Method and device for measuring scoliosis

Abstract

The invention discloses a scoliosis measuring method and device, and the method comprises the steps: S1, carrying out the collection of an electromyographic signal set of a user through a patch electrode disposed on the spine of the user; s2, acquiring a discrete point position information set by using a position measurement module arranged on the spine of the user; the discrete point position information set comprises a position information sequence measured by each position measurement module; and S3, performing scoliosis measurement processing on the electromyographic signal set and the discrete point position information set to obtain a scoliosis measurement information set of the user.

Description

Technical Field

[0001] The present invention relates to the fields of medical equipment and medical signal processing, and in particular to a method and device for measuring scoliosis. Background Art

[0002] Currently, the mainstream clinical methods for measuring scoliosis rely mainly on X-rays and CT scanning technologies. Through these imaging methods, doctors can intuitively obtain the morphological structure of the spine, thereby determining the degree and type of scoliosis. However, these methods have obvious limitations. On the one hand, X-rays and CT scans produce ionizing radiation, and frequent use can pose potential hazards to human health, especially for patients who require long-term monitoring of scoliosis, such as adolescents. Long-term radiation exposure may increase the risk of diseases such as cancer. On the other hand, such tests require patients to go to the hospital and be completed on specific equipment. The process is relatively cumbersome and cannot meet the user's needs for real-time and convenient monitoring of spinal curvature, and it is impossible to achieve daily dynamic tracking of scoliosis.

[0003] Furthermore, while existing non-invasive measurement methods, such as surface measurement, avoid radiation risks, they suffer from low accuracy and are susceptible to interference from factors such as body shape and measurement posture. Furthermore, some wearable devices, due to their limited measurement capabilities, fail to fully and accurately reflect the actual condition of scoliosis, making it difficult to meet the needs of clinical diagnosis and personalized treatment. Therefore, developing a non-invasive, high-precision scoliosis measurement method and device suitable for real-time monitoring and long-term use has become a critical issue in the fields of medical devices and medical signal processing. Summary of the Invention

[0004] The present invention mainly solves the problem of how to perform real-time and accurate measurement of the spine, and discloses a method and device for measuring scoliosis.

[0005] In a first aspect of an embodiment of the present invention, a method for measuring scoliosis is disclosed, comprising:

[0006] S1, using patch electrodes set on the user's spine to collect the user's electromyographic signal set;

[0007] S2, using a position measurement module disposed on the user's spine to collect a discrete point position information set; the discrete point position information set includes a position information sequence measured by each position measurement module;

[0008] S3, performing scoliosis measurement processing on the electromyographic signal set and the discrete point position information set to obtain a scoliosis measurement information set of the user.

[0009] The step of performing scoliosis measurement processing on the electromyographic signal set and the discrete point position information set to obtain a scoliosis measurement information set of the user includes:

[0010] S31, preprocessing the electromyographic signal set and the discrete point position information set to obtain a preprocessed information set;

[0011] S32, performing modal abnormality discrimination processing on the electromyographic signal set in the pre-processing information set to obtain a modal discrimination value;

[0012] S33, determining whether the modal discrimination value is greater than a preset first discrimination threshold, and obtaining a first discrimination result;

[0013] If the first judgment result is no, execute S32;

[0014] If the first discrimination result is yes, scoliosis calculation processing is performed on the discrete point position information set in the pre-processed information set to obtain the user's scoliosis measurement information set.

[0015] The preprocessing of the electromyographic signal set and the discrete point position information set to obtain a preprocessed information set includes:

[0016] S311, performing data cleaning processing on the electromyographic signal set and the discrete point position information set to obtain a first information set;

[0017] S312, performing data category check processing on the first information set to obtain a second information set;

[0018] S313: Perform deviation discrimination processing on the second information set to obtain a preprocessed information set.

[0019] The performing modal abnormality discrimination processing on the electromyographic signal set in the pre-processing information set to obtain a modal discrimination value includes:

[0020] S321, performing feature transformation on each electromyographic signal in the electromyographic signal set in the preprocessing information set to obtain a corresponding feature signal;

[0021] S322, performing characteristic integral calculation on each electromyographic signal in the electromyographic signal set in the preprocessing information set to obtain a corresponding characteristic value;

[0022] S323: Perform modal anomaly calculation processing on all myoelectric signals and corresponding characteristic signals and characteristic values ​​to obtain a modal discrimination value.

[0023] The expression for calculating the characteristic integral is:

[0024]

[0025] Among them, α is the characteristic value, f(t) is the value of the electromyographic signal at time t, and [0, T] is the value range of time t;

[0026] The expression for the modal abnormality calculation process is:

[0027]

[0028] Among them, yc1 is the modal discriminant value, M is the total number of myoelectric signals in the myoelectric signal set, and f i (t) is the i-th EMG signal in the EMG signal set, μ i (t) is the characteristic signal corresponding to the i-th EMG signal in the EMG signal set, f0(t) is the average signal of all EMG signals in the EMG signal set, and μ0(t) is the average signal of the characteristic signals corresponding to all EMG signals in the EMG signal set.

[0029] The step of performing scoliosis calculation processing on the discrete point position information set in the pre-processed information set to obtain the user's scoliosis measurement information set includes:

[0030] S331, calculating a straight line deviation value for the discrete point position information set in the preprocessing information set to obtain a straight line deviation value;

[0031] S332, calculating an angle deviation value for the discrete point position information set in the pre-processing information set to obtain an angle deviation value;

[0032] S333, performing scoliosis degree assessment processing on the straight line deviation value and the angle deviation value to obtain a scoliosis assessment value;

[0033] S334: Utilize the straight line deviation value, angle deviation value and scoliosis assessment value to construct a scoliosis measurement information set of the user.

[0034] A second aspect of the embodiments of the present invention discloses a scoliosis measurement device for implementing the scoliosis measurement method, comprising: patch electrodes, a position measurement module, and a scoliosis measurement and evaluation module;

[0035] The patch electrodes are placed on the user's spine to collect the user's electromyographic signals.

[0036] The position measurement module is arranged at the user's spine position and is used to collect and obtain a set of discrete point position information;

[0037] The scoliosis measurement and evaluation module is connected to the patch electrode and the position measurement module respectively, and is used to perform scoliosis measurement processing on the electromyographic signal set and the discrete point position information set to obtain the user's scoliosis measurement information set.

[0038] A third aspect of the present invention discloses a scoliosis measurement device, comprising:

[0039] a memory storing executable program code;

[0040] a processor coupled to the memory;

[0041] The processor calls the executable program code stored in the memory to execute the scoliosis measurement method.

[0042] According to a fourth aspect of an embodiment of the present invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions. When the computer instructions are called by a computer, the computer instructions are used to execute the method for measuring scoliosis.

[0043] According to a fifth aspect of the embodiments of the present invention, an information data processing terminal is disclosed, which is used to implement the scoliosis measurement method.

[0044] The beneficial effects of the present invention are:

[0045] The scoliosis measurement method and device proposed in the present invention use patch electrodes to collect electromyographic signals and position measurement modules to obtain discrete point position information in terms of measurement methods, completely abandoning the radiation measurement methods of traditional X-rays and CT scans, fundamentally eliminating the potential threat of radiation to human health, greatly improving the safety of measurement, and are especially suitable for special groups such as teenagers who need long-term monitoring. The measurement method and device are highly convenient and real-time. The wearable design of the patch electrodes and position measurement modules eliminates the need for users to frequently go to the hospital, and can monitor scoliosis at any time in daily life scenarios, thereby achieving dynamic tracking of the health status of the spine. Through the real-time collection of electromyographic signal sets and discrete point position information sets, a scoliosis measurement information set can be quickly obtained, providing a strong basis for users and doctors to promptly grasp changes in the disease.

[0046] In terms of measurement accuracy, the present invention significantly improves measurement accuracy through multi-step, refined processing of collected signals and information. By performing feature transformations such as variational modal decomposition on the EMG signal set, combined with unique feature integral calculations and modal anomaly calculation processing, the system can accurately determine the modal state of the EMG signal and effectively eliminate abnormal signal interference. Linear deviation and angular deviation values ​​are calculated for the discrete point position information set, along with scoliosis degree assessment. Scoliosis is quantified from multiple dimensions, making the measurement results more consistent with the actual condition and providing reliable data support for clinical diagnosis and the development of personalized treatment plans.

[0047] From the perspective of application prospects, the measuring device of the present invention has a relatively simple structure, is easy to manufacture and promote on a large scale, and is expected to fill the gap in the market for high-precision, safe and convenient scoliosis monitoring equipment. It has important practical significance and broad market value for promoting the early detection, precise treatment and daily health management of scoliosis diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flow chart for the implementation of the method of the present invention;

[0049] Figure 2 It is a composition diagram of the device of the present invention. DETAILED DESCRIPTION

[0050] In order to better understand the content of the present invention, an embodiment is given here.

[0051] Figure 1 4 is an implementation flow chart of the method of the present invention. Figure 2 It is a composition diagram of the device of the present invention.

[0052] In a first aspect of an embodiment of the present invention, a method for measuring scoliosis is disclosed, comprising:

[0053] S1, using patch electrodes set on the user's spine to collect the user's electromyographic signal set;

[0054] S2, using a position measurement module disposed on the user's spine to collect a set of discrete point position information; the discrete point position information set includes a position information sequence measured by each position measurement module; the position information sequence is an information sequence of the position of the user's spine measured by a position measurement module; the information sequence is a position value measured at a plurality of discrete moments;

[0055] S3, performing scoliosis measurement processing on the electromyographic signal set and the discrete point position information set to obtain a scoliosis measurement information set of the user.

[0056] The step of performing scoliosis measurement processing on the electromyographic signal set and the discrete point position information set to obtain a scoliosis measurement information set of the user includes:

[0057] S31, preprocessing the electromyographic signal set and the discrete point position information set to obtain a preprocessed information set;

[0058] S32, performing modal abnormality discrimination processing on the electromyographic signal set in the pre-processing information set to obtain a modal discrimination value;

[0059] S33, determining whether the modal discrimination value is greater than a preset first discrimination threshold, and obtaining a first discrimination result;

[0060] If the first judgment result is no, execute S33;

[0061] If the first discrimination result is yes, scoliosis calculation processing is performed on the discrete point position information set in the pre-processed information set to obtain the user's scoliosis measurement information set.

[0062] The preprocessing of the electromyographic signal set and the discrete point position information set to obtain a preprocessed information set includes:

[0063] S311, performing data cleaning processing on the electromyographic signal set and the discrete point position information set to obtain a first information set;

[0064] S312, performing data category check processing on the first information set to obtain a second information set;

[0065] S313: Perform deviation discrimination processing on the second information set to obtain a preprocessed information set.

[0066] The performing modal abnormality discrimination processing on the electromyographic signal set in the pre-processing information set to obtain a modal discrimination value includes:

[0067] S321, performing feature transformation on each electromyographic signal in the electromyographic signal set in the preprocessing information set to obtain a corresponding feature signal;

[0068] S322, performing characteristic integral calculation on each electromyographic signal in the electromyographic signal set in the preprocessing information set to obtain a corresponding characteristic value;

[0069] S323: Perform modal anomaly calculation processing on all myoelectric signals and corresponding characteristic signals and characteristic values ​​to obtain a modal discrimination value.

[0070] The feature transformation can be achieved by using a variational mode decomposition (VMD) algorithm.

[0071] The expression for calculating the characteristic integral is:

[0072]

[0073] Among them, α is the characteristic value, f(t) is the value of the electromyographic signal at time t, and [0, T] is the value range at time t.

[0074] The characteristic integral calculation utilizes a specific cosine function combined with a time variable to integrate the EMG signal f(t) within the time interval [0, T]. This calculation method can deeply explore the characteristic information of the EMG signal in both the time and frequency domains, and is more capable of capturing subtle changes in the EMG signal than traditional simple signal processing methods. This unique integral calculation converts the raw EMG signal into eigenvalues, effectively removing noise interference from the signal and highlighting the effective signal components related to scoliosis. This makes subsequent eigenvalue-based analysis more accurate and lays a reliable signal processing foundation for scoliosis diagnosis.

[0075] The expression for the modal abnormality calculation process is:

[0076]

[0077] Among them, yc1 is the modal discriminant value, M is the total number of myoelectric signals in the myoelectric signal set, and f i (t) is the i-th EMG signal in the EMG signal set, μ i (t) is the characteristic signal corresponding to the i-th EMG signal in the EMG signal set, f0(t) is the average signal of all EMG signals in the EMG signal set, and μ0(t) is the average signal of the characteristic signals corresponding to all EMG signals in the EMG signal set.

[0078] The modal abnormality calculation process is performed by comparing each electromyographic signal f i (t) and the difference between its corresponding characteristic signal and the overall average signal, combined with the exponential and fractional calculation forms, a comprehensive modal abnormality assessment of the EMG signal set is performed. This calculation method not only takes into account the changes in the individual EMG signals themselves, but also combines the overall characteristics of the signal set. It can accurately identify abnormal EMG signal modes and avoid measurement errors caused by individual abnormal signals. When the system identifies an abnormal mode, it can promptly eliminate the interfering signal to ensure the data quality of subsequent scoliosis calculation processing, thereby significantly improving the stability and reliability of the entire scoliosis measurement system, making the measurement results more realistically reflect the user's scoliosis condition, and providing more valuable data support for clinical diagnosis and health monitoring.

[0079] The step of performing scoliosis calculation processing on the discrete point position information set in the pre-processed information set to obtain the user's scoliosis measurement information set includes:

[0080] S331, calculating a straight line deviation value for the discrete point position information set in the preprocessing information set to obtain a straight line deviation value;

[0081] S332, calculating an angle deviation value for the discrete point position information set in the pre-processing information set to obtain an angle deviation value;

[0082] S333, performing scoliosis degree assessment processing on the straight line deviation value and the angle deviation value to obtain a scoliosis assessment value;

[0083] S334: Utilize the straight line deviation value, angle deviation value and scoliosis assessment value to construct a scoliosis measurement information set of the user.

[0084] The expression for calculating the straight line deviation value is:

[0085]

[0086] Among them, α is the deviation value of the straight line, [x ij ,y ij ] is the two-dimensional plane coordinate of the jth discrete point position information of the i-th position information sequence in the discrete point position information set, [x 0i ,y 0i ] is the standard value of the two-dimensional plane coordinate of the user's spine position where the position measurement module is located corresponding to the i-th position information sequence in the discrete point position information set, and K is the number of discrete point position information contained in a position information sequence.

[0087] The expression for calculating the straight line deviation value comprehensively considers the two-dimensional plane coordinates of the discrete point position information and its standard value. This calculation method can keenly capture the slight deviation between the position information of each discrete point and the standard position through logarithmic operations and coordinate difference processing, and quantify the degree of deviation of the spine on the two-dimensional plane as a whole through cumulative summation. Compared with the traditional simple distance calculation method, this expression can not only reflect the absolute offset of discrete points, but also amplify the impact of small deviations through logarithmic functions, and more accurately present the changing trend of scoliosis in straight line morphology. This precise calculation of straight line deviation values ​​provides a quantitative basis for doctors to intuitively judge the degree of linear deformation of scoliosis, which helps to formulate targeted correction plans.

[0088] The expression for calculating the angle deviation value is:

[0089]

[0090] Among them, γ is the preset bias normalization value, θ is the angle deviation value, M is the total number of position information sequences in the discrete point position information set, [x ij ,y ij ,z ij ] is the three-dimensional position coordinate of the jth discrete point position information of the ith position information sequence in the discrete point position information set, and K is the number of discrete point position information contained in a position information sequence.

[0091] The expression for calculating the angle deviation value is able to convert the position information of discrete points into accurate angle data through trigonometric operations such as arcsine and arctangent, combined with the three-dimensional coordinate relationship, and effectively avoids the angle error caused by only two-dimensional plane analysis. At the same time, the preset bias normalization further eliminates the systematic deviation caused by individual differences or measurement environment, making the angle deviation value more universal and accurate. This method of quantifying the degree of scoliosis from a three-dimensional spatial perspective is highly consistent with the physiological structural characteristics of the spine, and can more comprehensively and truly reflect the actual curvature state of the spine, providing a scientific and reliable quantitative indicator for the graded diagnosis and treatment effect evaluation of scoliosis. The two expressions complement each other, and from the two key dimensions of linear deviation and angular offset, they jointly construct a complete scoliosis quantitative evaluation system, which greatly improves the comprehensiveness and professionalism of scoliosis measurement.

[0092] The discrete point position information is the three-dimensional position coordinates measured in the geodetic rectangular coordinate system.

[0093] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers. The noise data smoothing process involves first identifying noise data and then smoothing the noise data based on the preceding and following data.

[0094] The data category checking process is to judge the attributes of each data in the information set, to judge whether the attributes are consistent with the preset attributes, and to delete the inconsistent data from the information set.

[0095] The deviation determination process includes:

[0096] Calculate the average signal of all myoelectric signals in the myoelectric signal set in the second information set;

[0097] Calculating a similarity value between each electromyographic signal and the average signal, and deleting electromyographic signals having similarity values ​​less than a preset discrimination threshold from the electromyographic signal set in the second information set, to obtain the electromyographic signal set in the preprocessing information set;

[0098] calculating a mean of all position information sequences of the discrete point position information set in the second information set to obtain an average position information sequence;

[0099] For each position information sequence, a similarity value between the position information sequence and the average position information sequence is calculated, and position information sequences having similarity values ​​less than a preset discrimination threshold are deleted from the discrete point position information set in the second information set to obtain the discrete point position information set in the preprocessed information set;

[0100] The similarity value can be calculated using methods such as Hamming distance, Jaccard coefficient, VDM (Value Difference Metric) or a category similarity matrix based on domain knowledge.

[0101] The step of performing scoliosis degree assessment processing on the straight line deviation value and the angle deviation value to obtain a scoliosis assessment value includes:

[0102] The preset discrimination range set is used to discriminate the discrimination range of the straight line deviation value and the angle deviation value, and the scoliosis assessment value corresponding to the discrimination range is determined as the obtained scoliosis assessment value.

[0103] The discrimination range set includes several discrimination ranges and corresponding scoliosis assessment values. The discrimination range is a combination of the ranges of linear deviation values ​​and angular deviation values. The scoliosis assessment value types include no scoliosis, general scoliosis, moderate scoliosis, and severe scoliosis.

[0104] The position measurement module may be an IMU position measurement device, a satellite positioning device, or an ultrasonic ranging sensor.

[0105] A second aspect of the embodiments of the present invention discloses a scoliosis measurement device for implementing the scoliosis measurement method, comprising: patch electrodes, a position measurement module, and a scoliosis measurement and evaluation module;

[0106] The patch electrodes are placed on the user's spine to collect the user's electromyographic signals.

[0107] The position measurement module is arranged at the user's spine position and is used to collect and obtain a set of discrete point position information;

[0108] The scoliosis measurement and evaluation module is connected to the patch electrode and the position measurement module respectively, and is used to perform scoliosis measurement processing on the electromyographic signal set and the discrete point position information set to obtain the user's scoliosis measurement information set.

[0109] A third aspect of the present invention discloses a scoliosis measurement device, comprising:

[0110] a memory storing executable program code;

[0111] a processor coupled to the memory;

[0112] The processor calls the executable program code stored in the memory to execute the scoliosis measurement method.

[0113] According to a fourth aspect of an embodiment of the present invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions. When the computer instructions are called by a computer, the computer instructions are used to execute the method for measuring scoliosis.

[0114] According to a fifth aspect of the embodiments of the present invention, an information data processing terminal is disclosed, which is used to implement the scoliosis measurement method.

[0115] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A method for measuring scoliosis, characterized in that: include: S1, using patch electrodes set on the user's spine to collect the user's electromyographic signal set; S2, using a position measurement module disposed on the user's spine to collect a discrete point position information set; the discrete point position information set includes a position information sequence measured by each position measurement module; S3, performing scoliosis measurement processing on the electromyographic signal set and the discrete point position information set to obtain a scoliosis measurement information set of the user.

2. The method for measuring scoliosis according to claim 1, wherein: The step of performing scoliosis measurement processing on the electromyographic signal set and the discrete point position information set to obtain a scoliosis measurement information set of the user includes: S31, preprocessing the electromyographic signal set and the discrete point position information set to obtain a preprocessed information set; S32, performing modal abnormality discrimination processing on the electromyographic signal set in the pre-processing information set to obtain a modal discrimination value; S33, determining whether the modal discrimination value is greater than a preset first discrimination threshold, and obtaining a first discrimination result; If the first judgment result is no, execute S32; If the first discrimination result is yes, scoliosis calculation processing is performed on the discrete point position information set in the pre-processed information set to obtain the user's scoliosis measurement information set.

3. The method for measuring scoliosis according to claim 2, wherein: The preprocessing of the electromyographic signal set and the discrete point position information set to obtain a preprocessed information set includes: S311, performing data cleaning processing on the electromyographic signal set and the discrete point position information set to obtain a first information set; S312, performing data category check processing on the first information set to obtain a second information set; S313: Perform deviation discrimination processing on the second information set to obtain a preprocessed information set.

4. The method for measuring scoliosis according to claim 2, wherein: The performing modal abnormality discrimination processing on the electromyographic signal set in the pre-processing information set to obtain a modal discrimination value includes: S321, performing feature transformation on each electromyographic signal in the electromyographic signal set in the preprocessing information set to obtain a corresponding feature signal; S322, performing characteristic integral calculation on each electromyographic signal in the electromyographic signal set in the preprocessing information set to obtain a corresponding characteristic value; S323: Perform modal anomaly calculation processing on all myoelectric signals and corresponding characteristic signals and characteristic values ​​to obtain a modal discrimination value.

5. The method for measuring scoliosis according to claim 4, wherein: The expression for calculating the characteristic integral is: Among them, α is the characteristic value, f(t) is the value of the electromyographic signal at time t, and [0, T] is the value range of time t; The expression for the modal abnormality calculation process is: Among them, yc1 is the modal discriminant value, M is the total number of myoelectric signals in the myoelectric signal set, and f i (t) is the i-th EMG signal in the EMG signal set, μ i (t) is the characteristic signal corresponding to the i-th EMG signal in the EMG signal set, f0(t) is the average signal of all EMG signals in the EMG signal set, and μ0(t) is the average signal of the characteristic signals corresponding to all EMG signals in the EMG signal set.

6. The method for measuring scoliosis according to claim 2, wherein: The step of performing scoliosis calculation processing on the discrete point position information set in the pre-processed information set to obtain the user's scoliosis measurement information set includes: S331, calculating a straight line deviation value for the discrete point position information set in the preprocessing information set to obtain a straight line deviation value; S332, calculating an angle deviation value for the discrete point position information set in the pre-processing information set to obtain an angle deviation value; S333, performing scoliosis degree assessment processing on the straight line deviation value and the angle deviation value to obtain a scoliosis assessment value; S334: Utilize the straight line deviation value, angle deviation value and scoliosis assessment value to construct a scoliosis measurement information set of the user.

7. A scoliosis measuring device for implementing the scoliosis measuring method according to any one of claims 1 to 6, characterized in that: include: Patch electrodes, position measurement module, scoliosis measurement and evaluation module; The patch electrodes are placed on the user's spine to collect the user's electromyographic signals. The position measurement module is arranged at the user's spine position and is used to collect and obtain a set of discrete point position information; The scoliosis measurement and evaluation module is connected to the patch electrode and the position measurement module respectively, and is used to perform scoliosis measurement processing on the electromyographic signal set and the discrete point position information set to obtain the user's scoliosis measurement information set.

8. A scoliosis measurement device, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the scoliosis measurement method according to any one of claims 1 to 6.

9. A computer storable medium, characterized in that The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the scoliosis measurement method according to any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the scoliosis measurement method according to any one of claims 1 to 6.

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