CN-122025123-A - Intelligent early warning method and system for dental trauma risk

CN122025123ACN 122025123 ACN122025123 ACN 122025123ACN-122025123-A

Abstract

The invention relates to the technical field of dental trauma risk assessment and early warning, in particular to an intelligent early warning method and system for dental trauma risk, comprising the following steps: through gesture calculation, the impact direction vector under the body trunk coordinate system is converted into the head coordinate system, and compared with the maximum fragile direction vector of the individual, the impact risk is quantified. Meanwhile, motion energy distribution is analyzed, and motion irregularity is estimated. Based on historical motion data, injury risk factors are assigned to each cluster by clustering with impact duration and impact rate. And (5) integrating the impact risk, the movement irregularity and the risk factors of the corresponding class clusters to obtain the current injury risk degree. Combining the degree of tooth stiffness calculated from the nutrition and density data, a final dental trauma risk factor is generated. The intelligent early warning system improves intelligent early warning of dental trauma risks.

Inventors

YANG PING
JIA LIENI
LIU RUI
ZHANG MIN
WANG DIE
Lv Shaodi
FAN WENBO

Assignees

中国人民解放军空军军医大学

Dates

Publication Date: 20260512
Application Date: 20260121
Priority Date: 20260116

Claims (10)

1. An intelligent early warning method for dental trauma risk is characterized by comprising the following steps: Acquiring exercise data, tooth density data and daily intake data of nutrients of a user, and preprocessing the data; Obtaining a personal maximum vulnerable direction vector under a head coordinate system by attitude inclination angle data in motion data, obtaining an impact direction vector under the body trunk coordinate system, converting the impact direction vector under the body trunk coordinate system to obtain an impact direction vector under the head coordinate system, obtaining the impact risk degree of a user according to the difference between the impact direction vector under the head coordinate system and the personal maximum vulnerable direction vector, obtaining the irregular degree of a current motion mode according to the distribution of energy data in the motion data, mapping the duration and the impact rate of an impact duration period in a plurality of times of motion in a user history in a motion habit feature space, clustering the plurality of times of motion to obtain a plurality of clusters, obtaining an injury risk factor of each cluster according to the data distribution in the clusters, and obtaining the current injury risk degree of the user through the injury risk factor of each cluster, the irregular degree of the current motion mode and the impact risk degree of the user; Obtaining the hardness degree of the teeth of the user according to the daily average intake of the nutrients and the density of the teeth of the user; different levels of early warning are performed through the current dental trauma risk factors of the user.
2. The intelligent early warning method for dental trauma risk according to claim 1, wherein the step of converting the impact direction vector in the body trunk coordinate system to obtain the impact direction vector in the head coordinate system, and the step of obtaining the impact risk degree of the user according to the difference between the impact direction vector in the head coordinate system and the personal maximum vulnerable direction vector comprises the following steps: The three-dimensional space posture of the body trunk in the inertial coordinate system is calculated in real time through the inertial measurement unit data worn on the body trunk, and a conversion matrix from the body trunk coordinate system to the inertial coordinate system is obtained; According to the transformation matrix from the body trunk coordinate system to the inertial coordinate system and the impact direction vector under the body trunk coordinate system, the impact direction vector under the inertial coordinate system is obtained, wherein the impact direction vector under the inertial coordinate system is specifically expressed as follows by a formula: In the formula, Representing a transformation matrix of the body trunk coordinate system to the inertial coordinate system, Representing the impact direction vector in the body trunk coordinate system, Representing an impact direction vector in an inertial coordinate system; according to the impact direction vector under the inertial coordinate system and the orientation matrix of the head coordinate system relative to the inertial coordinate system, the impact direction vector under the head coordinate system is obtained, wherein the impact direction vector under the head coordinate system is specifically expressed as follows by a formula: In the formula, Representing an orientation matrix of the head coordinate system relative to the inertial coordinate system, Representing an orientation matrix Is used for the inverse matrix of (a), Representing an impact direction vector in a head coordinate system; Obtaining the impact risk degree of a user according to the difference between the impact direction vector under the head coordinate system and the personal maximum fragile direction vector under the head coordinate system, wherein the impact risk degree of the user is specifically expressed as follows: In the formula, Representing the person's greatest vulnerability orientation vector in the head coordinate system, Representation of Is used for the mold length of the mold, Representation of Is used for the mold length of the mold, The activation function is represented as a function of the activation, Indicating the impact risk level of the user.
3. The intelligent early warning method for dental trauma risk according to claim 1, wherein the irregularity of the current movement pattern is obtained according to the distribution of the energy data in the movement data, and the irregularity of the current movement pattern is specifically expressed as: In the formula, Representing the first of the energy data sequences The probability of the occurrence of the seed data, Representing the number of all data types in the energy data sequence, Represents a logarithmic function based on a natural constant, Representing the degree of irregularity of the current movement pattern; wherein the same data in the energy data sequence is the same data.
4. The intelligent early warning method for dental trauma risk according to claim 1, wherein mapping the duration and the impact rate of the impact duration period in the several movements in the user history in the movement habit feature space, clustering the several movements to obtain several clusters, and obtaining the injury risk factor of each cluster according to the data distribution in the clusters, comprises: The method comprises the steps of obtaining a plurality of impact duration time periods in each movement of a user history, taking the average value of the duration time periods of the impact duration time periods as the duration time characteristic of the user in each movement, taking the average value of the impact rate in each movement of the user history as the impact rate characteristic of the user in each movement, and obtaining the combination of the duration time characteristic and the impact rate characteristic of the user in the history; The method comprises the steps of taking a duration characteristic as a horizontal axis and taking an impact rate characteristic as a vertical axis to construct a movement habit characteristic space of a user, mapping the duration characteristic and the impact rate characteristic combination of a plurality of movements of the user in a history into the movement habit characteristic space to obtain a plurality of data points, clustering the plurality of data points through a K-means clustering algorithm to obtain a plurality of class clusters; obtaining injury risk factors of each cluster according to the distance from the centroid point of each cluster to the origin, wherein the injury risk factors of each cluster are specifically expressed as follows: In the formula, Represent the first The distance from the centroid point of the individual cluster to the origin of the motion habit feature space, Represent the first A risk factor for injury in the individual clusters, A linear normalization function.
5. The intelligent early warning method for dental trauma risk according to claim 1, wherein the obtaining the current injury risk level of the user through the injury risk factor of each cluster, the irregularity level of the current movement pattern and the impact risk level of the user comprises: acquiring the duration time characteristic and the impact rate characteristic of the current motion of a user, dividing the current motion into corresponding class clusters through a KNN algorithm, and marking the class clusters as target class clusters of the current motion; obtaining the current injury risk degree of the user according to the injury risk factor of the current moving target cluster, the irregularity degree of the current movement mode and the impact risk degree of the user, wherein the current injury risk degree of the user is specifically expressed as follows: In the formula, A risk factor for injury representing a cluster of targets currently in motion, Indicating the degree of irregularity of the current movement pattern, Indicating the degree of risk of impact by the user, Indicating the current risk of injury to the user.
6. The intelligent early warning method for dental trauma risk according to claim 1, wherein the step of obtaining the user's dental hardness degree according to the daily intake of nutrients and the user's dental density, and obtaining the user's current dental trauma risk factor according to the user's dental hardness degree and the user's current risk of injury comprises the steps of: the hardness of the teeth of the user is expressed as follows: In the formula, Indicating the daily intake of nutrients, Representing the density of the teeth of the user, A linear normalization function is represented and, Indicating the degree of rigidity of the user's teeth; The current dental trauma risk factor of the user is specifically expressed as follows: In the formula, Indicating the current risk level of injury to the user, Indicating the degree of rigidity of the teeth of the user, The activation function is represented as a function of the activation, Representing the current dental trauma risk factor for the user.
7. The intelligent early warning method for dental trauma risk according to claim 1, wherein the early warning of different levels by the current dental trauma risk factor of the user comprises: if the current dental trauma risk factor of the user is greater than or equal to the preset first parameter And is smaller than a preset second parameter When the current dental trauma risk factor of the user is greater than or equal to a preset second parameter, primary early warning is carried out, namely text or icon reminding is pushed through the mobile phone APP And is smaller than a preset third parameter When the user is in the first state, the intelligent wearable device is triggered to perform the first-stage early warning, namely triggering the short-time vibration and voice prompt of the intelligent wearable device, and when the current dental trauma risk factor of the user is greater than or equal to a preset third parameter And when the alarm is smaller than or equal to 1, advanced early warning is carried out, namely, strong continuous vibration and high-frequency alarm sound of all terminals are triggered, and an danger avoiding action diagram is immediately displayed on a screen.
8. An intelligent early warning system for dental trauma risk, comprising: the data acquisition and preprocessing module is used for acquiring exercise data, tooth density data and daily intake data of nutrients of a user and preprocessing the data; The motion data analysis module is used for obtaining an impact direction vector under a body trunk coordinate system through posture inclination angle data in motion data, obtaining a personal maximum fragile direction vector under a head coordinate system, converting the impact direction vector under the body trunk coordinate system to obtain an impact direction vector under the head coordinate system, obtaining the impact risk degree of a user according to the difference between the impact direction vector under the head coordinate system and the personal maximum fragile direction vector, obtaining the irregular degree of a current motion mode according to the distribution of energy data in the motion data, mapping the duration and the impact rate of an impact duration period in a plurality of times of motion in a user history in a motion habit feature space, clustering the plurality of times of motion to obtain a plurality of clusters, obtaining an injury risk factor of each cluster according to the data distribution in the clusters, and obtaining the current injury risk degree of the user through the injury risk factor of each cluster, the irregular degree of the current motion mode and the impact risk degree of the user; The dental trauma risk assessment module is used for obtaining the hardness degree of the teeth of the user according to the daily average intake of nutrients and the density of the teeth of the user; And the early warning module is used for carrying out early warning of different levels through the current dental trauma risk factors of the user.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing an intelligent pre-warning method of dental trauma risk according to any one of claims 1-7 when the computer program is executed by the processor.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements an intelligent pre-warning method of dental trauma risk according to any one of claims 1-7.

Description

Intelligent early warning method and system for dental trauma risk Technical Field The invention relates to the technical field of dental trauma risk assessment and early warning, in particular to an intelligent early warning method and system for dental trauma risk. Background Dental injuries, especially in children and young children, are often caused by accidents such as sports, traffic accidents, falls and the like. Dental trauma not only affects the oral health of the patient, but may also lead to long-term oral dysfunction or cosmetic problems. Early warning and prevention of dental injuries is therefore of great importance, especially for high risk groups (e.g. athletes, children, etc.). In the intelligent early warning process of the dental injury risk, data collected by a sensor or equipment may be inaccurate or interfered by environment, so that risk assessment is inaccurate, health conditions, exercise habits and the like of different individuals are greatly different, a model is difficult to comprehensively adapt to the situation of each person, namely, the individual difference influences the intelligent early warning of the dental injury risk, a user may not be willing to continuously wear the equipment or provide related data, and effectiveness of a system is influenced, namely, the user dependence problem also influences the intelligent early warning of the dental injury risk. Disclosure of Invention The invention provides an intelligent early warning method and system for dental trauma risks, which are used for solving the problems of data accuracy, individual difference adaptability and user dependence in the intelligent early warning system for dental trauma risks. The aim of the invention can be achieved by the following technical scheme: The first aspect of the invention provides an intelligent early warning method for dental trauma risk, which comprises the following steps: Acquiring exercise data, tooth density data and daily intake data of nutrients of a user, and preprocessing the data; Obtaining a personal maximum vulnerable direction vector under a head coordinate system by attitude inclination angle data in motion data, obtaining an impact direction vector under the body trunk coordinate system, converting the impact direction vector under the body trunk coordinate system to obtain an impact direction vector under the head coordinate system, obtaining the impact risk degree of a user according to the difference between the impact direction vector under the head coordinate system and the personal maximum vulnerable direction vector, obtaining the irregular degree of a current motion mode according to the distribution of energy data in the motion data, mapping the duration and the impact rate of an impact duration period in a plurality of times of motion in a user history in a motion habit feature space, clustering the plurality of times of motion to obtain a plurality of clusters, obtaining an injury risk factor of each cluster according to the data distribution in the clusters, and obtaining the current injury risk degree of the user through the injury risk factor of each cluster, the irregular degree of the current motion mode and the impact risk degree of the user; Obtaining the hardness degree of the teeth of the user according to the daily average intake of the nutrients and the density of the teeth of the user; different levels of early warning are performed through the current dental trauma risk factors of the user. Further, the method for obtaining the impact risk degree of the user according to the difference between the impact direction vector under the head coordinate system and the maximum fragile direction vector of the individual comprises the following steps: The three-dimensional space posture of the body trunk in the inertial coordinate system is calculated in real time through the inertial measurement unit data worn on the body trunk, and a conversion matrix from the body trunk coordinate system to the inertial coordinate system is obtained; According to the transformation matrix from the body trunk coordinate system to the inertial coordinate system and the impact direction vector under the body trunk coordinate system, the impact direction vector under the inertial coordinate system is obtained, wherein the impact direction vector under the inertial coordinate system is specifically expressed as follows by a formula: In the formula, Representing a transformation matrix of the body trunk coordinate system to the inertial coordinate system,Representing the impact direction vector in the body trunk coordinate system,Representing an impact direction vector in an inertial coordinate system; according to the impact direction vector under the inertial coordinate system and the orientation matrix of the head coordinate system relative to the inertial coordinate system, the impact direction vector under the head coordinate system is obtained, wherein the impact directio