Cervical vertebra alignment pressure model customization method and system based on C-Scan system

Abstract

The invention discloses a C-Scan system-based cervical vertebra alignment pressure model customization method and system, and the method comprises the steps: collecting multi-dimensional core data, constructing a cervical vertebra alignment adaptive reference value based on anatomy, muscle, degeneration and body type parameters, constructing a dynamic pressure distribution coefficient in combination with a dynamic posture and pressure data, and carrying out the customization of a cervical vertebra alignment pressure model. And the real-time optimal support height is calculated by fusing long-term use data and is dynamically updated. The system correspondingly executes the method, and full-period precise adaptation is achieved through multi-module cooperation. The problems of insufficient dynamic adaptation and poor long-term stability of an existing scheme are solved, individualized, dynamic and long-term optimization of cervical vertebra customization is achieved, and adaptation accuracy and continuity are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of cervical spine health care equipment customization technology, specifically to a method and system for customizing a cervical spine alignment pressure model based on the C-Scan system. Background Technology

[0002] Current customized cervical spine pillow solutions largely rely on static anatomical parameters or single pressure data modeling, failing to fully consider the coupling effect between dynamic fluctuations in neck muscle tension and sleep posture transitions, while also ignoring individual differences in the degree of cervical degeneration among different subjects. These solutions lack an adaptive adjustment mechanism for changes in the physiological state of the cervical spine during long-term use, resulting in customized products failing to maintain a uniform pressure distribution in dynamic sleep scenarios and poor preservation of the cervical spine's physiological curvature. With prolonged use, the fit rapidly diminishes, failing to continuously meet the individual's cervical spine biomechanical needs. The static and one-sided nature of existing solutions prevents customized cervical spine pillows from achieving precise fit throughout the entire lifecycle.

[0003] Based on the above problems, there is an urgent need for a technical solution that can dynamically respond to changes in physiological state and take into account individual differences and long-term adaptation. Summary of the Invention

[0004] The purpose of this invention is to address the problems of evaluation lag, lack of process control, insufficient incentive mechanisms, and fragmented data dimensions in existing technologies, thereby providing a method for customizing a cervical spine alignment pressure model based on the C-Scan system, comprising the following steps:

[0005] S1. Collect multi-dimensional core data of the subjects, including cervical curvature index, neck length, average tension of neck muscles, cervical degeneration coefficient, sleep posture angle, posture switching rate, posture duration, shoulder width to neck width ratio, pressure data and displacement data. The cervical degeneration coefficient is calculated based on the degree of intervertebral space narrowing and the size of osteophytes in the subjects.

[0006] S2. Based on the cervical curvature index, the neck length, the average tension of the neck muscles, the cervical degeneration coefficient, and the ratio of shoulder width to neck width, construct the cervical spine alignment and adaptation benchmark value.

[0007] S3. Based on the cervical spine alignment and adaptation benchmark value, the sleep posture angle, the posture switching rate, the posture duration, and the pressure data, the maximum pressure standard deviation and the average pressure standard deviation are calculated to construct a dynamic pressure distribution coefficient.

[0008] S4. Based on the dynamic pressure distribution coefficient, initial support height, usage time, daily average cervical curvature index change, deviation of the pressure distribution coefficient of each historical posture from the average historical pressure distribution coefficient, and angle difference between each historical posture and the reference posture, the real-time optimal support height is calculated. The initial support height is initially set based on the cervical curvature index and the neck length.

[0009] S5. Store the multi-dimensional core data, the cervical spine alignment and adaptation benchmark value, the dynamic pressure distribution coefficient, and the real-time optimal support height. Analyze the trend of the daily average cervical curvature index change based on the usage time, and update the cervical spine alignment and adaptation benchmark value, the dynamic pressure distribution coefficient, and the real-time optimal support height.

[0010] Preferably, the multi-dimensional core data is acquired through a C-Scan system, an electromyography (EMG) signal acquisition device, a posture capture device, and an imaging device. The C-Scan system is used to acquire the cervical curvature index, the neck length, the pressure data, and the displacement data. The EMG signal acquisition device is used to acquire the average tension of the neck muscles. The posture capture device is used to acquire the sleep posture angle, the posture switching rate, and the posture duration. The imaging device is used to acquire the degree of intervertebral disc stenosis and the size of the osteophytes.

[0011] More preferably, the shoulder width to neck width ratio is obtained by scanning the contours of the subject's shoulders and neck. Specifically, the straight-line distance at the widest point of the shoulder is taken as the shoulder width, and the horizontal straight-line distance in the middle of the neck is taken as the neck width. The ratio of the shoulder width to the neck width is then calculated to obtain the shoulder width to neck width ratio.

[0012] More preferably, the cervical degenerative coefficient is calculated by quantifying and assigning values ​​to the degree of intervertebral space narrowing and the size of osteophytes, and obtaining the cervical degenerative coefficient by weighted summation, wherein the weights of the weighted summation are determined based on clinical data.

[0013] In a further preferred embodiment, the cervical spine alignment adaptation benchmark value is calculated using the cervical spine alignment adaptation benchmark value formula, which integrates anatomical features, muscle tension, degenerative changes, and body proportion parameters to achieve dimensionless benchmark value quantification.

[0014] More preferably, the dynamic pressure distribution coefficient is calculated by a dynamic pressure distribution optimization formula, which is based on the cervical spine alignment adaptation benchmark value, integrates dynamic posture parameters and pressure distribution uniformity parameters, and outputs the distribution coefficient of the corresponding pressure unit.

[0015] More preferably, the real-time optimal support height is calculated by a dynamic adjustment formula for support height, which combines the dynamic pressure distribution coefficient, long-term usage trend and historical posture deviation data to output a support height value in the corresponding length unit.

[0016] Further preferably, the trend analysis of the daily average change in cervical curvature index is achieved through a linear regression algorithm, wherein the linear regression algorithm uses the usage duration as the independent variable and the daily average change in cervical curvature index as the dependent variable; when the absolute value of the daily average change in cervical curvature index exceeds a preset threshold, an immediate update is triggered, wherein the immediate update includes recalculating the cervical spine alignment adaptation benchmark value, the dynamic pressure distribution coefficient, and the real-time optimal support height.

[0017] A cervical spine alignment pressure model customization system based on the C-Scan system is provided for executing any of the above-described methods for customizing cervical spine alignment pressure models based on the C-Scan system. The system includes a data acquisition module, a model construction module, a support height calculation module, and a parameter update module. The data acquisition module collects multi-dimensional core data from the subjects. The model construction module constructs a cervical spine alignment adaptation benchmark value and a dynamic pressure distribution coefficient based on the multi-dimensional core data. The support height calculation module calculates the real-time optimal support height based on the dynamic pressure distribution coefficient, initial support height, usage time, daily average change in cervical curvature index, deviation of the pressure distribution coefficient from the historical average pressure distribution coefficient, and angle difference between the historical posture and the benchmark posture. The parameter update module stores the multi-dimensional core data, the cervical spine alignment adaptation benchmark value, the dynamic pressure distribution coefficient, and the real-time optimal support height.

[0018] More preferably, the data acquisition module includes a C-Scan submodule, a myoelectronic module, a posture submodule, and an imaging submodule; the C-Scan submodule is used to acquire the cervical curvature index, the neck length, the pressure data, and the displacement data; the myoelectronic module is used to acquire the average tension of the neck muscles; the posture submodule is used to acquire the sleep posture angle, the posture switching rate, and the posture duration; the imaging submodule is used to acquire the degree of intervertebral space narrowing and the size of osteophytes in the subject, so as to calculate the cervical degenerative coefficient.

[0019] The present invention has the following beneficial effects:

[0020] This invention addresses the core issues of insufficient dynamic adaptation and poor long-term stability in existing solutions by coupling neck muscle tension, dynamic changes in sleep posture, and differences in cervical spine degeneration to construct a three-level linkage model and a full-cycle update mechanism. It creatively achieves synergistic adaptation between static anatomical parameters and dynamic physiological states, ensuring the individualized precision and long-term effectiveness of customized cervical spine treatments. Attached Figure Description

[0021] Figure 1 Flowchart of the method for customizing the cervical spine alignment pressure model based on the C-Scan system in this application;

[0022] Figure 2 A custom system connection diagram is provided for the cervical spine alignment pressure model based on the C-Scan system in this application. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Existing customized cervical spine solutions rely solely on static anatomical parameters such as cervical curvature index and neck length, or on modeling based on single pressure data. They fail to consider the dynamic fluctuations in average neck muscle tension and the coupling effect of sleep posture switching. Furthermore, they neglect individual differences in the degree of cervical degeneration among different subjects and lack adaptive adjustment mechanisms for changes in cervical physiological state during long-term use. This results in unbalanced pressure distribution in dynamic sleep scenarios, poor maintenance of cervical physiological curvature, and rapid decline in adaptability after long-term use.

[0025] Based on this, please refer to Figures 1-2 This embodiment provides a method for customizing a cervical spine alignment pressure model based on the C-Scan system, including the following steps:

[0026] S1: Collect multi-dimensional core data of the subjects, including cervical curvature index, neck length, average tension of neck muscles, cervical degeneration coefficient, sleep posture angle, posture switching rate, posture duration, shoulder width to neck width ratio, pressure data and displacement data. The cervical degeneration coefficient is calculated based on the degree of intervertebral space narrowing and the size of osteophytes in the subjects.

[0027] S2: Based on the cervical curvature index, the neck length, the average tension of the neck muscles, the cervical degeneration coefficient, and the ratio of shoulder width to neck width, construct the cervical spine alignment and adaptation benchmark value.

[0028] S3: Based on the cervical spine alignment and adaptation benchmark value, the sleep posture angle, the posture switching rate, the posture duration, and the pressure data, the maximum pressure standard deviation and the average pressure standard deviation are calculated to construct a dynamic pressure distribution coefficient;

[0029] S4: Based on the dynamic pressure distribution coefficient, initial support height, usage time, daily average cervical curvature index change, deviation of the pressure distribution coefficient of each historical posture from the average historical pressure distribution coefficient, and angle difference between each historical posture and the reference posture, the real-time optimal support height is calculated. The initial support height is initially set based on the cervical curvature index and the neck length.

[0030] S5: Store the multi-dimensional core data, the cervical spine alignment and adaptation benchmark value, the dynamic pressure distribution coefficient, and the real-time optimal support height; analyze the trend of the daily average cervical curvature index change based on the usage time; and update the cervical spine alignment and adaptation benchmark value, the dynamic pressure distribution coefficient, and the real-time optimal support height.

[0031] This technical solution achieves precise, full-cycle cervical spine customization through multi-dimensional data acquisition, three-level model construction, and a dynamic update mechanism. Notably, the acquisition of multi-dimensional core data in S1 requires collaborative work from multiple devices. The cervical curvature index is calculated by scanning the sagittal contour of the cervical spine using the displacement sensor of the C-Scan system, acquiring the coordinates of the posterior margins of the C1 to C7 vertebral bodies, and then calculating the curvature. The unit is... (i.e., 1 / meter); Neck length is obtained by measuring the straight-line distance from the occipital protuberance to the suprasternal notch using the laser ranging function of the C-Scan system, in meters; Average neck muscle tension is obtained by attaching surface electromyography electrodes to the surface of the sternocleidomastoid and trapezius muscles using an electromyography signal acquisition device, collecting electromyography signals, and processing them using an electromyography-tension conversion algorithm, in Pascals; Cervical degenerative coefficient is obtained by acquiring the degree of intervertebral space narrowing and the size of osteophytes using imaging devices, assigning values ​​of 0, 1, 2, and 3 to no intervertebral space narrowing, mild narrowing, moderate narrowing, and severe narrowing, respectively, and assigning values ​​of 0, 1, and 2 to osteophyte diameters of less than 2 mm, 2 to 5 mm, and greater than 5 mm, respectively, and then weighted and summed with weights of 0.6 and 0.4; Sleep The sleep posture angle is obtained by acquiring the angle between the cervical spine axis and the horizontal plane through the infrared positioning module of the posture capture device, in radians; the posture switching rate is calculated by dividing the change in sleep posture angle by the time interval, in radians per second; the posture duration is obtained by recording the duration of the same sleep posture angle, in seconds; the shoulder width to neck width ratio is obtained by calculating the ratio after obtaining the straight-line distance between the widest point of the shoulder and the middle of the neck through 3D scanning, and is dimensionless; pressure data is obtained by acquiring the head and neck contact pressure through the pressure sensor array of the C-Scan system, in Pascals; displacement data is obtained by acquiring the displacement of each segment of the head and neck through the displacement sensor of the C-Scan system, in meters. The construction of the cervical spine alignment adaptation benchmark in S2 requires the integration of four types of parameters: anatomy, muscle, degeneration, and body type, quantified through a dedicated formula, providing a basic threshold for subsequent dynamic adaptation. The construction of the dynamic pressure distribution coefficient in S3 requires combining real-time posture parameters and pressure distribution uniformity to ensure accurate pressure adaptation in dynamic scenarios. The initial support height in S4 is based on the cervical curvature index and neck length through a fitting formula. The calculated value is in meters; the daily average change in cervical curvature index is obtained by averaging the differences in cervical curvature index collected daily, and the unit is meters. (i.e., 1 / meter per day); historical deviation data is calculated by retrieving the most recent 30 posture pressure data from the storage module to ensure long-term adaptability. Data storage in the S5 uses a solid-state drive with a capacity of no less than 1TB. Trend analysis uses a linear regression algorithm to fit a trend curve with usage duration as the independent variable and the daily average change in cervical curvature index as the dependent variable. The update cycle is set to 30 days, and the absolute value of the daily average change in cervical curvature index exceeds 0.05. The system triggers an instant update, recalculates all parameters, and then feeds back to the customized execution module to adjust the support structure.

[0032] Existing data acquisition schemes do not clearly define the functional division of multi-source acquisition devices and the data collaboration logic, resulting in poor correlation between data collected by different devices, missing or duplicate collection of some core parameters, which affects the accuracy and efficiency of subsequent model building.

[0033] Based on this, the multi-dimensional core data is acquired through a C-Scan system, an electromyography (EMG) signal acquisition device, a posture capture device, and an imaging device. The C-Scan system is used to acquire the cervical curvature index, neck length, pressure data, and displacement data. The EMG signal acquisition device is used to acquire the average tension of the neck muscles. The posture capture device is used to acquire the sleep posture angle, posture switching rate, and posture duration. The imaging device is used to acquire the degree of intervertebral disc stenosis and the size of osteophytes. This technical solution ensures the accurate acquisition and correlation matching of multi-dimensional core data by clearly defining the dedicated acquisition functions and data collaboration logic of each device.

[0034] It is worth mentioning that the C-Scan system needs to integrate a high-density pressure sensor array and a high-precision displacement sensor. The pressure sensor array has no fewer than 256 sensing points, and the sampling frequency is set to 100 Hz to ensure the spatial and temporal resolution of the pressure data. The displacement sensor has a resolution of no more than 0.01 mm, and the scanning range covers the C1 to C7 cervical spine region to ensure the accuracy of cervical curvature index and neck length measurement. The electromyography (EMG) signal acquisition device needs to be equipped with four surface EMG electrodes: two attached to the middle and lower segments of the bilateral sternocleidomastoid muscles, and two attached to the upper parts of the bilateral trapezius muscles. The sampling frequency is set to 200 Hz. The acquired EMG signals are transmitted to the processing unit via Bluetooth, and after being filtered by a 50 Hz notch filter to remove power frequency interference, they are converted into the average tension of the neck muscles. The posture capture device requires two infrared motion capture cameras, positioned on either side of the sleep area, with a sampling frequency set to 30 Hz. By identifying infrared markers attached to the surfaces of the C3, C5, and C7 cervical vertebrae, it calculates the sleep posture angle, posture switching rate, and posture duration, with data transmission latency controlled within 10 milliseconds. The imaging device can be an X-ray machine or a CT scanner. The X-ray machine needs to take anteroposterior and lateral cervical spine radiographs with a resolution of at least 300 dpi for measuring intervertebral disc height. The CT scanner needs to perform thin-slice scans of the cervical spine with a slice thickness not exceeding 1 mm for measuring osteophyte size. The output data from both devices is transmitted to the computing unit via a DICOM interface to ensure data format consistency. Data collaboration is achieved among the devices through a timestamp synchronization mechanism. After receiving data from each device, the computing unit sorts and integrates it into a multi-dimensional core dataset according to the acquisition time, providing a complete data foundation for subsequent model calculations.

[0035] Existing body shape parameter acquisition methods do not clearly define the measurement standards and scanning methods for shoulder width and neck width, resulting in inconsistent body shape proportion representations among different subjects and affecting the accuracy of cervical spine adaptation benchmark values.

[0036] Based on this, the shoulder width to neck width ratio is obtained through contour scanning of the subject's shoulders and neck. Specifically, the straight-line distance at the widest point of the shoulder is taken as the shoulder width, and the horizontal straight-line distance at the mid-section of the neck is taken as the neck width. The ratio of the shoulder width to the neck width is then calculated. This technical solution ensures the accuracy and consistency of body proportion parameters by clearly defining the scanning method and measurement standards. It is worth mentioning that the contour scanning requires a 3D structured light scanner, covering the area from the top of the head to the suprasternal notch, with a scanning resolution set to 0.1 mm to ensure clear contour details. The widest point of the shoulder is located based on the apex of the lateral border of the scapula. The highest point of the lateral border of the left and right scapulae is automatically extracted using the scanner's feature recognition algorithm, and the straight-line distance between the two points is the shoulder width. During measurement, the subject's arms must hang naturally and the shoulders must be relaxed. The mid-neck is located using the upper edge of the thyroid cartilage as a reference. A cross-section of the neck is taken at this level, and the maximum horizontal diameter of the cross-section is measured using the scanner's cross-sectional analysis function; this is the neck width. During measurement, the subject's head must be kept in a neutral position to avoid measurement errors caused by head tilt. After scanning, the scanner's software automatically calculates the ratio of shoulder width to neck width and outputs a dimensionless value. This value must correspond to the subject's actual body shape; for example, if the shoulder width is 0.4 meters and the neck width is 0.12 meters, the shoulder width to neck width ratio is 3.33. To ensure measurement repeatability, the same subject should be scanned three times, and the average of the three measurements is taken as the final shoulder width to neck width ratio, reducing the impact of random errors on subsequent model calculations.

[0037] The existing calculation scheme for cervical spine degeneration coefficient does not clearly define the quantitative standards and weighting criteria for the degree of intervertebral space narrowing and osteophyte size, resulting in inaccurate characterization of the degree of degeneration in different subjects and failing to provide precise degeneration constraints for cervical spine fitting.

[0038] Based on this, the calculation method for the cervical degenerative coefficient involves quantifying and assigning values ​​to the degree of intervertebral space narrowing and the size of osteophytes, and then obtaining the cervical degenerative coefficient through weighted summation. The weights of the weighted summation are determined based on clinical data. This technical solution ensures that the degenerative coefficient can accurately reflect the cervical degenerative state of the subject by clearly defining the quantitative standards and weighting criteria. It is worth mentioning that the quantification of the degree of intervertebral space narrowing needs to be based on lateral cervical spine radiographs obtained by imaging devices. The vertical height of each intervertebral space from C2-C3 to C6-C7 is measured using image measurement software. A value of 0 is assigned to no narrowing (intervertebral space height ≥ 5 mm), a value of 1 to mild narrowing (intervertebral space height 3 to 5 mm), a value of 2 to moderate narrowing (intervertebral space height 1 to 3 mm), and a value of 3 to severe narrowing (intervertebral space height < 1 mm). The average value of each intervertebral space is taken as the final quantified value of the degree of intervertebral space narrowing. The quantification of osteophyte size is based on cervical spine transverse images obtained by CT scans. The maximum diameter of osteophytes at the anterior and lateral margins of each vertebra is measured using image measurement software. Osteophytes with diameters <2 mm are assigned a value of 0, 2-5 mm a value of 1, and >5 mm a value of 2. The average of these values ​​is taken as the final quantified value of osteophyte size. The weights for the weighted summation are determined based on clinical data. Clinical data from 500 patients with cervical spondylosis of varying degrees of degeneration were collected, including the degree of intervertebral space narrowing, osteophyte size, and cervical discomfort symptom scores. Multiple linear regression analysis was used to fit the influence coefficients of intervertebral space narrowing and osteophyte size on the discomfort symptom scores. The weight for intervertebral space narrowing was determined to be 0.6, and the weight for osteophyte size to be 0.4. The formula for calculating the cervical degeneration coefficient is as follows: For example, when a subject's intervertebral space narrowing is quantified as 1 and osteophyte size is quantified as 1, the cervical degeneration coefficient is 0.6×1+0.4×1=1.0. This value is dimensionless and can objectively reflect the subject's cervical degeneration degree, providing reliable constraint parameters for the subsequent calculation of cervical spine alignment and adaptation benchmark values.

[0039] The existing cervical spine alignment and adaptation benchmark calculation scheme does not integrate multi-dimensional parameters such as anatomical features, muscle tension, degenerative changes, and body proportions, and the quantitative logic is unclear, resulting in a lack of scientific basis for the adaptation benchmark and failing to provide an accurate foundation for subsequent dynamic pressure distribution and support height calculation.

[0040] Based on this, the cervical spine alignment and adaptation benchmark value is calculated by the cervical spine alignment and adaptation benchmark value formula, which integrates anatomical features, muscle tension, degenerative changes and body proportion parameters to achieve dimensionless benchmark value quantification.

[0041] This technical solution achieves the coupling and quantification of multi-dimensional parameters through a proprietary formula, ensuring the scientific validity and accuracy of the cervical spine alignment benchmark values. It is worth noting that the formula for the cervical spine alignment benchmark values ​​is as follows:

[0042] ;

[0043] in, The cervical spine alignment and fitting reference value is dimensionless and is used to characterize the basic fitting threshold for maintaining the physiological curvature of the cervical spine. The anatomical feature weighting coefficient is dimensionless and was obtained by fitting data from a fitting experiment of 300 subjects with different cervical spine anatomical features. Its value ranges from 0.6 to 0.8, for example, for subjects with normal cervical curvature. The value was 0.7, for subjects with cervical lordosis reversal. A value of 0.8 is used to adjust the degree of influence of anatomical features on the adaptation benchmark; Cervical curvature index, unit: It is calculated by scanning the sagittal profile of the cervical spine using the C-Scan system, reflecting the degree of curvature of the cervical spine, such as the normal cervical spine. The value is approximately 0.005. When the cervical spine arches backward The value can be negative; Neck length, measured in meters, is obtained via laser ranging using a C-Scan system. It reflects the overall length of the neck and is suitable for adult males. The average length is approximately 0.15 meters for adult women. The average length is approximately 0.14 meters. The muscle tension influence coefficient is dimensionless and derived from pressure adaptation data of 200 subjects with different muscle tension states. Its value ranges from 0.5 to 0.7. For example, for subjects with high muscle tension... A value of 0.7 was taken from subjects with normal muscle tone. A value of 0.6 is used to adjust the constraint effect of muscle tension on the adaptation reference. The average tension of neck muscles, measured in Pascals, is obtained through electromyography (EMG) signal acquisition and is measured under normal resting conditions. The average value is approximately 200 Pascals, when muscles are tense. It can rise to over 300 Pascals; This is a baseline value for muscle tension, measured in Pascals. It is taken as the average resting tension of the neck muscles in healthy adults aged 18 to 45, which is 200 Pascals. It is used to... Convert to dimensionless ratios to ensure consistent dimensions in the formulas; The coefficient representing the influence of degenerative changes is dimensionless and derived from clinical data of 150 patients with different degrees of degenerative changes. Its value ranges from 0.7 to 0.9, for example, in subjects with severe degenerative changes. A value of 0.9 indicates mild degenerative changes in the subjects. The value is 0.7, which is used to adjust the constraint effect of the degenerative variation on the adaptation reference; The coefficient for cervical spondylosis is dimensionless and is obtained by quantifying and weighting the degree of intervertebral space narrowing and the size of osteophytes. Its value ranges from 0 to 2.5, with higher values ​​indicating more severe degeneration. The larger the value; This is a dimensionless body proportion correction coefficient, obtained by fitting feedback data from 250 subjects with different shoulder-neck ratios. Its value ranges from 0.2 to 0.4. For example, for subjects with a larger shoulder-to-neck ratio... A value of 0.4 indicates a smaller ratio in the subjects. A value of 0.2 is used to correct the impact of body proportions on the adaptation benchmark; This is the exponential correction coefficient, dimensionless, obtained based on correlation analysis between body proportion and fit deviation, with a value ranging from 0.3 to 0.5. For example... When the value is 0.4, When the index is increased from 2 to 4, the correction margin of the benchmark value increases from 0.15 to 0.25. This is the ratio of shoulder width to neck width, dimensionless, calculated through 3D scanning measurement, for adult males. The mean is approximately 3.3 for adult women. The mean is approximately 3.1. The calculation process of the formula is executed by the processor of the computing unit, and is updated every 10 seconds based on the latest collected multi-dimensional core data. The value ensures that the adaptation benchmark can reflect changes in the subject's physiological state in real time.

[0044] This formula is based on the core principles of cervical spine biomechanics. The essence of cervical spine adaptation lies in the synergistic relationship between balanced anatomical foundations, dynamic muscle support, degenerative tolerance, and body type differences. The neck muscles, acting as a stabilizing band for the cervical spine, directly affect the force balance of the cervical spine due to changes in tension. Cervical degeneration reduces vertebral tolerance, necessitating a reduction in the weight of basic anatomical parameters. Body type differences indirectly affect the adaptation effect through pressure distribution, and this effect exhibits a non-linear, diminishing marginal effect, consistent with ergonomic principles.

[0045] The logical derivation process is as follows: Coupling of core anatomical parameters: cervical curvature index With neck length This is the core factor determining the adaptation benchmark. The product of these two factors comprehensively represents the basic adaptation requirements of the cervical spine's physiological structure, multiplied by the anatomical weighting coefficient. Based on extensive experimental fitting, the dominance of anatomical features is ensured, forming the molecular part of the formula. Constraint factors are integrated: muscle tension. After normalization, divide by the baseline value Combined with muscle influence coefficient This reflects the constraint of muscle tension on adaptation—higher muscle tension results in a larger denominator, weakening the contribution of basic anatomical parameters and preventing excessive support that could lead to muscle fatigue; cervical spine degenerative coefficient. With the degradation effect coefficient Coupling: The more severe the degeneration, the larger the denominator, and the more conservative the fitting benchmark, which aligns with the physiological law of decreased tolerance in degenerated cervical vertebrae. The additive effect of both factors, due to the synergistic effect of muscle tension and degeneration on the fitting, contributes to this. Body shape correction supplement: Shoulder width to neck width ratio. The impact on adaptation is not linear, so an exponential term is used. To simulate the diminishing marginal returns effect and avoid fit bias caused by extreme body size values, a body size correction factor is applied. This process supplements the impact of individual body shape differences on the adaptation benchmark, ultimately forming a complete quantitative formula for the benchmark value.

[0046] The existing dynamic pressure distribution coefficient calculation scheme does not link the cervical spine alignment adaptation benchmark value with dynamic posture parameters, and does not consider the impact of pressure distribution uniformity on adaptation, resulting in a mismatch between pressure distribution and cervical spine physiological needs in dynamic scenarios, affecting the cervical curvature maintenance effect.

[0047] Based on this, the dynamic pressure distribution coefficient is calculated by the dynamic pressure distribution optimization formula. The dynamic pressure distribution optimization formula is based on the cervical spine alignment adaptation benchmark value, integrates the dynamic posture parameters and the pressure distribution uniformity parameters, and outputs the distribution coefficient of the corresponding pressure unit.

[0048] This technical solution uses a proprietary formula to couple the baseline value with dynamic parameters, ensuring the accuracy of dynamic pressure distribution.

[0049] It is worth mentioning that the dynamic pressure distribution optimization formula is:

[0050]

[0051] in, The dynamic pressure distribution coefficient, in Pascals, is used to characterize the optimal contact pressure required by the cervical spine under different sleeping postures. The pressure benchmark is measured in Pascals and is set at 300 Pascals, representing the average head-neck contact pressure required to maintain the physiological lordosis of the cervical spine. This value was determined based on a cervical spine biomechanical experiment involving 50 healthy adults to ensure that the pressure benchmark meets physiological requirements. The cervical spine alignment and adaptation benchmark value is dimensionless. The latest calculation results are retrieved from the model building module to provide a basic adaptation basis for dynamic pressure distribution. The weighting coefficient for posture influence is dimensionless and obtained from pressure adaptation experiments under different sleep postures. Its value ranges from 0.4 to 0.6, for example, in the supine position. The value is 0.4, when in a side-lying position. The value is 0.6, which is used to adjust the degree of influence of dynamic attitude parameters on pressure distribution; This refers to the sleep posture angle, measured in radians, acquired using a posture capture device while lying supine. The average value is approximately 0.174 radians (i.e., 10 degrees) when lying on one's side. The average value is approximately 0.873 radians (i.e., 50 degrees). The attitude switching rate is measured in radians per second, obtained through continuous data acquisition. The change in volume is calculated by dividing the time interval; this occurs when slowly turning over during sleep. The average value is approximately 0.01 radians per second, during rapid rolling over. It can reach 0.05 radians per second; The standard posture transition rate, measured in radians per second, is based on a statistical mean of 0.02 radians per second derived from the sleep posture transition rates of 100 healthy adults. Convert to a dimensionless ratio to eliminate the influence of the absolute value of the rate on the formula; The standard deviation of the maximum pressure, expressed in Pascals, is calculated from the pressure data collected by the pressure sensor array of the C-Scan system. It reflects the degree of dispersion in the pressure distribution. A smaller value indicates a more uniform pressure distribution; for example, a uniform pressure distribution... Approximately 20 Pascals, when non-uniform It can exceed 50 Pascals; The standard deviation of the average pressure, expressed in Pascals, is calculated from the pressure data collected by the pressure sensor array of the C-Scan system. It helps characterize the stability of the pressure distribution. The smaller the value, the more stable the pressure distribution; The duration of the posture, in seconds, is recorded by a posture capture device during the supine position during sleep. The average duration is approximately 300 seconds in the side-lying position. The average duration is approximately 200 seconds; The baseline posture duration, in seconds, is based on an average duration of 300 seconds for each posture during sleep in 100 healthy adults. The formula is converted to a dimensionless ratio to balance the effect of attitude duration on pressure distribution. The calculation process is handled in parallel by the GPU of the computing unit, updated every 5 seconds based on the latest attitude and pressure data. This value ensures that dynamic pressure distribution can respond to changes in sleep posture in real time.

[0052] The dynamic pressure distribution formula needs to respond to real-time changes in sleep posture while ensuring pressure uniformity and stability, which conforms to the principle of load and posture coordination in dynamic mechanics. Pressure uniformity is key to cervical spine adaptation; uneven stress distribution can lead to localized compression. The posture switching rate reflects the dynamic response requirements, and the duration affects the stability of pressure adaptation. This design conforms to the dynamic stress characteristics of the cervical spine during sleep.

[0053] The logical derivation process is as follows: Basic reference coupling: reference pressure to maintain the physiological curvature of the cervical spine. Based on, multiplied by the cervical spine alignment and adaptation benchmark value This ensures that dynamic pressure always aligns with individual basic adaptation needs and avoids deviating from individual physiological characteristics.

[0054] Dynamic posture adaptation: Sleep posture angle Using sine function transformation, because The angle between the cervical spine axis and the horizontal plane. It can accurately reflect the vertical force component, closely matching the influence of attitude changes on pressure; attitude switching rate After normalization, divide by the standard rate Eliminate absolute value differences and multiply by attitude weight coefficient. , and pressure uniformity term ( This forms a balance relationship, adapting to the priority differences in dynamic parameters and pressure uniformity under different postures.

[0055] Pressure uniformity correction: standard deviation of maximum pressure With mean pressure standard deviation The ratio directly characterizes the dispersion of pressure distribution. The smaller the ratio, the more uniform the pressure, which is consistent with the conclusion in stress distribution uniformity studies that "uniform stress reduces the risk of local damage" and ensures that the pressure distribution in dynamic scenarios conforms to the physiological needs of the cervical spine.

[0056] Duration Adjustment: Attitude Duration After normalization, take the square root and divide by the baseline duration. The subsequent opening method, because the effect of duration on pressure has a diminishing marginal return, avoids excessive pressure correction due to excessively long or short time periods, thus balancing dynamic adaptation and stability.

[0057] The existing real-time optimal support height calculation scheme does not integrate dynamic pressure distribution coefficient, long-term usage trend and historical attitude deviation data, which makes it impossible to take into account both short-term dynamic adaptation and long-term physiological changes in support height. After long-term use, the adaptation accuracy has decreased significantly.

[0058] Based on this, the real-time optimal support height is calculated using a dynamic adjustment formula. This formula combines the dynamic pressure distribution coefficient, long-term usage trends, and historical posture deviation data to output a support height value for the corresponding length unit. This technical solution achieves coordinated adjustment of multiple factors through a dedicated formula, ensuring both short-term accuracy and long-term stability of the real-time optimal support height. It is worth noting that the dynamic adjustment formula for support height is as follows:

[0059] ;

[0060] in, This is the real-time optimal support height, expressed in meters, used to guide the adjustment of the support structure of custom pillows or neck braces, such as those used by subjects with normal cervical spines. The mean was approximately 0.1 meters, for subjects with cervical lordosis reversal. The average length is approximately 0.12 meters. The initial support height, in meters, is determined by a fitted formula based on the cervical curvature index and neck length. Calculated, for example , Rice time, Meters provide an initial benchmark for subsequent dynamic adjustments; The dynamic pressure distribution coefficient, in Pascals, is obtained from the latest calculation results of the dynamic optimization unit, reflecting the real-time pressure adaptation requirements. This is a pressure reference value, in Pascals, with a value of 300 Pascals, used to... Convert to dimensionless ratios to ensure consistent dimensions in the formulas; This is a long-term adjustment coefficient, dimensionless, obtained by fitting correlation data between changes in cervical curvature and support height adjustments over a 6-month usage period. The value ranges from 0.05 to 0.1. For example, for subjects whose cervical curvature improves more quickly... A value of 0.1 indicates slow improvement in subjects. A value of 0.05 is used to adjust the effect of long-term physiological changes on the support height; The usage duration is measured in days and is recorded by the system's timing module as the cumulative number of days since the first use. For example, if the usage lasts for 30 days... ; This represents the daily average change in cervical curvature index, in units of... The cervical curvature index is calculated by averaging the differences collected daily over seven consecutive days. For example, when the cervical curvature improves... It is a positive value when the condition worsens and a negative value when the condition deteriorates. This is the height compensation coefficient, measured in meters, derived from fitting clinical data of historical posture deviation and height adjustment. Its value ranges from 0.01 to 0.03 meters. For example, for subjects with larger historical deviations... The value is set to 0.03 meters, and 0.01 meters when the deviation is small, to compensate for the impact of historical attitude deviation on the support height. For the first time in history The pressure distribution coefficient for each attitude, in Pascals, is retrieved from the data storage module from the last 30 attitudes. Values ​​are used as historical data; The historical pressure distribution coefficient is the average value, expressed in Pascals, calculated from the last 30 pressure distributions. The arithmetic mean is obtained, reflecting the average level of historical stress adaptation; For the first time in history The angle difference between the secondary posture and the reference posture, in radians, is set to the supine position. radians, the first in history Second-rate Compared with the benchmark The difference is For example, when lying on your side It is a positive value; The number of historical attitude records is a positive integer, typically 30, to ensure statistical representativeness of the historical data. The calculation process is executed by the XC7K325T FPGA chip of the real-time drive correction unit, updated every 2 seconds based on the latest data. The calculation latency is controlled within 1 millisecond to ensure real-time support for height adjustment.

[0061] The core requirement of this formula for support height is to balance short-term dynamic adaptation with long-term physiological changes, which aligns with the optimization logic of real-time response, trend prediction, and error compensation in feedback control theory. The cervical curvature gradually improves or changes with long-term use, and historical posture deviations accumulate adaptation errors. Therefore, multi-dimensional adjustments are needed to ensure accurate adaptation throughout the entire lifecycle. This aligns with the common-sense principle that the support height must dynamically match the physiological state of the cervical spine.

[0062] The logical derivation of this formula is as follows: Short-term dynamic adjustment: Initial support height Determined based on anatomical parameters, multiplied by the dynamic pressure distribution coefficient. Compared with reference pressure The ratio directly translates real-time pressure requirements into height adjustment, rapidly responding to dynamic changes in sleep posture and pressure distribution to meet short-term adaptation needs. Long-term physiological adaptation: usage duration. Daily average change in cervical curvature index The product of these factors reflects the cumulative changes in the physiological state of the cervical spine, using a natural logarithmic function. To avoid excessive adjustments, the adjustment should conform to the physiological principle of gradual changes in cervical curvature; multiply by a long-term adjustment coefficient. This ensures that the long-term adjustment rate matches the individual's improvement pace, avoiding over-intervention. Historical bias compensation: Cumulative historical [number missing] Sub-attitude pressure distribution coefficient deviation ,in The historical pressure distribution coefficient mean and attitude angle difference The product of these factors is used to extract systematic errors from past adaptations, and then multiplied by a high compensation coefficient. This enables iterative error correction; the nearest error is selected. By using historical data, we can ensure the statistical representativeness of deviation compensation, avoid the influence of random errors, and ultimately form a highly sophisticated computational logic that takes into account short-term, long-term and historical optimization.

[0063] The existing trend analysis scheme for the daily average change in cervical curvature index does not specify the algorithm type and does not set an update trigger threshold, which makes it impossible to respond in a timely manner to significant changes in the physiological state of the cervical spine. After long-term use, the deviation between the adaptation parameters and actual needs accumulates.

[0064] Based on this, the trend analysis of the daily average change in cervical curvature index is achieved through a linear regression algorithm. This algorithm uses the usage duration as the independent variable and the daily average change in cervical curvature index as the dependent variable. When the absolute value of the daily average change in cervical curvature index exceeds a preset threshold, an immediate update is triggered. This immediate update includes recalculating the cervical alignment adaptation benchmark value, the dynamic pressure distribution coefficient, and the real-time optimal support height. This technical solution, by clearly defining the trend analysis algorithm and the update triggering mechanism, ensures that the adaptation parameters can respond promptly to physiological changes in the cervical spine.

[0065] It is worth mentioning that the linear regression algorithm is implemented using the least squares method, first collecting daily usage time. and the corresponding daily average change in cervical curvature index Build dataset ,in The number of days for data collection is typically between 7 and 30 days; then, a linear regression equation is fitted based on this dataset. ,in The slope reflects Follow The changing trend The intercept reflects the initial state. Level; this equation can predict the level of a certain period of time in the future. Changes provide a basis for updating adaptation parameters. The preset threshold is set based on clinical data; by analyzing the correlation between changes in cervical curvature and discomfort symptoms in 100 subjects, the appropriate threshold is determined. The absolute value exceeds 0.05 At this time, the physiological state of the cervical spine changes significantly, requiring an immediate update. The immediate update process involves the parameter update module sending an update command to the model building module, which then retrieves the latest multi-dimensional core data and recalculates. , , The updated parameters are transmitted to the personalized execution module via the data bus. The flexible adjustment component of the personalized execution module adjusts according to the new parameters. Adjust the support height, for example When adjusted from 0.1 meters to 0.11 meters, the inflation volume of the inflatable chamber of the flexible adjustment component increases by 10%. The regular update cycle is set at 30 days, even... Even if the threshold is not exceeded, the data will be readjusted based on the trend curve over the past 30 days. , , The calculation coefficients, for example , The coefficients are fine-tuned based on the slope of the trend curve to ensure that the fitting parameters fit the physiological state of the cervical spine in the long term.

[0066] The existing cervical spine alignment pressure model customization system has a vague module division, unclear functional definitions of each module, and lacks data interaction logic, which makes the system unable to efficiently coordinate and execute the customization method, affecting the customization accuracy and efficiency.

[0067] Based on this, this embodiment provides a cervical spine alignment pressure model customization system based on the C-Scan system, used to execute the cervical spine alignment pressure model customization method based on the C-Scan system described above. The system includes a data acquisition module, a model construction module, a support height calculation module, and a parameter update module. The data acquisition module is used to collect multi-dimensional core data of the subjects. The model construction module is used to construct cervical spine alignment adaptation benchmark values ​​and dynamic pressure distribution coefficients based on the multi-dimensional core data. The support height calculation module is used to calculate the real-time optimal support height based on the dynamic pressure distribution coefficient, initial support height, usage time, daily average change in cervical curvature index, deviation of the pressure distribution coefficient of each historical posture from the average historical pressure distribution coefficient, and angle difference between each historical posture and the benchmark posture. The parameter update module is used to store the multi-dimensional core data, the cervical spine alignment adaptation benchmark values, the dynamic pressure distribution coefficient, and the real-time optimal support height, analyze the trend of the daily average change in cervical curvature index, and update the above parameters. This technical solution, through clear module division and explicit functional definition, ensures efficient collaboration among the system modules and achieves accurate execution of the customization method. It is worth mentioning that the data acquisition module includes a C-Scan submodule, a myoelectronic module, an attitude submodule, and an imaging submodule. Each submodule is connected to the computing unit via a PCIe bus, with a data transmission rate of no less than 1GB per second. The C-Scan submodule integrates a 256-point pressure sensor array and a high-precision displacement sensor. The pressure sensor array has a sampling frequency of 100 Hz, and the displacement sensor has a resolution of 0.01 mm, used for data acquisition. , Pressure data, displacement data; the electromyography module is equipped with four surface electromyography electrodes with a sampling frequency of 200 Hz for data acquisition. The attitude submodule deploys two infrared motion capture cameras with a sampling frequency of 30 Hz for data acquisition. , , The imaging submodule connects to an X-ray machine or CT scanner via a DICOM interface to acquire data on the degree of intervertebral space stenosis and the size of osteophytes. The model building module includes a baseline calculation unit and a dynamic optimization unit. The baseline calculation unit runs the cervical spine alignment and adaptation baseline formula, while the dynamic optimization unit runs the dynamic pressure distribution optimization formula. The computation unit uses an Intel Core i9-13900K processor and an NVIDIA RTX 4090 graphics card. The processor handles data scheduling, and the graphics card handles parallel calculations of the formulas, ensuring... and The calculation latency is less than 10 milliseconds and 5 milliseconds respectively. The support height calculation module uses the FPGA chip XC7K325T, runs the dynamic adjustment formula for support height, and has a calculation latency of less than 1 millisecond, with the output... The values ​​are transmitted via serial port to the customized execution module, which controls the flexible adjustment component to adjust the support structure. The parameter update module includes a data storage unit, a trend analysis unit, and a parameter update submodule. The data storage unit uses a 1TB solid-state drive with a read / write speed of no less than 500MB / s, storing multi-dimensional core data. , , Historical records; the trend analysis unit runs a linear regression algorithm to analyze... The parameter update submodule triggers immediate or regular updates based on the trend analysis results. The update command is transmitted to the model building module and the support height calculation module via TCP / IP protocol to ensure that the parameters of each module are updated synchronously.

[0068] The existing data acquisition module has unclear internal sub-module divisions, and some sub-modules do not fully cover the functions, resulting in omissions or duplications in the acquisition of multi-dimensional core data, and the data accuracy cannot meet the needs of model calculation.

[0069] Based on this, the data acquisition module includes a C-Scan submodule, a myoelectronic module, a posture submodule, and an imaging submodule. The C-Scan submodule is used to acquire the cervical curvature index, neck length, pressure data, and displacement data. The myoelectronic module is used to acquire the average tension of the neck muscles. The posture submodule is used to acquire the sleep posture angle, posture switching rate, and posture duration. The imaging submodule is used to acquire the degree of intervertebral space narrowing and osteophyte size of the subject to calculate the cervical degenerative coefficient. This technical solution ensures accurate acquisition and complete coverage of multi-dimensional core data by subdividing the submodules and clarifying the exclusive functions of each submodule. It is worth mentioning that the pressure sensor array of the C-Scan submodule uses piezoresistive pressure sensors with a sensor point spacing of 5 mm, covering a 200 mm × 150 mm head and neck contact area. The collected pressure data is converted into digital signals by a 16-bit analog-to-digital converter, ensuring a pressure measurement accuracy of ±2 Pascals. The displacement sensor uses a laser displacement sensor with a measurement range of 0 to 500 mm and a measurement accuracy of ±0.01 mm. When scanning the sagittal plane of the cervical spine, a step-by-step scanning method is used with a step size of 1 mm to ensure... and The measurement accuracy is high. The surface electromyography (EMG) electrodes of the EMG module are made of Ag / AgCl material, with an electrode diameter of 8 mm and a skin contact impedance of less than 5 kΩ. The acquired EMG signals are amplified 1000 times by a differential amplifier circuit, then filtered by a 50 Hz notch filter and a 0.5 to 500 Hz bandpass filter to remove interference. Finally, the EMG signals are converted into EMG signals using an EMG-tension conversion algorithm. The conversion error is controlled within ±5 Pascals. The infrared motion capture camera in the attitude submodule uses a 1280×1024 resolution CMOS sensor with a frame rate of 30 frames per second. The infrared markers are 6 mm diameter reflective spheres attached to the surfaces of the C3, C5, and C7 cervical vertebrae. The camera calculates the three-dimensional coordinates of the markers using triangulation, thereby obtaining... , , , The measurement accuracy is ±0.01 radians. The measurement accuracy is ±0.001 radians per second. The timing accuracy is ±0.1 seconds. The X-ray machine in the imaging submodule uses a high-frequency digital X-ray machine with a tube voltage of 60 to 80 kV and a tube current of 10 to 30 mA. The lateral cervical spine radiographs have a resolution of 300 dpi. The intervertebral disc height is measured using image measurement software with an accuracy of ±0.1 mm. The CT equipment uses a 64-slice spiral CT scanner with a slice thickness of 0.625 mm and a reconstruction interval of 0.625 mm. The size of osteophytes is measured using multiplanar reconstruction technology with an accuracy of ±0.1 mm. The acquired data from each submodule is transmitted to the STM32F407 microcontroller in the data acquisition module via an I2C bus. The microcontroller performs time synchronization on the data, ensuring that the timestamp deviation of each data point is less than 1 millisecond. The synchronized data is then transmitted to the computing unit via Ethernet, providing a precise and complete data foundation for subsequent model calculations.

[0070] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for customizing a cervical spine alignment pressure model based on the C-Scan system, characterized in that, Includes the following steps: S1. Collect multi-dimensional core data of the subjects, including cervical curvature index, neck length, average tension of neck muscles, coefficient of cervical degeneration based on the customized method of cervical alignment pressure model of C-Scan system, sleep posture angle, posture switching rate, posture duration, shoulder width to neck width ratio, pressure data and displacement data. The coefficient of cervical degeneration is calculated based on the degree of intervertebral space narrowing and the size of osteophytes of the subjects. S2. Based on the cervical curvature index, the neck length, the average tension of the neck muscles, the cervical degeneration coefficient, and the ratio of shoulder width to neck width, construct the cervical spine alignment and adaptation benchmark value. S3. Based on the cervical spine alignment and adaptation benchmark value, the sleep posture angle, the posture switching rate, the posture duration, and the pressure data, the maximum pressure standard deviation and the average pressure standard deviation are calculated to construct a dynamic pressure distribution coefficient. S4. Based on the dynamic pressure distribution coefficient, initial support height, usage time, daily average cervical curvature index change, deviation of the pressure distribution coefficient of each historical posture from the average historical pressure distribution coefficient, and angle difference between each historical posture and the reference posture, the real-time optimal support height is calculated. The initial support height is initially set based on the cervical curvature index and the neck length. S5. Store the multi-dimensional core data, the cervical spine alignment and adaptation benchmark value, the dynamic pressure distribution coefficient, and the real-time optimal support height. Analyze the trend of the daily average cervical curvature index change based on the usage time, and update the cervical spine alignment and adaptation benchmark value, the dynamic pressure distribution coefficient, and the real-time optimal support height.

2. The method for customizing a cervical spine alignment pressure model based on the C-Scan system according to claim 1, characterized in that, The multidimensional core data is acquired through a C-Scan system, an electromyography (EMG) signal acquisition device, a posture capture device, and an imaging device. The C-Scan system is used to acquire the cervical curvature index, the neck length, the pressure data, and the displacement data. The EMG signal acquisition device is used to acquire the average tension of the neck muscles. The posture capture device is used to acquire the sleep posture angle, the posture switching rate, and the posture duration. The imaging device is used to acquire the degree of intervertebral disc stenosis and the size of the osteophytes.

3. The method for customizing a cervical spine alignment pressure model based on the C-Scan system according to claim 1, characterized in that, The shoulder width to neck width ratio is obtained by scanning the contours of the subject's shoulders and neck. Specifically, the straight-line distance at the widest point of the shoulder is taken as the shoulder width, and the horizontal straight-line distance in the middle of the neck is taken as the neck width. The ratio of the shoulder width to the neck width is then calculated to obtain the shoulder width to neck width ratio.

4. The method for customizing a cervical spine alignment pressure model based on the C-Scan system according to claim 1, characterized in that, The cervical degenerative coefficient is calculated by quantifying and assigning values ​​to the degree of intervertebral space narrowing and the size of osteophytes, and then obtaining the cervical degenerative coefficient by weighted summation. The weights of the weighted summation are determined based on clinical data.

5. The method for customizing a cervical spine alignment pressure model based on the C-Scan system according to claim 1, characterized in that, The cervical spine alignment and adaptation benchmark value is calculated using the cervical spine alignment and adaptation benchmark value formula, which integrates anatomical features, muscle tension, degenerative changes, and body proportion parameters to achieve dimensionless benchmark value quantification.

6. The method for customizing a cervical spine alignment pressure model based on the C-Scan system according to claim 5, characterized in that, The dynamic pressure distribution coefficient is calculated using a dynamic pressure distribution optimization formula. This formula is based on the cervical spine alignment and adaptation benchmark value, integrates dynamic posture parameters and pressure distribution uniformity parameters, and outputs the distribution coefficient for the corresponding pressure unit.

7. The method for customizing a cervical spine alignment pressure model based on the C-Scan system according to claim 6, characterized in that, The real-time optimal support height is calculated using a dynamic adjustment formula for support height. This formula combines the dynamic pressure distribution coefficient, long-term usage trends, and historical posture deviation data to output a support height value in the corresponding length unit.

8. The method for customizing a cervical spine alignment pressure model based on the C-Scan system according to claim 1, characterized in that, The trend analysis of the daily average change in cervical curvature index is achieved by a linear regression algorithm, which uses the usage duration as the independent variable and the daily average change in cervical curvature index as the dependent variable. When the absolute value of the daily average change in cervical curvature index exceeds a preset threshold, an instant update is triggered. The instant update includes recalculating the cervical alignment adaptation benchmark value, the dynamic pressure distribution coefficient, and the real-time optimal support height.

9. A cervical spine alignment pressure model customization system based on the C-Scan system, characterized in that, The method for customizing a cervical spine alignment pressure model based on the C-Scan system according to any one of claims 1 to 8, wherein the system includes a data acquisition module, a model building module, a support height calculation module, and a parameter update module; the data acquisition module is used to collect multi-dimensional core data of the subjects; the model building module is used to construct cervical spine alignment adaptation benchmark values ​​and dynamic pressure distribution coefficients based on the multi-dimensional core data; The support height calculation module is used to calculate the real-time optimal support height based on the dynamic pressure distribution coefficient, initial support height, usage time, daily average change in cervical curvature index, deviation of the pressure distribution coefficient of each historical posture from the average historical pressure distribution coefficient, and angle difference between each historical posture and the reference posture; the parameter update module is used to store the multi-dimensional core data, the cervical spine alignment and adaptation reference value, the dynamic pressure distribution coefficient, and the real-time optimal support height.

10. The cervical spine alignment pressure model customization system based on the C-Scan system according to claim 9, characterized in that, The data acquisition module includes a C-Scan submodule, a myoelectronic module, a posture submodule, and an imaging submodule; the C-Scan submodule is used to acquire the cervical curvature index, the neck length, the pressure data, and the displacement data; the myoelectronic module is used to acquire the average tension of the neck muscles; the posture submodule is used to acquire the sleep posture angle, the posture switching rate, and the posture duration; The imaging submodule is used to acquire the degree of intervertebral space narrowing and the size of osteophytes in the subject in order to calculate the cervical degenerative coefficient.

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