Intelligent cervical traction optimization system based on multi-modal data analysis

Abstract

The invention discloses an intelligent cervical traction optimization system based on multi-modal data analysis, and relates to the technical field, the system comprises a multi-modal data acquisition module for acquiring neck posture, stress, muscle electrical activity, carotid artery blood vessel diameter and blood flow velocity data; the data preprocessing module performs filtering and normalization operation; the data analysis module carries out feature extraction and state evaluation; and the traction control module regulates and controls traction force, angle and time parameters according to the evaluation result in combination with carotid artery blood vessel diameter change and blood flow velocity data monitored by the ultrasonic probe in real time. In addition, a user interaction module is used for processing user instructions and feedback, a data storage module is used for storing and backing up related data, and a remote monitoring module is used for transmitting the data and a system state to a remote server for professional personnel to remotely check and analyze.

Description

Technical Field

[0001] The present invention relates to the fields of medical technology and sensor technology, and particularly to an intelligent cervical traction optimization system based on multimodal data analysis. Background Art

[0002] In recent years, with the huge changes in people's lifestyles and work patterns, cervical spondylosis has shown an increasingly high incidence and a trend of getting younger. Long hours of sitting at a desk, frequent head-down use of smartphones and tablets, and poor sleeping postures all keep the cervical spine in an abnormal stress state for a long time, causing the neck muscles to be continuously tense and the intervertebral disc degeneration to accelerate, thus triggering various cervical spine problems. According to relevant statistical data, the prevalence of cervical spondylosis has been rising year by year globally and has become one of the important health hazards affecting people's quality of life and work efficiency.

[0003] Patients with cervical spondylosis are often troubled by discomfort symptoms such as neck pain, stiffness, dizziness, and numbness in the upper limbs. In severe cases, they may even experience unsteady walking, blurred vision, etc., which not only cause great damage to the patients' physical health but also bring heavy economic burdens and psychological pressures due to the long-term treatment and rehabilitation process. Traditional cervical traction, as a commonly used treatment method for cervical spondylosis, can relieve the compression of the cervical spine on nerves and blood vessels and alleviate symptoms to a certain extent, but it has many obvious limitations.

[0004] In terms of traction equipment, the functions of most traditional cervical traction devices are relatively single, and the traction parameters are often fixed values set in advance, making it difficult to make personalized adjustments according to the specific conditions, physical conditions, and tolerance levels of different patients. For example, for patients with weak constitutions and mild cervical spine lesions, excessive traction force may cause neck muscle strains and increased pain; while for patients with severe conditions who require stronger traction effects, the fixed traction parameters may not achieve the ideal treatment effect, lengthening the treatment cycle and increasing the possibility of recurrence of the condition. At the same time, traditional cervical traction treatment lacks an effective monitoring and feedback mechanism for the real-time state of patients; it is difficult for doctors to accurately grasp the cervical spine stress, muscle state, and physiological reactions of patients during the treatment process, and they can only roughly judge the treatment effect based on experience and the subjective feedback of patients, resulting in untimely and inaccurate adjustment of treatment plans and affecting the overall treatment quality.

[0005] To solve the above problems, the present invention proposes an intelligent cervical traction optimization system based on multimodal data analysis. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent cervical traction optimization system based on multimodal data analysis to solve the problems raised in the prior art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An intelligent cervical traction optimization system based on multimodal data analysis, including the following:

[0009] It includes a multimodal data acquisition module, a data preprocessing module, a data analysis module, a traction control module, a user interaction module, a data storage module, and a remote monitoring module; the multimodal data acquisition module uses an attitude sensor and an ultrasonic sensor to collect neck attitude data, neck force data, neck muscle electrical activity, blood vessel diameter, and blood flow velocity data; the data preprocessing module is used to preprocess the collected multimodal data, and the preprocessing operations include data filtering and data normalization; the data analysis module is used to analyze the preprocessed multimodal data, and the analysis operations include feature extraction and state evaluation; the traction control module is used to control the traction parameters of the traction device according to the data analysis and evaluation results and in combination with the real-time monitoring data of the blood vessel diameter and blood flow velocity of the carotid artery by the ultrasonic probe built in the cervical collar, and the traction parameters include traction force, traction angle, and traction time; the user interaction module is used to receive instructions input by the user and give feedback during use, and give feedback to the user; the data storage module is used to store and back up the collected multimodal data, preprocessed data, analysis results, and traction parameter settings; the remote monitoring module is used to transmit relevant data of the user's cervical vertebra and the system operation status to a remote server, and allow professionals to view and analyze the data through a remote terminal.

[0010] The multimodal data acquisition module includes a neck attitude data acquisition unit, a neck force data acquisition unit, a neck muscle electrical activity data acquisition unit, and an ultrasonic data acquisition unit; the neck attitude data acquisition unit uses an attitude sensor to obtain the attitude information of the neck in three-dimensional space, including the movement direction and angle change of the neck; the neck force data acquisition unit measures the pressure on the neck during traction by setting a pressure sensor at the contact part between the traction device and the neck; for attitude data, the original data obtained from the accelerometer, gyroscope, and magnetometer can be used to obtain accurate attitude angles through a fusion algorithm. When using the quaternion method for attitude calculation, the attitude update formula is:

[0011]

[0012] where q is the quaternion, w is the angular velocity vector measured by the gyroscope, is quaternion multiplication. To obtain the change of the attitude quaternion q over time, q 1 needs to be integrated. Given the integration formula is as follows:

[0013]

[0014] where q(t) is a function of the quaternion with respect to time t, q(t 0 ) is the quaternion at the initial time t 0 , e is the exponential function of the quaternion, and w(t) is the value of the angular velocity vector at time t;

[0015] The neck muscle electroactivity data acquisition unit is obtained by relying on muscle sensors attached to the surface of the neck muscles, and is used to record the fatigue degree and functional state of the neck muscles. Muscle electroactivity can reflect the contraction and relaxation states of the muscles, which helps to understand the fatigue degree and functional state of the neck muscles;

[0016] The ultrasonic data acquisition unit refers to adding a flexible ultrasonic sensor on the side of the traction device that fits the neck to real-time monitor the changes in the blood vessel diameter and blood flow velocity of the user's carotid artery during traction.

[0017] The data preprocessing module includes a data filtering processing unit and a data normalization unit. The data filtering processing unit removes noise through different algorithms. For neck posture data, the Kalman filtering algorithm is used to recursively estimate the system state and fuse the predicted value and the measured value to reduce the influence of noise on the posture data; for neck force data, the median filtering algorithm is used to replace the value of a certain point in the data sequence with the median of the values of each point in a neighborhood of this point, making the values of the surrounding points close to the true value to eliminate isolated noise points; for neck muscle electroactivity data, the Butterworth filtering algorithm is used, and this algorithm can provide a relatively flat frequency response in the passband to eliminate high-frequency and low-frequency noise in the muscle electrical signal; the data normalization unit unifies different types of data into the range [0, 1] for subsequent data analysis and processing. For a certain data x, the normalization formula is:

[0018] X n =(x - x min ) / (x max - x min )

[0019] where x max and x min are the maximum and minimum values in this data type respectively.

[0020] The data analysis module includes a feature extraction unit and a state evaluation unit. The feature extraction unit is used to extract key information that can reflect the cervical spine state from the preprocessed data. For neck posture data, the mean and variance of the data are analyzed to understand the average state and stability of the user's neck posture; for neck force data, the maximum and minimum values of the neck force are judged to analyze the extreme situation of the neck force; for neck muscle electromyogram signals, the heart rate characteristics and amplitude characteristics of the muscle electrical activity are extracted to reflect the muscle fatigue degree and contraction strength. The state evaluation unit uses an evaluation model constructed based on machine learning algorithms. By training on cervical spine sample data, the algorithm learns the relationship between data features and cervical spine states, and then evaluates the cervical spine health state of new users.

[0021] The traction control module controls the parameters of the traction device based on the evaluation conclusion given by the data analysis module for the user's cervical spine state. During the traction process, the flexible ultrasonic sensor built into the traction device can measure the diameter change and blood flow velocity of the carotid artery during traction, and further adjust the traction parameters based on this. In terms of traction force, there is a mechanism that can be adjusted up or down according to the user's needs, and the adjustment range changes gradually at intervals to adapt to the diverse cervical spine conditions and tolerance levels of different users. When the system receives user feedback through the interaction page that adjustment is needed, the adjustment formula is as follows:

[0022] F n =F n-1 +K·ΔF

[0023] Where F n is the adjusted traction force, F n-1 is the traction force before adjustment, k takes -1 or 1 according to whether the force decreases or increases, and ΔF = 1;

[0024] For the traction angle, it can be adjusted within a certain range in the positive and negative directions, and the adjustment step size is 1°, which fits the cervical spine physiological curvature requirements and disease differences of different users. According to the analysis results, when adjusting the angle, it is adjusted according to a fixed step size, and the adjustment formula is as follows:

[0025] θ n =θ n-1 +k·Δθ

[0026] Where θ n is the adjusted traction angle, θn - 1 is the traction angle before adjustment, k takes -1 or 1 according to the adjustment direction requirement, and Δθ = 1;

[0027] For the traction time, it is set according to the user's needs, and each change is in units of a fixed small time length to meet the requirements of different users for the traction duration at each stage from initial diagnosis and treatment to subsequent rehabilitation; each adjustment needs to be made according to a fixed step size, and the adjustment formula is as follows:

[0028] T n =T n-1 +k·ΔT

[0029] T n where is the adjusted duration, T n-1 is the duration before adjustment, k takes the value of -1 or 1 according to shortening or lengthening the time, and △T = 1;

[0030] For the measurement of the change in carotid artery diameter, by integrating a flexible ultrasonic sensor inside the traction collar, when the user performs cervical traction, the flexible ultrasonic sensor emits ultrasonic waves to the carotid artery. After the ultrasonic waves are emitted to the carotid artery, they will be reflected, and the sensor will receive the reflected ultrasonic signal. Then, by analyzing the amplitude and phase changes of the reflected signal, the interface between the inner and outer membranes of the carotid artery wall can be identified, and further the change in the carotid artery diameter can be calculated;

[0031] For the monitoring of blood flow velocity, when the ultrasonic waves emitted by the flexible ultrasonic sensor encounter flowing blood, based on the Doppler effect, the frequency of the reflected wave will change; there is a quantitative relationship between the blood flow velocity and the change in the frequency of the reflected wave, and this relationship is described by the Doppler equation, and the formula is as follows:

[0032]

[0033] where, f d is the change in the frequency of the reflected wave, f 0 is the frequency of the emitted ultrasonic wave, v is the blood flow velocity, θ is the angle between the ultrasonic beam and the blood flow direction, and c is the propagation speed of ultrasonic waves in human tissues;

[0034] According to the measurement formula, first, ultrasonic waves with a fixed frequency f 0 are emitted into the carotid artery. When the ultrasonic waves encounter flowing blood, the frequency of the reflected wave will change, and the system can detect the change in frequency f d , and then through the above-known data, the blood flow velocity can be calculated using the Doppler equation;

[0035] By measuring the change in the diameter of the user's carotid artery and the blood flow velocity, the system can monitor the change in the pressure on the user's neck during traction in real time, so as to adjust the traction parameters in real time.

[0036] The user interaction module includes an operation interface unit, a cervical spine status visualization unit, and a traction parameter adjustment unit; the operation interface unit is used for the user to operate the traction device in the system and adjust the traction device at any time during use; the cervical spine status visualization unit uses a combination of text and charts to visually display data on the current neck posture stability and muscle fatigue degree of the user to the user, and compares and marks the data with health indicators; the traction parameter adjustment unit is used for the user to adjust the traction force, traction angle, and traction time through a visual operation panel during traction. The interaction module will list the current traction force, traction angle, and traction time data of the device, and these values will change in real time as the user makes manual adjustments; enabling the user to intuitively understand their cervical spine condition and traction settings, improving the user experience and treatment compliance.

[0037] The data storage module includes a data backup unit and a data recovery unit; in the intelligent cervical spine traction optimization system, the data storage module will automatically back up data at a preset time interval. The backup time interval is after each use by the user, and the backed-up data will be stored on an external storage device and a cloud server; when data is lost or damaged, the data storage module can start a recovery mechanism, and the system can extract corresponding data from the most recent backup for recovery. The recovery process involves data reading, transmission, and re-writing to the local storage system, and data consistency checks and validations will be performed.

[0038] The remote monitoring module includes a data transmission unit and a remote terminal data analysis unit; the data transmission unit integrates multiple network communication methods, covering Wi-Fi, Bluetooth, and mobile data networks, to perform real-time data transmission in different user scenarios, and uses encryption algorithms during transmission to encapsulate the data in ciphertext form; the remote terminal data analysis unit is adapted to various mobile phones, computers, and tablets, and professionals use terminal analysis tools to analyze and provide feedback on the actual data of users.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. Precise assessment and health insight: Relying on the multi-modal data acquisition module, integrating rich data sources such as neck posture, force, and muscle electrical activity, and using data processing and analysis technologies to customize personalized traction plans for users. For example, by long-term monitoring the mean and variance of neck posture data, accurately grasping personal daily neck habitual postures and stability, comparing with the benchmark model of healthy people, and early warning of bad posture tendencies, enabling users to timely detect potential cervical spine health hazards, which is of great significance in preventing the occurrence of cervical spondylosis and changing the passive situation of only seeking medical treatment after symptoms appear.

[0041] 2. Personalized Interaction Experience: The user interaction module greatly enhances the user's sense of participation and control. The operation interface is simple and intuitive, suitable for any age group. Users can conveniently input instructions and feedback on their usage experience. When adjusting the traction parameters, for example, they can change the traction force and angle in fine steps according to their immediate comfort level, and view the adjusted status at any time. The whole process is visualized, eliminating concerns about unknown mechanical settings, and significantly improving the comfort level of use, meeting the personalized demands of users of different ages and cultural backgrounds.

[0042] 3. Optimization and Expansion of Remote Medical Resources: The remote monitoring module uses various network communication means (Wi-Fi, Bluetooth, mobile data network) to break geographical limitations and push cervical spine-related data and system status to the remote server in real time. This helps professional medical staff view and analyze remotely. For example, experts can analyze the data of users in remote areas across regions and give targeted maintenance suggestions, realizing the sharing of high-quality medical intellectual resources, making medical services easily accessible, avoiding long-distance trips for patients, saving time and economic costs, and improving the fairness and efficiency of medical services in the whole society, expanding the breadth and depth of medical service coverage.

[0043] 4. Ensuring User Safety: Measuring the diameter of the carotid artery can directly reflect whether the carotid artery is compressed during traction; if the diameter of the carotid artery significantly decreases during traction, it may mean that the traction force is too large or the traction angle is inappropriate, resulting in blood vessel compression; when the blood vessel is compressed, the blood flow velocity usually changes, for example, the blood flow velocity may decrease. By detecting this change in a timely manner, the traction parameters can be adjusted before more serious vascular injuries or cerebral blood supply insufficiency occur, avoiding harm to the patient.

[0044] During traction, abnormal changes in its diameter and blood flow velocity may affect the blood supply to the brain. Through real-time monitoring, cardiovascular complications caused by hemodynamic changes, such as cerebral ischemia and thrombosis, can be prevented. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the organizational structure of an intelligent cervical traction optimization system based on multi-modal data analysis according to the present invention;

[0046] Figure 2 It is a schematic diagram of the system flow of an intelligent cervical traction optimization system based on multi-modal data analysis according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution, an intelligent cervical traction optimization system based on multimodal data analysis, including the following:

[0049] It includes a multimodal data acquisition module, a data preprocessing module, a data analysis module, a traction control module, a user interaction module, a data storage module, and a remote monitoring module; the multimodal data acquisition module uses an attitude sensor and an ultrasonic sensor to collect neck attitude data, neck force data, neck muscle electrical activity, blood vessel diameter, and blood flow velocity data; the data preprocessing module is used to preprocess the collected multimodal data, and the preprocessing operations include data filtering and data normalization; the data analysis module is used to analyze the preprocessed multimodal data, and the analysis operations include feature extraction and state evaluation; the traction control module is used to control the traction parameters of the traction device according to the data analysis and evaluation results and in combination with the real-time monitoring data of the blood vessel diameter and blood flow velocity of the carotid artery by the ultrasonic probe built in the neck brace, and the traction parameters include traction force, traction angle, and traction time; the user interaction module is used to receive instructions and feedback input by the user during use and give feedback to the user; the data storage module is used to store and back up the collected multimodal data, preprocessed data, analysis results, and traction parameter settings; the remote monitoring module is used to transmit relevant data of the user's cervical spine and the system operation status to a remote server and allow professionals to view and analyze the data through a remote terminal.

[0050] The multimodal data acquisition module includes a neck attitude data acquisition unit, a neck force data acquisition unit, a neck muscle electrical activity data acquisition unit, and an ultrasonic data acquisition unit; the neck attitude data acquisition unit uses an attitude sensor to obtain the attitude information of the neck in three-dimensional space, including the movement direction and angle change of the neck; the neck force data acquisition unit measures the pressure on the neck during traction by setting a pressure sensor at the contact part between the traction device and the neck; for attitude data, the original data obtained from the accelerometer, gyroscope, and magnetometer can be used to obtain accurate attitude angles through a fusion algorithm. When using the quaternion method for attitude calculation, the attitude update formula is:

[0051]

[0052] Among them, q is a quaternion, w is the angular velocity vector measured by the gyroscope, is quaternion multiplication. To obtain the change of the attitude quaternion q with time, it is necessary to perform an integral on q 1 . Given that the integral formula is as follows:

[0053]

[0054] where q(t) is a function of the quaternion with respect to time t, q(t 0 ) is the quaternion at the initial time t 0 , e is the exponential function of the quaternion, and w(t) is the value of the angular velocity vector at time t;

[0055] The neck muscle electroactivity data acquisition unit is obtained by relying on muscle sensors attached to the surface of the neck muscles, and is used to record the fatigue degree and functional state of the neck muscles. Muscle electroactivity can reflect the contraction and relaxation states of the muscles, which helps to understand the fatigue degree and functional state of the neck muscles;

[0056] The ultrasonic data acquisition unit refers to adding a flexible ultrasonic sensor on the side of the traction device that fits the neck to monitor the changes in the blood vessel diameter and blood flow velocity of the user's carotid artery in real time during the traction process.

[0057] The data preprocessing module includes a data filtering processing unit and a data normalization unit. The data filtering processing unit removes noise through different algorithms. For neck posture data, the Kalman filtering algorithm is used to recursively estimate the system state and fuse the predicted value and the measured value to reduce the influence of noise on the posture data; for neck force data, the median filtering algorithm is used to replace the value of a certain point in the data sequence with the median of the values of each point in a neighborhood of this point, so that the values of the surrounding points are close to the true value to eliminate isolated noise points; for neck muscle electroactivity data, the Butterworth filtering algorithm is used, which can provide a relatively flat frequency response within the passband to eliminate high-frequency and low-frequency noise in the muscle electrical signals; the data normalization unit unifies different types of data into the range of [0, 1] for subsequent data analysis and processing. For a certain data x, the normalization formula is:

[0058] X n =(x - x min ) / (x max - x min )

[0059] where x max and x min are the maximum and minimum values in this data type respectively.

[0060] The data analysis module includes a feature extraction unit and a state evaluation unit. The feature extraction unit is used to extract key information that can reflect the cervical spine state from the preprocessed data. For neck posture data, the mean and variance of the data are analyzed to understand the average state and stability of the user's neck posture; for neck force data, the maximum and minimum values of the neck force are judged to analyze the extreme situation of the neck force; for neck muscle electrical activity signals, the heart rate characteristics and amplitude characteristics of the muscle electrical activity are extracted to reflect the muscle fatigue degree and contraction strength. The state evaluation unit uses an evaluation model constructed based on machine learning algorithms. By training on cervical spine sample data, the algorithm learns the relationship between data features and the cervical spine state, and then evaluates the cervical spine health state of new users.

[0061] The traction control module controls the parameters of the traction device based on the evaluation conclusion given by the data analysis module for the user's cervical spine state. During traction, the flexible ultrasonic sensor built into the traction device can measure the diameter change and blood flow velocity of the carotid artery during traction, and further adjust the traction parameters based on this. In terms of traction force, there is a mechanism that can be adjusted according to the user's needs to increase or decrease. The adjustment range changes gradually at intervals to adapt to the diverse cervical spine conditions and tolerance levels of different users. When the system receives user feedback through the interaction page that adjustment is needed, the adjustment formula is as follows:

[0062] F n =F n-1 +K·△F

[0063] Where F n is the adjusted traction force, F n-1 is the traction force before adjustment, k takes -1 or 1 according to whether the force decreases or increases, and △F = 1;

[0064] For the traction angle, it can be adjusted within a certain range in the positive and negative directions, and the adjustment step size is 1°, which fits the cervical spine physiological curvature requirements and disease differences of different users. According to the analysis results, when adjusting the angle, it is adjusted according to a fixed step size, and the adjustment formula is as follows:

[0065] θ n =θ n-1 +k·△θ

[0066] Where θ n is the adjusted traction angle, θn - 1 is the traction angle before adjustment, k takes -1 or 1 according to the adjustment direction requirement, and Δθ = 1;

[0067] For the traction time, it is set according to the user's needs, with each change in a fixed tiny time unit to meet the requirements of different users for the traction duration at each stage from initial diagnosis and treatment to subsequent rehabilitation; each adjustment needs to be made according to a fixed step size, and the adjustment formula is as follows:

[0068] T n = T n-1 + k·△T

[0069] T n where is the adjusted duration, T n-1 is the duration before adjustment, k takes the value of -1 or 1 according to shortening or lengthening the time, and ΔT = 1;

[0070] For the measurement of the change in carotid artery diameter, by integrating a flexible ultrasonic sensor inside the traction collar, when the user performs cervical traction, the flexible ultrasonic sensor emits ultrasonic waves to the carotid artery. After the ultrasonic waves are emitted to the carotid artery, they will be reflected, and the sensor will receive the reflected ultrasonic signal. Then, by analyzing the amplitude and phase changes of the reflected signal, the interface between the inner and outer membranes of the carotid artery wall can be identified, and further the change in carotid artery diameter can be calculated;

[0071] For the monitoring of blood flow velocity, when the ultrasonic waves emitted by the flexible ultrasonic sensor encounter flowing blood, based on the Doppler effect, the frequency of the reflected wave will change; there is a quantitative relationship between the blood flow velocity and the change in the frequency of the reflected wave, and this relationship is described by the Doppler equation, and the formula is as follows:

[0072]

[0073] where, f d is the change in the frequency of the reflected wave, f 0 is the frequency of the emitted ultrasonic wave, v is the blood flow velocity, θ is the angle between the ultrasonic beam and the blood flow direction, and c is the propagation speed of ultrasonic waves in human tissues;

[0074] According to the measurement formula, first, ultrasonic waves with a fixed frequency f 0 are emitted into the carotid artery. When the ultrasonic waves encounter flowing blood, the frequency of the reflected wave will change, and the system can detect the change in frequency f d , and then through the above-known data, the blood flow velocity can be calculated using the Doppler equation;

[0075] By measuring the change in the diameter of the user's carotid artery and the blood flow velocity, the system can monitor the change in the pressure on the user's neck during traction in real time, so as to adjust the traction parameters in real time.

[0076] The user interaction module includes an operation interface unit, a cervical spine status visualization unit, and a traction parameter adjustment unit; the operation interface unit is used for the user to operate the traction device in the system and adjust the traction device at any time during use; the cervical spine status visualization unit uses a combination of text and charts to visually display data on the current neck posture stability and muscle fatigue degree of the user to the user, and compares and marks the data with health indicators; the traction parameter adjustment unit is used for the user to adjust the traction force, traction angle, and traction time through a visual operation panel during traction. The interaction module will list the current traction force, traction angle, and traction time data of the device, and these values will change in real time as the user makes manual adjustments; enabling the user to intuitively understand their cervical spine condition and traction settings, and improving the user experience and treatment compliance.

[0077] The data storage module includes a data backup unit and a data recovery unit; in the intelligent cervical spine traction optimization system, the data storage module will automatically perform data backup at a preset time interval. The backup time interval is after each use by the user, and the backed-up data will be stored on an external storage device and a cloud server; the data recovery unit can initiate a recovery mechanism when data is lost or damaged. The system can extract corresponding data from the most recent backup for recovery. The recovery process involves data reading, transmission, and re-writing to the local storage system, and data consistency checks and validations will be performed.

[0078] The remote monitoring module includes a data transmission unit and a remote terminal data analysis unit; the data transmission unit integrates various network communication methods, covering Wi-Fi, Bluetooth, and mobile data networks, to perform real-time data transmission in different user scenarios, and uses encryption algorithms during transmission to encapsulate the data in ciphertext form; the remote terminal data analysis unit is adapted to various mobile phones, computers, and tablets. Professional personnel use terminal analysis tools to analyze and provide feedback on the actual data of the user.

[0079] Suppose in a professional rehabilitation and physiotherapy center, this intelligent cervical spine traction optimization system based on multi-modal data analysis is introduced, aiming to provide precise and personalized services for various types of cervical spondylosis patients and people with cervical spine health care needs. Before being officially put into use, the system has undergone strict debugging to ensure the normal operation of each module. At the same time, based on a large amount of past clinical cervical spine data, the machine learning evaluation model in the data analysis module has been pre-trained to enable it to have preliminary diagnostic and evaluation capabilities.

[0080] Suppose a user comes for adjustment due to cervical discomfort. After wearing a traction device equipped with a posture sensor (integrated accelerometer, gyroscope, and magnetometer), the system automatically activates the neck posture data acquisition unit. In the initial static state, the sensor records the initial posture data of the user's neck. Suppose the angular velocity vector w = [0.1, 0.5, -0.02] obtained from the gyroscope at this time, unit: rad / s. Here, the data is assumed for easy calculation and will actually fluctuate with slight neck movements. Through attitude calculation using the quaternion method, the initial quaternion q(t 0 ) = [1, 0, 0, 0] (representing the initial non-rotating state), and using the attitude update formula and the integral formula Within the subsequent 10 seconds, through step-by-step iterative calculations, a quaternion sequence changing with time is obtained, and then the pitch angle, yaw angle, and roll angle data of the neck are converted. The record shows that within these 10 seconds, the pitch angle of the user fluctuates from 0° to 5°, the yaw angle floats within the range of 2°, and the roll angle fluctuates within the range of 1°, reflecting that there are slight involuntary shakes in his neck and the attitude stability is poor.

[0081] The collected multi-modal data is transmitted to the data preprocessing module. The neck posture data is filtered by Kalman filter, combined with the prediction model and the measured value, to optimize the attitude angle deviation caused by sensor errors; after median filtering of the force data, it is connected to the normalization unit. Suppose the acquisition range of the force data F is 15N - 25N, and the force at a certain moment is 21N. According to the normalization formula X n =(x - x min ) / (x max - x min )=(21 - 15) / (25 - 15)=0.6. The EMG data is also normalized after filtering for subsequent unified analysis.

[0082] The feature extraction unit integrates the processed data and analyzes that the mean values of the neck posture are pitch angle 3°, yaw angle 0°, and roll angle 0°. The variance shows that the variance of the pitch angle is 2 (reflecting the dispersion degree of the shakes); the maximum force is 22N and the minimum force is 18N; the EMG MPF and amplitude features are as described above. The state evaluation unit, based on a pre-trained machine learning model, evaluates that the user's cervical vertebra is in a mild dysfunction state, providing a basis for setting subsequent traction parameters.

[0083] According to the evaluation results, the traction control module sets the initial traction parameters for the user. Through formula calculation, the traction force is set to 20N, the traction angle is 5°, and the traction time is initially set to 20 minutes. During the traction process, continuously monitor the diameter change and blood flow velocity of the user's carotid artery. If the user feels discomfort, they can give feedback through the user interaction module, and then adjust the traction parameters on the interaction panel.

[0084] The user can operate conveniently through the operation interface unit. The cervical spine status visualization unit displays the deviation of the neck posture stability and the degree of muscle fatigue. The traction parameter adjustment unit lists the current traction force of 25 N, the angle of 5°, the time of 20 minutes (the duration of traction) in real time, etc. The user can increase or decrease the force (in steps) and the angle (in steps) as needed. After adjustment, the system responds in real time to ensure the balance between comfort and treatment effect.

[0085] The diagnosis and treatment data of this time is automatically backed up to the cloud server and the external storage hard disk through the data storage module after the user's diagnosis and treatment is completed. The stored content includes the collected original data, the preprocessing results, the analysis and evaluation, and the traction parameters. If the system fails subsequently, the data recovery unit can extract the complete data from the most recent backup (such as cloud storage), and after consistency verification, rewrite it locally to ensure the integrity and traceability of the data.

[0086] At the same time, the data transmission unit encrypts the data of the user's entire diagnosis and treatment process (using the SSL / TLS encryption protocol) and transmits it to the remote server of the rehabilitation center in real time through Wi-Fi. Remote experts can view the data through the data analysis unit of the remote terminal on the mobile phone (installing a dedicated medical analysis APP), such as observing the change curve of the posture angle, the stress statistical chart, etc., remotely evaluate the rehabilitation progress of this user, and adjust the traction parameter suggestions if necessary, and transmit them back to the physical therapy center system to optimize the subsequent treatment.

[0087] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. An intelligent cervical traction optimization system based on multimodal data analysis, characterized in that: The system comprises a multimodal data acquisition module, a data preprocessing module, a data analysis module, a traction control module, a user interaction module, a data storage module and a remote monitoring module; the multimodal data acquisition module refers to the use of a posture sensor and an ultrasonic sensor to collect neck posture data, neck force data, neck muscle electrical activity, blood vessel diameter and blood flow velocity data; the data preprocessing module is used to preprocess the collected multimodal data, and the preprocessing operation includes data filtering and data normalization; the data analysis module is used to analyze the preprocessed multimodal data, and the analysis operation includes feature extraction and state evaluation; the traction control module is used to analyze the preprocessed multimodal data according to the state of the neck. The data analysis and evaluation results are combined with the real-time monitoring data of the carotid artery blood vessel diameter and blood flow velocity of the built-in ultrasonic probe of the cervical collar to control the traction parameters of the traction device, and the traction parameters include traction strength, traction angle, and traction time; the user interaction module is used to receive instructions and feedback input by the user during use, and give feedback to the user; the data storage module is used to store and back up the collected multimodal data, pre-processed data, analysis results and traction parameter settings; the remote monitoring module is used to transmit relevant data of the user's cervical spine and the system operation status to the remote server, and allow professionals to view and analyze data through a remote terminal.

2. The intelligent cervical traction optimization system based on multimodal data analysis according to claim 1, characterized in that: The multimodal data acquisition module includes a neck posture data acquisition unit, a neck force data acquisition unit, a neck muscle electrical activity data acquisition unit and an ultrasonic data acquisition unit; The neck posture data acquisition unit uses a posture sensor to obtain the posture information of the neck in three-dimensional space, including the movement direction and angle change of the neck; the neck force data acquisition unit measures the pressure on the neck during traction by setting a pressure sensor at the contact position between the traction device and the neck; for posture data, the original data obtained from the accelerometer, gyroscope and magnetometer can be used to obtain an accurate posture angle through a fusion algorithm. When the quaternion method is used for posture calculation, the posture update formula is: Among them, q is a quaternion, w is the angular velocity vector measured by the gyroscope, It is quaternion multiplication. To get the change of attitude quaternion q over time, q1 needs to be integrated. The integral formula is as follows: Where q(t) is the function of the quaternion with respect to time t, q(t0) is the quaternion at the initial time t0, e is the exponential function of the quaternion, and w(t) is the value of the angular velocity vector at time t; The neck muscle electrical activity data acquisition unit relies on muscle sensors attached to the surface of the neck muscles to obtain and record the fatigue degree and functional status of the neck muscles; The ultrasound acquisition unit refers to a flexible ultrasound sensor installed on the side of the traction device that fits the neck, which monitors the changes in the blood vessel diameter and blood flow velocity of the user's carotid artery in real time during the traction process.

3. The intelligent cervical traction optimization system based on multimodal data analysis according to claim 1, characterized in that: The data preprocessing module includes a data filtering processing unit and a data normalization unit. The data filtering processing unit removes noise through different algorithms. For the neck posture data, the Kalman filtering algorithm is used to recursively estimate the system state and fuse the predicted value and the measured value to reduce the influence of noise on the posture data. The median filtering algorithm is used for the neck force data. The value of a certain point in the data sequence is replaced by the median of the values ​​of each point in a neighborhood of the point, so that the values ​​of the surrounding points are close to the true value to eliminate isolated noise points. The Butterworth filtering algorithm is used for the neck muscle electrical activity data. The algorithm can provide a relatively flat frequency response within the passband to eliminate high-frequency and low-frequency noise in the muscle electrical signal. The data normalization unit unifies different types of data into the range of [0, 1] for subsequent data analysis and processing. For a certain data x, the normalization formula is: X n =(x-x min ) / (x max -x min ) where x max and x min The maximum and minimum values ​​of this data type, respectively.

4. The intelligent cervical traction optimization system based on multimodal data analysis according to claim 1, characterized in that: The data analysis module includes a feature extraction unit and a state evaluation unit. The feature extraction unit is used to extract key information that can reflect the state of the cervical spine from the preprocessed data. For the neck posture data, the mean and variance of the data are analyzed to understand the average state and stability of the user's neck posture. For the neck force data, the maximum and minimum values ​​of the neck force are determined to analyze the limit conditions of the neck force; for the neck muscle electrical activity signal, the fatigue degree and contraction force of the muscle are reflected by extracting the heart rate characteristics and amplitude characteristics of the muscle electrical activity; the state evaluation unit uses an evaluation model built based on a machine learning algorithm to train the cervical sample data. The algorithm learns the relationship between data characteristics and cervical state, and then evaluates the cervical health status of new users.

5. The intelligent cervical traction optimization system based on multimodal data analysis according to claim 1, characterized in that: The traction control module controls various parameters of the traction device based on the evaluation conclusion given by the data analysis module on the user's cervical spine status; during the traction process, the built-in flexible ultrasonic sensor of the traction device can measure the diameter change and blood flow velocity of the carotid artery during the traction process, and further adjust the traction parameters based on this; in terms of traction strength, a mechanism is provided that can increase or decrease the adjustment according to user needs, and the adjustment range changes gradually in intervals to adapt to the diverse cervical spine conditions and tolerance levels of different users; when the system receives user feedback through the interactive page that adjustment is required, the adjustment formula is as follows: F n =F n-1 +K·ΔF Among them, F n is the adjusted traction force, F n-1 is the traction force before adjustment, k takes the value of -1 or 1 according to whether the force decreases or increases, △F = 1; The traction angle can be adjusted within a certain range in the positive and negative directions, with an adjustment step of 1°, to meet the physiological curvature requirements of the cervical spine and the differences in disease conditions of different users. According to the analysis results, the angle is adjusted according to a fixed step length, and the adjustment formula is as follows: i n =θ n-1 +k·△θ where θ n is the traction angle after adjustment, θn-1 is the traction angle before adjustment, k takes the value of -1 or 1 according to the adjustment direction requirement, Δθ=1; The traction time is set according to user needs, and each change is in units of fixed micro-hours to meet the traction time requirements of different users from initial diagnosis and treatment to subsequent rehabilitation. Each adjustment needs to be adjusted according to a fixed step length, and the adjustment formula is as follows: T n =T n-1 +k·△T T n Where T is the adjusted duration, n-1 is the duration before adjustment, k takes the value of -1 or 1 according to shortening or extending the time, ΔT = 1; For the measurement of carotid artery diameter changes, a flexible ultrasonic sensor is integrated on the inside of the traction neck brace. When the user performs cervical traction, the flexible ultrasonic sensor transmits ultrasonic waves to the carotid artery. After the ultrasonic waves are transmitted to the carotid artery, they will be reflected. The sensor will receive the reflected ultrasonic signal, and then by analyzing the amplitude and phase changes of the reflected signal, the interface between the inner and outer membranes of the carotid artery wall can be identified, and the change in the carotid artery diameter can be further calculated. For the monitoring of blood flow velocity, when the ultrasonic wave emitted by the flexible ultrasonic sensor encounters the flowing blood, the frequency of the reflected wave will change based on the Doppler effect; there is a quantitative relationship between the blood flow velocity and the change in the reflected wave frequency, which is described by the Doppler equation, as follows: Among them, f d is the change in the reflected wave frequency, f0 is the frequency of the emitted ultrasonic wave, v is the blood flow velocity, θ is the angle between the ultrasonic beam and the blood flow direction, and c is the propagation speed of the ultrasonic wave in human tissue; According to the calculation formula, the ultrasound first emits an ultrasound wave with a fixed frequency f0 into the carotid artery. When the ultrasound wave encounters the flowing blood, the frequency of the reflected wave changes, and the system can detect the change in frequency f0. d Then, the blood flow velocity can be calculated using the Doppler equation based on the above known data.

6. The intelligent cervical traction optimization system based on multimodal data analysis according to claim 1, characterized in that: The user interaction module includes an operation interface unit, a cervical vertebra status visualization unit and a traction parameter adjustment unit; the operation interface unit is used to allow the user to operate the traction device in the system and adjust the traction device at any time during use; the cervical vertebra status visualization unit uses a combination of text and charts to intuitively show the user the data of the user's current neck posture stability and muscle fatigue level, and compares and annotates the data with health indicators; the traction parameter adjustment unit is used for the user to adjust the traction force, traction angle and traction time through a visual operation panel during the traction process, and the interaction module will list the current traction force, traction angle and traction time data of the device, and these values ​​will change in real time with the user's manual adjustment.

7. The intelligent cervical traction optimization system based on multimodal data analysis according to claim 1, characterized in that: The data storage module includes a data backup unit and a data recovery unit; in the intelligent cervical traction optimization system, the data storage module will automatically back up data at a preset time interval, and the backup time interval is after each user use, and the backed up data will be stored in an external storage device and a cloud server; when the data is lost or damaged, the data storage module can start the recovery mechanism, and the system can extract the corresponding data from the most recent backup for recovery. The recovery process involves reading, transmitting and rewriting data into the local storage system, and performing data consistency checks and verifications.

8. The intelligent cervical traction optimization system based on multimodal data analysis according to claim 1, characterized in that: The remote monitoring module includes a data transmission unit and a remote terminal data analysis unit; the data transmission unit integrates multiple network communication methods, covering Wi-Fi, Bluetooth and mobile data networks, and performs real-time data transmission in different user scenarios, and uses encryption algorithms during the transmission process to encapsulate the data into ciphertext form; the remote terminal data analysis unit is compatible with various mobile phones, computers and tablets, and professionals use terminal analysis tools to analyze and feedback the user's actual data.

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