavity topology coverage model that fits the user's oral cavity structure and brushing habits, dividing the oral cavity into multiple independent cleaning areas: maxillary, mandibular, left side, right side, and anterior teeth.

[0022] This invention employs a multi-parameter fusion method to calculate the coverage rate of each area. It quantifies the true cleanliness of each area by weighted summing of normalized effective dwell time, trajectory density, contact pressure, and motion direction matching degree. The system determines whether an area exhibits abnormal conditions such as missed brushing, insufficient cleaning, repeated brushing, or excessive pressure based on the threshold values ​​of each parameter.

[0023] When a corresponding abnormal state is detected, the main control module drives the prompting module to output prompts for brushing again, changing brushing zones, or reducing pressure through lights, vibration, or voice, respectively, to guide the user to brush their teeth correctly in real time. At the same time, the system can dynamically adjust the model weights and evaluation parameters based on historical cleaning data, the probability of missed areas, and brush head wear characteristics, to achieve long-term adaptive iterative optimization of the model.

[0024] This invention does not rely on cameras or any image acquisition devices throughout the entire process. It only relies on sensor data for modeling and recognition, which significantly reduces equipment costs while ensuring recognition accuracy and completely avoids the risk of oral image privacy leakage. It is suitable for daily intelligent oral cleaning guidance for all types of people.

Claims

1. A smart toothbrush system based on oral cavity topology adaptive modeling, characterized in that, include: Toothbrush body; The posture sensing module is used to collect brushing posture data of the toothbrush body during use; The pressure sensing module is used to collect pressure data when the bristles come into contact with the teeth; The main control module is electrically connected to the attitude sensing module and the pressure sensing module respectively; The storage module is used to store predefined oral cavity area models and users' historical brushing data; The prompt module is used to output brushing guidance information; The main control module is configured to perform the following operations: (a) Construct the motion trajectory of the toothbrush bristles in the oral cavity based on the brushing posture data; (b) Determine whether the pressure data exceeds a preset pressure threshold and continues for a preset time. If so, it is identified as a valid brushing action. (c) Based on the motion trajectory and the effective brushing action, construct an oral cavity topology coverage model, wherein the oral cavity topology coverage model is a three-dimensional mesh model that characterizes the trajectory density of the brush bristles in each spatial region of the oral cavity and the distribution of the effective brushing action; (d) Divide the oral cavity into multiple target cleaning areas according to the oral cavity topology coverage model, and calculate the cleaning coverage rate of each area based on the trajectory dwell time, trajectory density and contact pressure of effective brushing action in each target cleaning area; (e) Compare the cleaning coverage rate of each area with a preset coverage threshold. When the cleaning coverage rate of any area is lower than the preset coverage threshold, generate a guidance prompt signal and output it to the prompt module. (f) Based on the distribution characteristics of cleaning coverage in each region of the user's historical brushing data, adaptively and dynamically correct the region division boundary of the oral cavity topology coverage model or the weight parameters in the cleaning coverage calculation.

2. The intelligent toothbrush system based on oral topology adaptive modeling according to claim 1, characterized in that, The attitude sensing module includes a three-axis accelerometer and a three-axis gyroscope.

3. The intelligent toothbrush system based on oral topology adaptive modeling according to claim 1, characterized in that, The pressure sensing module is located at the brush head or brush neck of the toothbrush body.

4. The intelligent toothbrush system based on oral topology adaptive modeling according to claim 1, characterized in that, The main control module processes the brushing posture data using a time series analysis algorithm to identify different oral cleaning areas.

5. The intelligent toothbrush system based on oral topology adaptive modeling according to claim 1, characterized in that, The oral cavity topology coverage model adopts a region division method, dividing the oral cavity into the maxillary region, mandibular region, left side region, right side region, and anterior tooth region; the adaptive dynamic correction includes dynamically shrinking or expanding the boundaries of each region based on the user's brushing habits.

6. The intelligent toothbrush system based on oral topology adaptive modeling according to claim 1, characterized in that, The prompting module includes a vibration prompting unit, an audio prompting unit, and / or an audio prompting unit.

7. The intelligent toothbrush system based on oral topology adaptive modeling according to claim 1, characterized in that, It also includes a communication module, used to establish data connections and interact with external terminal devices.

8. The intelligent toothbrush system based on oral topology adaptive modeling according to claim 7, characterized in that, The communication module is a Bluetooth communication module.

9. The intelligent toothbrush system based on oral topology adaptive modeling according to claim 1, characterized in that, When constructing the oral cavity topology coverage model, the main control module uses the Kalman filter algorithm to denoise the brushing posture data.

10. A smart brushing guidance method based on oral topology adaptive modeling, applied to the smart toothbrush system as described in any one of claims 1 to 9, characterized in that, Includes the following steps: (a) Collect brushing posture data and brushing pressure data of the toothbrush body; (b) Construct a toothbrush motion trajectory based on the brushing posture data, and identify effective brushing actions based on the brushing pressure data; (c) Construct an oral cavity topology coverage model based on the toothbrush motion trajectory and the effective brushing action; (d) Divide the oral cavity topology coverage model into multiple target cleaning areas and calculate the cleaning coverage rate of each area, wherein the cleaning coverage rate is determined based on the trajectory dwell time, trajectory density and contact pressure; (e) Compare the cleaning coverage rate of each area with a preset threshold. If it is lower than the threshold, output a guidance prompt. (f) Based on the regional coverage distribution characteristics in the user's historical brushing data, adaptively and dynamically correct the regional division or cleaning coverage calculation parameters of the oral cavity topology coverage model.

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