Health index monitoring tooth socket and monitoring method thereof

Abstract

The invention relates to a health index monitoring tooth socket and a monitoring method thereof, and the method comprises the steps: collecting an optical signal in an oral cavity in real time through a collection module, and extracting a DC component and an AC component from the optical signal; and evaluating the signal quality of the direct-current component and the alternating-current component according to the standard signal proportion, and obtaining a target direct-current component and a target alternating-current component which meet the standard signal quality. On the basis of the cross correlation between the target direct-current component and the target alternating-current component, the blood oxygen saturation health index is obtained through a piecewise function. Based on the target alternating component, the heart rate health index is obtained through peak value detection. The problem of low monitoring accuracy of a monitoring method for monitoring health indexes in related technologies is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mouthpiece monitoring, in particular to a mouthpiece for monitoring health indicators and a monitoring method thereof. BACKGROUND

[0002] The blood oxygen saturation reflects the percentage of oxygenated hemoglobin in the blood relative to the total available hemoglobin capacity, and is an important indicator for evaluating the state of the respiratory and circulatory systems. Heart rate refers to the frequency of heart contractions and the number of beats per minute. Normal values of heart rate and blood oxygen saturation are crucial for maintaining the health of the human body.

[0003] Heart rate and blood oxygen saturation health indicators are usually monitored by medical devices and wearable devices. Traditional medical devices, such as medical-grade blood oxygen monitoring systems and portable blood oxygen meters, are difficult to provide convenient continuous real-time monitoring. Existing wearable devices, such as smart watches or bracelets, mainly rely on the method of collecting photoelectric signals through the skin surface for measurement. This method is easily affected by user movement, device fit, friction, and relative displacement, resulting in low monitoring data accuracy.

[0004] Therefore, there is an urgent need to provide a mouthpiece for monitoring health indicators and a monitoring method thereof to solve the above problems. SUMMARY

[0005] The embodiments of the present application provide a mouthpiece for monitoring health indicators and a monitoring method thereof to at least solve the problem of low monitoring accuracy in the related art monitoring method for monitoring health indicators.

[0006] In a first aspect, the embodiments of the present application provide a monitoring method of a mouthpiece for monitoring health indicators, the mouthpiece comprising an acquisition module, the method comprising:

[0007] real-time acquisition of an optical signal in the oral cavity by the acquisition module, extraction of a direct current component and an alternating current component in the optical signal;

[0008] evaluation of signal quality of the direct current component and the alternating current component according to a standard signal ratio, obtaining of target direct current components and target alternating current components meeting standard signal quality;

[0009] acquisition of a blood oxygen saturation health indicator by a segmented function based on the cross-correlation of the target direct current component and the target alternating current component;

[0010] acquisition of a heart rate health indicator by peak detection based on the target alternating current component.

[0011] In an embodiment, the acquisition of the blood oxygen saturation health indicator by the segmented function based on the cross-correlation of the target direct current component and the target alternating current component comprises:

[0012] The target AC component of the red light and the green light cross-correlation and the target AC component of the infrared light and the green light cross-correlation are obtained through a cross-correlation algorithm.

[0013] The red light target DC component and the infrared light target DC component are calculated through an average value algorithm respectively.

[0014] The proportional coefficient is determined based on the target AC component of the red light and the green light cross-correlation, the target AC component of the infrared light and the green light cross-correlation, the red light target DC component and the infrared light target DC component.

[0015] The blood oxygen saturation health index is obtained through a piecewise function based on the proportional coefficient.

[0016] In an embodiment, before the blood oxygen saturation health index is obtained through a piecewise function based on the proportional coefficient, the method further comprises:

[0017] The two-stage smoothing operation is performed on the continuous several proportional coefficients to obtain the smoothed proportional coefficient; wherein,

[0018] The first-stage smoothing comprises calculating the median M1 of the several proportional coefficients in the first buffer. R ;

[0019] The second-stage smoothing comprises determining a second buffer based on the median M1, calculating the median M2 of the several proportional coefficients in the second buffer, and taking the median M2 as the smoothed proportional coefficient. R R R

[0020] In an embodiment, the blood oxygen saturation health index is obtained through a piecewise function based on the proportional coefficient, comprising:

[0021] When the proportional coefficient is less than or equal to a preset threshold, the blood oxygen saturation health index is obtained through a linear relationship SpO2=a+bR.

[0022] When the proportional coefficient is greater than the preset threshold, the blood oxygen saturation health index is obtained through a linear relationship SpO2=a+bc+d(R-c).

[0023] Wherein, a, b, c, and d are calibration coefficients calculated through least square regression, a is the intercept of the low section interval, b is the slope of the low section interval, c is the preset threshold, d is the slope of the high section interval, R is the proportional coefficient, and SpO2 is the blood oxygen saturation health index.

[0024] In an embodiment, the DC component and the AC component are extracted from the light signal, comprising: ​​​

[0025] extracting a filtered direct current component from the optical signal by moving average filtering, wherein a window length of the moving average filtering is several times of a sampling frequency;

[0026] obtaining an initial alternating current component according to a signal difference between the optical signal and the direct current component;

[0027] performing outlier detection and replacement on the initial alternating current component to obtain a normal alternating current component.

[0028] In an embodiment, performing outlier detection and replacement on the initial alternating current component to obtain a normal alternating current component comprises:

[0029] defining outliers according to an upper quartile, a lower quartile and an interquartile range;

[0030] judging whether data of each sampling point of the initial alternating current component is the outlier;

[0031] when the data of each sampling point of the initial alternating current component is the outlier, shifting the data of each sampling point of the initial alternating current component which is the outlier to the direct current component to obtain a normal alternating current component;

[0032] wherein the outlier is a value less than a lower threshold or greater than an upper threshold, the lower threshold is a difference between the lower quartile and N times of the interquartile range, and the upper threshold is a sum of the upper quartile and N times of the interquartile range.

[0033] In an embodiment, obtaining target direct current component and target alternating current component which meet standard signal quality according to the signal quality of the direct current component and the alternating current component comprises:

[0034] judging whether the signals of the direct current component and the alternating current component are normal, and / or judging whether the signal of the alternating current component is delayed in alignment processing;

[0035] taking the signals of the normal direct current component and the normal alternating current component, and / or the signal of the alternating current component which is not delayed in alignment processing as valid signals;

[0036] obtaining a standard signal proportion coefficient according to a square of a ratio of a length of the valid signals to a total signal window length;

[0037] comparing the signal quality of the current direct current component and the current alternating current component with the standard signal proportion coefficient;

[0038] taking the direct current component and the alternating current component which are greater than or equal to the standard signal proportion coefficient as target direct current component and target alternating current component.

[0039] In an embodiment, the heart rate health index is obtained by peak detection based on the target alternating current component, including:

[0040] initial peak detection is performed on the target alternating current component to determine an initial peak interval;

[0041] target peak detection is performed based on the initial peak interval to obtain a target peak interval, wherein a minimum interval of the target peak detection is several times of the initial peak interval;

[0042] a heart rate health index is obtained according to the target peak interval and a sampling frequency.

[0043] In an embodiment, after the direct current component and the alternating current component are extracted from the light signal, the method further includes:

[0044] phase differences between red light and infrared light alternating current component signals and a green light alternating current component signal are calculated respectively by a cross-correlation algorithm, the red light and infrared light alternating current components are respectively phase-aligned with the green light alternating current component based on the phase differences, and aligned alternating current components are obtained;

[0045] the aligned alternating current components are filtered by a third-order Butterworth band-pass filter to obtain denoised alternating current components, wherein the filtering includes forward and reverse filtering.

[0046] Before the direct current component and the alternating current component are extracted from the light signal, the method further includes:

[0047] whether the mouthpiece is worn is determined by a temperature sensor and a touch sensor in the acquisition module, and health index monitoring is performed when the mouthpiece is in a worn state;

[0048] wherein a change value of a capacitance is monitored in real time by the touch sensor, and when the change value is higher than a preset capacitance threshold, a touch event is determined, and in the case of the touch event, infrared light and ambient light are monitored by a PPG sensor, and when the infrared light and the ambient light are both less than or equal to a preset threshold, the mouthpiece is determined to be in a worn state;

[0049] when the infrared light or the ambient light is higher than a preset threshold, a temperature monitored by the temperature sensor is lower than a preset temperature threshold, a power of the mouthpiece is lower than a preset power threshold, or the mouthpiece is in a charging state, the mouthpiece is determined to be in an unworn state.

[0050] In a second aspect, an embodiment of the present application provides a mouthpiece for monitoring a health index, including:

[0051] The acquisition module comprises a substrate, a connecting piece, an optical sensor unit and an optical window, one end of the connecting piece is fixedly connected with the substrate, the other end is fixedly connected with the optical sensor unit, and a monitoring surface of the optical sensor unit is provided with the optical window.

[0052] The mouthpiece body is provided with a mounting cavity matched with the acquisition module, and the acquisition module is mounted through the mounting cavity.

[0053] In an embodiment, the detection surface of the optical sensor unit is 2.5-3.0 mm away from the surface of the substrate.

[0054] In an embodiment, the distance between the inner wall of the optical window and the detection surface of the optical sensor module is less than 0.05-0.1 mm, and the thickness of the optical window is 0.5-0.7 mm.

[0055] In a third aspect, the embodiments of the present application provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the monitoring method of the monitoring health index mouthpiece according to the first aspect.

[0056] The monitoring health index mouthpiece and the monitoring method thereof provided by the embodiments of the present application have at least the following technical effects.

[0057] The acquisition module can acquire the optical signal in the oral cavity in real time, and separate the direct current component and the alternating current component therefrom, so as to effectively acquire the dynamic characteristics of the blood flow in the tissue. The signal quality of the direct current component and the alternating current component can be evaluated by the standard signal ratio, and the low-quality signal caused by the motion artifact or the environmental noise can be removed, thereby laying a reliable foundation for blood oxygen and heart rate calculation. Based on the cross-correlation of the target direct current component and the target alternating current component, the blood oxygen saturation health index is obtained by combining the piecewise function model, which can overcome the problem of insufficient precision of the traditional single model. At the same time, the heart rate is extracted by peak detection of the target alternating current component, which further improves the stability of heart rate calculation. The monitoring precision of the blood oxygen saturation and the heart rate health index is realized, and the problem of low monitoring precision of the monitoring method of the monitoring health index in the related art is solved.

[0058] The details of one or more embodiments of the present application are presented in the following drawings and description to make other features, objects and advantages of the present application more clear and easy to understand. BRIEF DESCRIPTION OF DRAWINGS

[0059] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0060] Figure 1 This is a schematic diagram of the overall structure of a type of brace used to monitor health indicators.

[0061] Figure 2 This is a top view of a type of brace used to monitor health indicators.

[0062] Figure 3 This is a side view of a type of brace used to monitor health indicators.

[0063] Figure 4 This is a flowchart of a monitoring method for braces used to monitor health indicators;

[0064] Figure 5 This is a flowchart illustrating step S101 according to an exemplary embodiment;

[0065] Figure 6 This is a flowchart illustrating step S102 according to an exemplary embodiment;

[0066] Figure 7 This is a flowchart illustrating step S103 according to an exemplary embodiment;

[0067] Figure 8 This is a flowchart illustrating step S104 according to an exemplary embodiment;

[0068] Figure 9 This is a schematic diagram illustrating the original PPG signal according to an exemplary embodiment;

[0069] Figure 10 This is a schematic diagram illustrating DC component extraction according to an exemplary embodiment;

[0070] Figure 11 This is a schematic diagram illustrating AC component extraction according to an exemplary embodiment;

[0071] Figure 12 This is a schematic diagram of the AC components after denoising and signal alignment, according to an exemplary embodiment.

[0072] Figure 13 This is a schematic diagram illustrating the results of blood oxygen saturation monitoring according to an exemplary embodiment;

[0073] Figure 14 This is a schematic diagram illustrating heart rate monitoring results according to an exemplary embodiment.

[0074] In the above figures, the meanings of the reference numerals are as follows:

[0075] 1. Optical sensor unit; 2. Connector; 3. Substrate; 4. PPG light window; 5. Braces pre-drilled slot; 6. Braces. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0077] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0078] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0079] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0080] Blood oxygen saturation reflects the percentage of oxygenated hemoglobin bound to oxygen in the blood, relative to the total available hemoglobin, and is an important indicator for assessing the state of the respiratory and circulatory systems. Heart rate refers to the frequency of heart contractions and the number of beats per minute. Normal values ​​for heart rate and blood oxygen saturation are crucial for maintaining good health. Current technologies in blood oxygen saturation and heart rate monitoring mainly include wearable devices (such as watches and wristbands) and medical devices (such as pulse oximeters and electrocardiographs). These devices are mostly based on photoplethysmography (PPG) and / or electrophysiological signal measurement (such as ECG) technologies. PPG technology assesses blood flow characteristics by emitting a light source onto the skin and measuring changes in reflected or transmitted light, thereby calculating blood oxygen saturation and heart rate. Typically, red and infrared PPG signals are used to analyze blood oxygen, while green PPG signals are used to calculate heart rate. ECG technology measures heart rate and cardiac health by monitoring cardiac electrical activity. Normal values ​​for heart rate and blood oxygen saturation are crucial for maintaining good health.

[0081] Despite the availability of effective monitoring technologies, existing medical and wearable devices for blood oxygen monitoring still have some limitations and problems in their use.

[0082] 1) Insufficient monitoring continuity and real-time performance

[0083] Traditional medical devices, such as medical-grade pulse oximeters and portable pulse oximeters, struggle to provide convenient, continuous, real-time monitoring. For example, medical-grade pulse oximeters are typically bulky and expensive, making them unsuitable for home use. Portable pulse oximeters, which collect data from the fingertip, are inconvenient for continuous, real-time monitoring around the clock.

[0084] 2) Insufficient monitoring accuracy and stability

[0085] Existing wearable devices, such as smartwatches or wristbands, primarily rely on collecting photoelectric signals from the skin's surface for measurement. This method is susceptible to factors such as user movement, device fit, friction, and relative displacement. A user's skin type, body hair, and skin color can also lead to inaccurate and fluctuating data. Unstable and inaccurate monitoring data directly impacts a user's assessment of their health, potentially causing unnecessary anxiety or missed diagnoses.

[0086] Therefore, there is an urgent need to provide a brace for monitoring health indicators and a monitoring method thereof, in order to solve the problems of low monitoring accuracy and poor portability of the monitoring methods for monitoring health indicators in related technologies.

[0087] Based on the above, this application provides a dental brace for monitoring health indicators, as well as a monitoring method and medium thereof.

[0088] In this document, it should be understood that the terms used may be technical means used to implement part of the present invention or other summary technical terms. For example, the terms may include:

[0089] Photoplethysmography (PPG): The PPG signal consists of a direct current (DC) component and an alternating current (AC) component. The DC component represents the baseline blood volume, reflecting the non-pulsatile blood volume in the tissue, while the AC component reflects the changes in blood volume caused by blood pulsation.

[0090] Optical signal: refers to a signal that transmits physiological health information through changes in parameters such as the intensity, frequency, phase, or pulse of a light wave. In this application, optical signal specifically refers to optical data collected in real time within the oral cavity by a PPG sensor (LED light source and photodetector).

[0091] Outliers: Values ​​in a dataset that deviate significantly from the majority of the data, possibly caused by measurement errors, data anomalies, or special events.

[0092] Lower quartile: The value at the 25th percentile after the data is sorted from smallest to largest (i.e., larger than 25% of the data and smaller than 75% of the data).

[0093] Upper quartile: The value at the 75th percentile after the data is sorted from smallest to largest (larger than 75% of the data, but smaller than 25% of the data).

[0094] Interquartile range: The difference between the upper quartile and the lower quartile, reflecting the dispersion of the data within the middle 50%.

[0095] Firstly, embodiments of this application provide a dental brace 6 for monitoring health indicators. Figure 1 This is a schematic diagram of the overall structure of a type of braces 6 used for monitoring health indicators, as shown below. Figure 1 As shown, braces 6 include:

[0096] The acquisition module includes a substrate 3, a connector 2, an optical sensor unit 1, and an optical window. One end of the connector 2 is fixedly connected to the substrate 3, and the other end is fixedly connected to the optical sensor unit 1. The monitoring surface of the optical sensor unit 1 is provided with an optical window.

[0097] The braces 6 body has an installation cavity that matches the acquisition module, through which the acquisition module is installed.

[0098] Specifically, Figure 2 This is a top view of the structure of a type of brace 6 used for monitoring health indicators. Figure 3 This is a side view diagram of a type of braces 6 used for monitoring health indicators, such as... Figure 2 and Figure 3 As shown, the coordinated design of the acquisition module and the main body of the braces 6 significantly improves the stability and reliability of health monitoring indicators. Specifically, the fixed connection between the base plate 3 and the connector 2 ensures the structural stability of the optical sensor unit 1 in the oral environment, avoiding sensor displacement or tilting caused by chewing or oral movements, thereby maintaining the constancy of the light signal acquisition path. The optical sensor unit 1 includes a PPG sensor, which includes an LED and a PD (photodetector). The monitoring surface of the optical sensor unit 1 is equipped with an optical window. By using high-transmittance materials and precision machining (such as surface high-gloss treatment and micro-gap control) to optimize the light path transmission efficiency, light scattering and absorption interference can be reduced, and the signal-to-noise ratio can be improved. Through the matching design of the mounting cavity of the braces 6 and the acquisition module, the rapid assembly and positioning of the module is realized. Sealant is injected into the mounting cavity to make the braces 6 and the acquisition module form a whole. This ensures the waterproof and moisture-proof performance of the sensor unit, adapting to the needs of long-term use in the oral environment. Ultimately, high accuracy, high stability, and comfort of blood oxygen saturation and heart rate monitoring are achieved.

[0099] In one embodiment, Figure 2 This is a top view diagram of a type of braces 6 used for monitoring health indicators, as shown below. Figure 2 As shown, the detection surface of the optical sensor unit 1 is 2.5mm-3.0mm away from the surface of the substrate 3.

[0100] Specifically, by designing the distance between the detection surface of the optical sensor unit 1 and the surface of the substrate 3 to be 2.5mm-3.0mm, the propagation path and acquisition efficiency of the optical signal can be effectively optimized. This solves the signal distortion problem caused by unstable optical path or environmental interference in traditional designs.

[0101] In one embodiment, the distance between the inner wall of the optical window and the detection surface of the optical sensing module is less than 0.05mm-0.1mm, and the thickness of the optical window is 0.5mm-0.7mm.

[0102] In one embodiment, the acquisition module further includes a temperature sensor for monitoring temperature and a touch sensor for monitoring capacitance.

[0103] Specifically, this parameter combination improves the signal acquisition accuracy of PPG sensor 1 in the oral cavity environment, and solves the monitoring error problem caused by light scattering or environmental interference in traditional designs.

[0104] The drawback of existing technologies is their limited applicability to relatively dry skin surfaces. Sweat or water residue on the light window surface can affect the transmission of the PPG optical path, thus impacting the accuracy of PPG detection results.

[0105] In summary, the PPG sensor's protrusion height can be increased through connector 2. By embedding the thin-walled optical window into the pre-reserved slot 5 of the brace 6, the internal tissues of the mouth apply pressure to connector 2 (the protruding part) after the brace 6 is worn. Under this pressure, the PPG sensor achieves higher data quality from peripheral arterial blood vessels. In this structure, connector 2 also reduces the influence of saliva. The encapsulated brace 6 addresses the adaptability of PPG optical path transmission to the moist environment of the oral cavity and the impact of saliva accumulation on the surface of the PPG optical window 4, thus achieving better accuracy in PPG detection results.

[0106] To further verify the effectiveness of this application, the following experiments were conducted: The researchers wore braces 6 packaged according to this technical structure, and simultaneously wore the gold standard device. They obtained approximately 350 hours of data through two modes: sleep and laboratory hypoxemia. After statistical analysis with the data from the gold standard device, the root mean square error (RMSE) between the two was 3.0, which meets the US FDA requirement of a root mean square error (RMSE) of less than 3.5.

[0108] Experimental verification has proven that the dental crown 6 packaged according to this technical structure solves the problems of PPG optical path transmission adaptability in the moist environment of the oral cavity and the impact of saliva accumulation and residue on the surface of PPG optical window 4 on PPG optical path transmission, thereby obtaining better accuracy of PPG detection results.

[0109] Secondly, embodiments of this application provide a method for monitoring health indicators of braces 6. Figure 4 This is a flowchart of a monitoring method for health indicators using braces 6, such as... Figure 4 As shown, the method includes:

[0110] Step S101: Acquire the light signal in the oral cavity in real time through the acquisition module, and extract the DC component and AC component from the light signal.

[0111] Step S102: Evaluate the signal quality of the DC component and AC component according to the standard signal ratio, and obtain the target DC component and target AC component that meet the standard signal quality.

[0112] Step S103: Based on the cross-correlation between the target DC component and the target AC component, obtain the blood oxygen saturation health index through a piecewise function.

[0113] Step S104: Based on the target communication component, obtain heart rate health indicators through peak detection.

[0114] In summary, this application provides a method for monitoring health indicators in braces 6. By using the cross-correlation between the target DC component and the target AC component, combined with a piecewise function model, the method obtains the blood oxygen saturation health indicator, overcoming the accuracy limitations of traditional single models. Simultaneously, by extracting heart rate through peak detection of the target AC component, the stability of heart rate calculation is further improved. This achieves high monitoring accuracy for both blood oxygen saturation and heart rate health indicators, solving the problem of low monitoring accuracy in related technologies.

[0115] Figure 5 This is a flowchart illustrating step S101 according to an exemplary embodiment, as follows: Figure 5 As shown, step S101 involves acquiring the light signal inside the oral cavity in real time using the acquisition module, and extracting the DC and AC components from the light signal. This specifically includes the following steps:

[0116] Step S1011: Extract the filtered DC component from the optical signal by moving average filtering, wherein the window length of the moving average filtering is a multiple of the sampling frequency.

[0117] Alternatively, moving average filtering is a computationally simple filtering method that behaves similarly to a low-pass filter, but operates in the time domain and does not require calculating the coefficients of an FIR or IIR filter. The window length for moving average filtering can be set to 1-2 times the sampling frequency.

[0118] Step S1012: Obtain the initial AC component based on the signal difference between the optical signal and the DC component.

[0119] Optionally, a moving average filter can be used to obtain the DC component and the baseline drift portion. Then, the filtered signal is subtracted from the optical signal (the original time signal includes the PPG signals from the red, infrared, and green light paths) to obtain the initial AC component. The expression for the moving average filter is as follows:

[0120]

[0121] S AC (i)=S(i)-S DC (i)

[0122] Where N is the window length of the moving average filter, and W is the window length of the PPG signal processing. S(i) represents the i-th sampling point of the original PPG signal, 1≤i≤W, S DC (i) represents the i-th sampling point of the filtered signal.

[0123] Step S1013: Perform outlier detection and replacement on the initial AC components to obtain normal AC components. Specifically, this includes:

[0124] Outliers are defined by the upper quartile, lower quartile, and interquartile range.

[0125] Determine whether the data at each sampling point of the initial AC component are outliers;

[0126] When the data at each sampling point of the initial AC component are outliers, the outliers at each sampling point of the initial AC component are transferred to the DC component to obtain the normal AC component.

[0127] Outlier values ​​are those that are less than the lower threshold or greater than the upper threshold. The lower threshold is the difference between the lower quartile and N times the interquartile range, and the upper threshold is the sum of the upper quartile and N times the interquartile range.

[0128] Optionally, since the extreme values ​​of the AC component should be relatively stable within a short window, and moving average filtering may introduce errors, leading to excessively large outliers in the AC extreme values, outlier detection and replacement are performed on the AC component. Outliers are defined as elements that are more than 0.6 interquartile ranges above the upper quartile (75%) or more than 0.6 interquartile ranges below the lower quartile (25%), and those exceeding the threshold are transferred from the AC component to the DC component.

[0129] S AC (i)←S AC (i)-e AC (i), 1≤i≤W

[0130] S DC (i)←S DC (i)+e AC (i), 1≤i≤W

[0131] Among them, e AC (i)=(S AC (i)-UB)I(S AC (i)-UB>0)+(S AC (i)-LB)I(S AC (i) - LB < 0) represents the outlier error, and I is the indicator function.

[0132]

[0133] The upper threshold, Q is the lower threshold. S (p) represents the p-quantile of the signal S, and λ>0 is a parameter.

[0134] Furthermore, PPG signals acquired within the oral cavity are susceptible to various types of interference, including low-frequency noise such as baseline drift caused by respiration and high-frequency noise such as motion artifacts introduced by head movement or saliva flow. A photoplethysmograph (PPG) sensor is embedded in brace 6 to acquire PPG signals from three pathways (red, infrared, and green light) at a frequency of 50Hz. The acquired signals are stored in a buffer, and the latest acquired signals are processed at fixed preset intervals. Real-time measurement and monitoring of blood oxygen and heart rate are achieved through a series of signal analysis and processing methods.

[0135] Therefore, step S101 significantly improves the accuracy and robustness of signal processing by processing the DC and AC components of the PPG optical signal in stages, combined with moving average filtering and outlier detection techniques. Specifically, a moving average filter (1-2 times the sampling frequency) with dynamically matched window length and sampling frequency is used to extract the DC component, significantly reducing computational complexity while preserving effective baseline information. Secondly, the initial AC component is obtained based on the difference between the DC component and the original signal, and outliers are dynamically defined using quartiles and interquartile ranges (IQR). Outliers exceeding the threshold are removed from the AC component and transferred to the DC component, thus purifying noise in the AC component (such as LED fluctuations or detection errors) and correcting systematic errors in the DC component caused by baseline drift, avoiding misjudgments of dynamic signals using the fixed threshold method. This provides a cleaner signal foundation for subsequent blood oxygen and heart rate calculations, ultimately achieving medical-grade monitoring accuracy.

[0136] In one embodiment, before step S101, where the optical signal inside the oral cavity is acquired in real time by the acquisition module, and the DC component and AC component are extracted from the optical signal, the method further includes:

[0137] The wearing status of braces 6 is determined by the temperature sensor and touch sensor in the acquisition module;

[0138] Specifically, the capacitance change value is monitored in real time by the touch sensor. When the change value is higher than the preset capacitance threshold, it is determined to be a touch event. In the case of a touch event, infrared light and ambient light are monitored by the PPG sensor. When the infrared light and ambient light are both less than or equal to the preset threshold, the braces 6 are determined to be in the wearing state.

[0139] When the braces are in the wearing state, if the infrared light or ambient light is higher than the preset threshold, the temperature monitored by the temperature sensor is lower than the preset temperature threshold, the battery level of the braces is lower than the preset battery level threshold, or the braces are in the charging state, the braces are determined to be in the unworn state.

[0140] By combining the capacitance change value of the touch sensor (contact detection) with the infrared / ambient light intensity of the PPG sensor for dual verification, the wearing status of the braces 6 can be accurately identified. Simultaneously, through infrared or ambient light monitoring, temperature sensor monitoring, and battery level monitoring, the braces 6 can be accurately identified as not being worn. By accurately monitoring the wearing status of the braces 6, and then executing the on / off action of the braces 6 to monitor health indicators, the problem of wasted energy due to misjudgments in the braces 6 is solved.

[0141] In one embodiment, after extracting the DC and AC components from the optical signal, the method further includes:

[0142] The phase difference between the red light and infrared light AC component signals and the green light AC component signal is calculated by cross-correlation algorithm. Based on the phase difference, the red light and infrared light AC components are phase aligned with the green light AC component to obtain the aligned AC components.

[0143] Based on the aligned AC components, a third-order Butterworth bandpass filter is used for filtering to obtain the denoised AC components. The filtering process includes forward and backward filtering.

[0144] Optionally, step S101 has separated the DC and AC components from the original signal. The AC signal has had its baseline drift removed, but the current signal may contain phase errors and high-frequency or low-frequency noise. Therefore, the signal is aligned and denoised. First, a signal alignment method is used to eliminate the phase error. The phase difference of the signal is calculated by cross-correlation, and then the earlier signal is delayed to achieve signal alignment. Due to the characteristic of less scattering, green light often has better signal quality, so the red light and infrared light signals are aligned with the green light signal once each. After signal alignment, a third-order Butterworth bandpass filter is used to filter the signal. The Butterworth bandpass filter is characterized by a flat amplitude-frequency response within the passband, i.e., ripple-free characteristics, which is suitable for maintaining the waveform of the signal without distortion. The passband frequency range is set to 0.5Hz to 4Hz. Then, the signal is forward and backward filtered to remove unwanted frequency components from the signal while avoiding phase delay. At this point, the AC component with phase error and noise removed is obtained.

[0145] The accuracy of the AC components was further improved by aligning and denoising them. Specifically, firstly, a green light signal was used as a reference for multi-wavelength phase alignment, which solved the problem of asynchronous multi-channel signals caused by differences in optical paths. Then, a third-order Butterworth bandpass filter was used in combination with forward-backward filtering technology to effectively filter out breathing interference (low frequency) and motion artifacts (high frequency) while avoiding the phase distortion problem caused by traditional filtering.

[0146] Figure 6 This is a flowchart illustrating step S102 according to an exemplary embodiment, as follows: Figure 6 As shown, step S102 involves evaluating the signal quality of the DC and AC components based on the standard signal ratio to obtain target DC and target AC components that meet the standard signal quality. Specifically, this includes the following steps:

[0147] Step S1021: Determine whether the DC component and AC component signals are normal, and / or determine whether the AC component signal is delayed during alignment processing.

[0148] Step S1022: Take the normal DC component and AC component signals, and / or the AC component signal that was not delayed during the alignment process, as valid signals.

[0149] Step S1023: Obtain the standard signal scaling factor by squared the ratio of the length of the effective signal to the length of the total signal window.

[0150] Step S1024: Compare the signal quality of the current DC component and AC component with the magnitude of the scaling factor of the standard signal;

[0151] Step S1025: The DC component and AC component that are greater than or equal to the standard signal scaling factor are taken as the target DC component and target AC component.

[0152] Optionally, after aligning and denoising the AC components, the signal quality needs to be assessed to determine the reliability of the calculated blood oxygen or heart rate. If the signal quality is good, the output is considered reliable; if the signal quality is poor, the output is considered unreliable, and the result calculated from the previous signal window is used. Commonly used signal quality assessment methods based on statistical rules suffer from strong subjectivity and inability to adapt to environmental changes, while signal quality assessment models based on deep learning suffer from high computational complexity and inability to process in real time. This application proposes a signal quality assessment method based on the effective signal ratio. During DC / AC component extraction, outliers are considered invalid signals; during AC alignment, the delayed portion of the signal is considered invalid. The signal quality is calculated using the following formula:

[0153]

[0154] The decision on whether to use the window to calculate the result is based on the value of η.

[0155] Step S102 addresses the issues of strong subjectivity in traditional statistical rule-based methods and high computational complexity in deep learning models by evaluating signal quality based on the effective signal ratio, thereby improving the objectivity and real-time performance of PPG signal processing. Specifically, during DC / AC component extraction, outliers and delays in AC alignment are considered invalid signals. Signal quality is quantified using η. When η is greater than or equal to a preset standard signal ratio coefficient, the current window's signal is used to calculate blood oxygen and heart rate; otherwise, the results from the previous window are used. This reduces the overhead of invalid signal processing and adapts to instantaneous errors caused by environmental interference (such as motion artifacts or oral movements). Dynamic adaptive evaluation of signal quality is achieved through simple ratio calculation, aligning with the low-power requirements of embedded devices.

[0156] Figure 7 This is a flowchart illustrating step S103 according to an exemplary embodiment, as follows: Figure 7As shown, step S103 involves obtaining the blood oxygen saturation health index through a piecewise function based on the cross-correlation between the target DC component and the target AC component. Specifically, this includes the following steps:

[0157] Step S1031: Obtain the target communication component of the cross-correlation between red light and green light, and the target communication component of the cross-correlation between infrared light and green light through the cross-correlation algorithm.

[0158] Step S1032: Calculate the DC component of the red light target and the DC component of the infrared light target respectively using the average value algorithm.

[0159] Step S1033: Determine the scaling factor based on the target AC component of the cross-correlation between red and green light, the target AC component of the cross-correlation between infrared and green light, the target DC component of red light, and the target DC component of infrared light.

[0160] Step S1034: Based on the proportional coefficient, obtain the blood oxygen saturation health index through a piecewise function. Specifically, this includes:

[0161] When the proportional coefficient is less than or equal to the preset threshold, the blood oxygen saturation health index is obtained through the linear relationship SpO2=a+bR;

[0162] When the proportional coefficient is greater than the preset threshold, the blood oxygen saturation health index is obtained through the linear relationship SpO2=a+bc+d(Rc);

[0163] That is, the piecewise function is:

[0164]

[0165] Where a, b, c, and d are calibration coefficients obtained through least squares regression. a is the intercept of the lower segment interval, b is the slope of the lower segment interval, c is the R value (preset threshold) corresponding to the inflection point, d is the slope of the upper segment interval, R is the proportionality coefficient, and SpO2 is a health indicator of blood oxygen saturation.

[0166] Alternatively, traditional PPG blood oxygen calculation uses the following formula

[0167]

[0168] SpO2=AB×R

[0169] Among them, AC R and DC R This indicates the magnitudes of the AC and DC components of red light, AC IR and DC IRThe AC and DC components of infrared light are represented by A and B, which are coefficients obtained through experimental fitting. Since the sensor can acquire green light signals, and the quality of green light signals is generally better than that of red and infrared light signals, the AC component of green light is included in the R-value calculation to improve the reliability of the calculation. The formula is transformed as follows:

[0170]

[0171] AC component of a given signal and DC components Calculate AC using the following cross-correlation formulas respectively. R AC G AC IR AC G The value of , calculate DC using the average value. R DC IR Value:

[0172]

[0173] Compared with traditional formulas and methods for calculating AC / DC component values ​​using peak-valley values, root mean square (RMS), envelope analysis, etc., this application has the advantages of a) incorporating green light information for calculation; b) using all sampling points to reduce the proportion of noise; and c) simplifying mathematical operations.

[0174] After calculating the R value, the following piecewise function is used to calculate blood oxygen saturation:

[0175]

[0176] The piecewise function is a ray with the same two endpoints, and a, b, c, d are the coefficients obtained from experimental fitting.

[0177] Step S103 achieves high-precision blood oxygen saturation monitoring through a multi-wavelength fusion algorithm and a piecewise function. Specifically, firstly, leveraging the high signal-to-noise ratio of the green light signal, the effective pulse wave feature extraction is enhanced by cross-correlation calculations between red and green light and infrared and green light, improving the anti-interference capability of R-value calculation. The DC component is calculated by averaging the data across the entire window, effectively suppressing the influence of random noise. A piecewise function (setting a threshold value c to distinguish between high and low blood oxygenation intervals) is used, and experimentally optimized coefficients a, b, and d are used to achieve accurate fitting of different blood oxygenation intervals.

[0178] In one embodiment, before obtaining the blood oxygen saturation health index based on a proportionality coefficient and a piecewise function, the method further includes:

[0179] Perform a two-stage smoothing operation on a consecutive set of scaling factors to obtain the smoothed scaling factors; where...

[0180] Level 1 smoothing involves calculating the median M1 of several scaling factors in the first buffer. R ;

[0181] Second-order smoothing includes median M1 R Determine the second buffer zone, and calculate the median M2 of several scaling factors in the second buffer zone. R The median M2 R As a scaling factor to obtain the smoothed result.

[0182] Optionally, to stabilize the output of continuously monitored heart rate and blood oxygenation and make it less susceptible to occasional noise, the output data is smoothed. For blood oxygenation data, median smoothing is applied twice. The first buffer stores the K most recent calculated R values, and the median of these values ​​is calculated. Then, a second buffer is used to store the K most recent calculated results. Then calculate the median. The last This is the smoothed R value, used to calculate blood oxygen saturation. A two-stage median smoothing algorithm improves the stability of blood oxygen monitoring.

[0183] Figure 8 This is a flowchart illustrating step S104 according to an exemplary embodiment, as follows: Figure 8 As shown, step S104 involves obtaining heart rate health indicators based on the target AC component through peak detection. This specifically includes the following steps:

[0184] Step S1041: Perform initial peak detection on the target AC component and determine the initial peak spacing;

[0185] Step S1042: Based on the initial peak spacing, perform target peak detection to obtain the target peak spacing, wherein the minimum spacing of the target peak detection is several times the initial peak spacing;

[0186] Step S1043: Obtain heart rate health indicators based on the target peak interval and sampling frequency.

[0187] Optionally, peak detection is first performed on the green light AC component, requiring the minimum distance between two peak points to be 0.1-0.2 times the sampling rate, and the relative height difference of the peaks to be greater than 0.05-0.1 times the AC amplitude range. If there are more than one peak within the window, the average distance Δ1 between the peak points is calculated. Then, a second peak detection is performed, requiring the minimum distance between two peak points to be 0.8Δ1, and the peak must be positive. If there are more than one peak within the window, the average distance Δ2 between the peak points is calculated. This is then performed using the formula...

[0188]

[0189] To calculate heart rate. Where PR is the heart rate value and f is the sampling frequency.

[0190] The accuracy of heart rate detection is improved through a dynamic peak detection algorithm. First, initial peak detection is performed using the green light AC component, with the minimum peak spacing set to 0.1-0.2 times the sampling rate. A relative height difference threshold (0.05-0.1 times the AC amplitude) is used to filter noise peaks, ensuring that false peaks caused by motion artifacts or environmental interference are eliminated in the initial detection. Then, based on the dynamic adjustment of the initial peak spacing Δ1, the minimum spacing for target peak detection is set to 0.8Δ1. Simultaneously, by forcing peaks to be positive, detection robustness is enhanced. Finally, the heart rate is calculated using the average value of the target peak spacing Δ2 within the window (PR = 60 / (Δ2×f)), providing stable and reliable heart rate data output for the braces 6. Combined with the advantages of green light signal quality (such as lower motion artifact interference), the heart rate calculation maintains a high signal-to-noise ratio and dynamic adaptability even in complex oral environments (such as chewing and speaking), providing stable and reliable heart rate data output for health monitoring devices.

[0191] Furthermore, Figure 9 This is a schematic diagram of the original PPG signal according to an exemplary embodiment, such as... Figure 9 As shown, using the Smart Braces 6, a 3-second raw PPG signal was acquired from the oral cavity at a frequency of 50Hz, including the raw PPG signal (red light), raw PPG signal (infrared light), and raw PPG signal (green light). According to... Figure 9 It can be seen that the signal has baseline drift, and the degree of baseline drift is even greater than the peak height difference of the PPG signal. Figure 10 This is a schematic diagram illustrating DC component extraction according to an exemplary embodiment, such as... Figure 10 As shown, the DC components obtained after DC component extraction include DC component (red light), DC component (infrared light), and DC component (green light). Figure 11 This is a schematic diagram illustrating AC component extraction according to an exemplary embodiment, such as... Figure 11 As shown, after removing baseline drift and outliers, the AC components are obtained, including the AC component (red light), the AC component (infrared light), and the AC component (green light). Figure 12 This is a schematic diagram illustrating the AC components after denoising and signal alignment according to an exemplary embodiment, such as... Figure 12 As shown, after denoising and signal alignment, AC components are obtained, including denoised and aligned AC components (red light), denoised and aligned AC components (infrared light), and denoised and aligned AC components (green light), resulting in a PPG signal with good waveform quality.

[0192] Immediately afterwards, Figure 13This is a schematic diagram illustrating the blood oxygen saturation monitoring results according to an exemplary embodiment, such as... Figure 13 As shown, a 2000-second blood oxygen monitoring session was conducted, yielding a root mean square error (RMSE) of 2.58, which is lower than the FDA standard of 3.5. This demonstrates that accurate real-time blood oxygen monitoring can be achieved.

[0193] Figure 14 This is a schematic diagram illustrating heart rate monitoring results according to an exemplary embodiment, such as... Figure 14 As shown, when blood oxygen monitoring is performed simultaneously with heart rate monitoring, the root mean square error of heart rate is 2.11, which is lower than the FDA standard of 3.0. This demonstrates that accurate real-time blood oxygen monitoring can be achieved.

[0194] In summary, the monitoring method for health indicators provided in this application using a dental brace 6 acquires light signals from the oral cavity in real time through an acquisition module, separating the DC and AC components to effectively capture the dynamic characteristics of blood flow in the tissues. By evaluating the signal quality of the DC and AC components using a standard signal ratio, low-quality signals caused by motion artifacts or environmental noise can be eliminated, thus laying a reliable foundation for blood oxygen and heart rate calculations. Based on the cross-correlation between the target DC and AC components, combined with a piecewise function model, the blood oxygen saturation health indicator is obtained, overcoming the accuracy limitations of traditional single models. Simultaneously, heart rate is extracted by detecting the peak value of the target AC component, further improving the stability of heart rate calculations. This achieves high monitoring accuracy for blood oxygen saturation and heart rate health indicators, solving the problem of low monitoring accuracy in related technologies.

[0195] Fourthly, embodiments of this application provide a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements a method for monitoring health indicators of braces provided in the first aspect.

[0196] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0197] In a possible implementation, the present invention can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, causes the terminal device to perform the steps of implementing the monitoring method for monitoring health indicators of braces provided in the second aspect.

[0198] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0199] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0200] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for monitoring health indicators in braces, characterized in that, The braces include a data acquisition module, and the method includes: The acquisition module acquires optical signals in the oral cavity in real time, and extracts DC and AC components from the optical signals. The signal quality of the DC component and the AC component is evaluated according to the standard signal ratio to obtain the target DC component and the target AC component that meet the standard signal quality. Based on the cross-correlation between the target DC component and the target AC component, the blood oxygen saturation health index is obtained through a piecewise function. Based on the target communication component, heart rate health indicators are obtained through peak detection.

2. The monitoring method for health indicators of a brace according to claim 1, characterized in that, The method of obtaining the blood oxygen saturation health index through a piecewise function based on the cross-correlation between the target DC component and the target AC component includes: The target communication component of the cross-correlation between red light and green light, and the target communication component of the cross-correlation between infrared light and green light are obtained by using the cross-correlation algorithm. The DC components of the red light target and the infrared light target are calculated separately using the average value algorithm. Based on the target AC component of the cross-correlation between red and green light, the target AC component of the cross-correlation between infrared and green light, the target DC component of red light, and the target DC component of infrared light, a proportionality coefficient is determined. Based on the aforementioned proportionality coefficient, blood oxygen saturation health indicators are obtained through a piecewise function.

3. The method for monitoring health indicators in a brace according to claim 2, characterized in that, Before obtaining the blood oxygen saturation health index based on the aforementioned proportionality coefficient and through a piecewise function, the method further includes: Perform a two-stage smoothing operation on a consecutive set of the aforementioned scaling factors to obtain smoothed scaling factors; wherein, First-level smoothing includes calculating the median M1 of several scaling factors in the first buffer. R ; Secondary smoothing includes smoothing based on the median M1. R Determine the second buffer zone, and calculate the median M2 of several of the aforementioned scaling factors in the second buffer zone. R The median M2 R As a scaling factor to obtain the smoothed result.

4. The monitoring method for health indicators of a brace according to claim 2, characterized in that, The process of obtaining the blood oxygen saturation health index based on the aforementioned proportional coefficient and through a piecewise function includes: When the proportional coefficient is less than or equal to a preset threshold, the blood oxygen saturation health index is obtained through the linear relationship SpO2=a+bR; When the proportional coefficient is greater than a preset threshold, the blood oxygen saturation health index is obtained through the linear relationship SpO2=a+bc+d(Rc); Where a, b, c, and d are calibration coefficients obtained by least squares regression, a is the intercept of the lower segment interval, b is the slope of the lower segment interval, c is the preset threshold, d is the slope of the higher segment interval, R is the proportionality coefficient, and SpO2 is the blood oxygen saturation health indicator.

5. The monitoring method for health indicators of a brace according to claim 1, characterized in that, Extracting the DC and AC components from the optical signal includes: The filtered DC component is extracted from the optical signal by means of a moving average filter, wherein the window length of the moving average filter is a multiple of the sampling frequency. The initial AC component is obtained based on the signal difference between the optical signal and the DC component; The initial AC component is subjected to outlier detection and replacement to obtain the normal AC component.

6. The method for monitoring health indicators in a brace according to claim 5, characterized in that, The initial AC component is subjected to outlier detection and replacement to obtain normal AC components, including: Outliers are defined by the upper quartile, lower quartile, and interquartile range. Determine whether the data at each sampling point of the initial AC component is an outlier; When the data at each sampling point of the initial AC component is the outlier, the data at each sampling point of the initial AC component that is the outlier is transferred to the DC component to obtain the normal AC component; Wherein, the outlier value is a value that is less than the lower threshold or greater than the upper threshold, the lower threshold is the difference between the lower quartile and N times the interquartile range, and the upper threshold is the sum of the upper quartile and N times the interquartile range.

7. The method for monitoring health indicators in a brace according to claim 1, characterized in that, The step of evaluating the signal quality of the DC component and the AC component according to a standard signal ratio, and obtaining target DC component and target AC component that meet the standard signal quality, includes: Determine whether the DC component and the AC component signals are normal, and / or determine whether the AC component signal is delayed during the alignment process; The normal DC component and the AC component signals, and / or the AC component signal that is not delayed during the alignment process, are taken as valid signals; The standard signal scaling factor is obtained by squared the ratio of the length of the effective signal to the total signal window length. Compare the signal quality of the current DC component and the AC component with the magnitude of the scaling factor of the standard signal; The DC component and the AC component that are greater than or equal to the standard signal scaling factor are taken as the target DC component and the target AC component.

8. The method for monitoring health indicators in a brace according to claim 1, characterized in that, The step of obtaining heart rate health indicators based on the target communication component through peak detection includes: The target AC component is subjected to initial peak detection to determine the initial peak spacing; Based on the initial peak spacing, target peak detection is performed to obtain the target peak spacing, wherein the minimum spacing of the target peak detection is several times the initial peak spacing; Based on the target peak spacing and sampling frequency, heart rate health indicators are obtained.

9. The method for monitoring health indicators in a brace according to claim 1, characterized in that, After extracting the DC and AC components from the optical signal, the method further includes: The phase difference between the red light and infrared light AC component signals and the green light AC component signal is calculated by cross-correlation algorithm. Based on the phase difference, the red light and infrared light AC components are phase aligned with the green light AC component to obtain the aligned AC components. Based on the aligned AC components, a third-order Butterworth bandpass filter is used for filtering to obtain the denoised AC components, wherein the filtering process includes forward and backward filtering.

10. The method for monitoring health indicators in a brace according to claim 1, characterized in that, Before extracting the DC and AC components from the optical signals in the oral cavity in real time via the acquisition module, the method further includes: The wearing status of the braces is determined by the temperature sensor and touch sensor in the acquisition module, and health indicators are monitored when the braces are being worn. The touch sensor monitors the capacitance change value in real time. When the change value is higher than the preset capacitance threshold, it is determined to be a touch event. In the case of a touch event, the PPG sensor monitors infrared light and ambient light. When the infrared light and the ambient light are both less than or equal to the preset threshold, the braces are determined to be in the wearing state. When the infrared light or ambient light is higher than a preset threshold, the temperature monitored by the temperature sensor is lower than a preset temperature threshold, the battery level of the braces is lower than a preset battery level threshold, or the braces are in a charging state, the braces are determined to be not being worn.

11. A brace for monitoring health indicators, characterized in that, A monitoring method for a dental brace used to monitor health indicators as described in any one of claims 1-10, wherein the dental brace comprises: The acquisition module includes a substrate, a connector, an optical sensor unit, and an optical window. One end of the connector is fixedly connected to the substrate, and the other end is fixedly connected to the optical sensor unit. The optical sensor unit has the optical window on its monitoring surface. The braces body has an installation cavity that matches the acquisition module, through which the acquisition module is installed.

12. A dental brace for monitoring health indicators according to claim 11, characterized in that: The detection surface of the optical sensor unit is 2.5mm-3.0mm away from the surface of the substrate.

13. A dental brace for monitoring health indicators according to claim 11, characterized in that: The distance between the inner wall of the optical window and the detection surface of the optical sensing module is less than 0.05mm-0.1mm, and the thickness of the optical window is 0.5mm-0.7mm.

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