Massage traction dynamic parameter cooperative control method

Abstract

The invention relates to the technical field of medical instrument control, in particular to a massage traction dynamic parameter cooperative control method, which comprises the following steps: S1, collecting real-time three-dimensional motion trails of each segment of a spine and stress distribution and deformation data of soft tissues in a target area; s2, calculating a deviation value between a current traction action and a target state, and generating a cooperative adjustment instruction of traction force, angle and frequency; s3, the traction direction is adjusted according to the traction force adjusting instruction in the S2; s4, modulating and controlling the intermittent frequency of the traction action; s5, if the deformation rate exceeds a preset safety threshold value, triggering a dynamic compensation mechanism; s6, forming a personalized traction scheme for the current patient; according to the spine traction control system and method, personalized spine traction control is achieved by monitoring the spine movement track and soft tissue stress changes in real time and dynamically adjusting traction force, angle and frequency, and therefore the treatment effect is remarkably improved, and the soft tissue injury risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of medical device control, and in particular to a method for collaborative control of dynamic parameters of massage and traction. Background Art

[0002] Spinal massage and traction is a non-invasive intervention method widely used in the rehabilitation treatment of spinal diseases. It is mainly used to relieve spinal misalignment, improve the stress state of intervertebral discs and loosen soft tissue adhesions. During the massage and traction process, external force is usually applied to specific segments of the spine to achieve spinal morphological correction and muscle relaxation. However, due to differences in individual spinal physiological curvature and complex soft tissue stress relaxation characteristics, traditional traction methods often use fixed traction parameters and lack personalized adaptation capabilities for different patients. In addition, existing traction equipment is usually based on a simple mechanical stretching mode, which makes it difficult to achieve real-time monitoring and dynamic adjustment of spinal motion trajectory, soft tissue deformation and stress distribution, resulting in unstable traction effect and may even cause damage to soft tissue.

[0003] The existing technology has the following problems during spinal massage and traction: First, the traction process fails to fully incorporate the biomechanical characteristics of the spine, resulting in the difficulty in accurately matching the direction and magnitude of the traction force with the real-time changes in the spinal morphology, affecting the correction effect; second, the soft tissue exhibits complex viscoelastic properties during the traction process, and the current traction frequency control method fails to adapt to the dynamic stress relaxation process of the soft tissue, which can easily lead to excessive traction or ineffective traction; third, the existing technology lacks a safe monitoring and feedback adjustment mechanism for the spinal motion trajectory and soft tissue deformation rate, and cannot effectively avoid overload or injury during the traction process. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a method for coordinated control of massage and traction dynamic parameters.

[0005] The massage and traction dynamic parameter coordinated control method comprises the following steps: S1: The inertial sensor array is used to collect the real-time three-dimensional motion trajectory of each spinal segment, and the flexible pressure sensor array is used to obtain the stress distribution and deformation data of the soft tissue in the target area; S2: The spinal motion trajectory data obtained in S1 is morphologically matched with the preset spinal physiological curvature standard template, the deviation between the current traction action and the target state is calculated, and the stress gradient distribution in the soft tissue deformation data is combined to generate coordinated adjustment instructions for traction force, angle and frequency; S3: According to the traction force adjustment instruction in S2, the servo motor dynamically adjusts the traction force. At the same time, the multi-degree-of-freedom manipulator is controlled to adjust the traction direction based on the angle adjustment instruction to ensure that the traction force vector direction is consistent with the normal direction of the target spinal segment. S4: Based on the frequency adjustment command generated in S2, combined with the current traction force and angle parameters, the intermittent frequency of the traction action is controlled through pulse width modulation to match the traction frequency with the stress relaxation characteristics of the soft tissue; S5: Real-time monitoring of the soft tissue deformation rate during S3 and S4. If the deformation rate exceeds the preset safety threshold, the dynamic compensation mechanism is triggered to reduce the traction force and the traction angle simultaneously. S6: When the spinal motion trajectory deviation is reduced to within the tolerance range and the soft tissue deformation rate is stable in the safe range, the current traction force, angle and frequency parameter combination is locked to form a personalized traction plan for the current patient.

[0006] Optionally, the S1 specifically includes: S11: An inertial sensor array is deployed in the target area. Each inertial sensor includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. Real-time motion data of each spinal segment in a six-degree-of-freedom space is obtained through combined measurement. Error compensation of the sensor data is performed based on a quaternion attitude fusion algorithm to obtain the three-dimensional motion trajectory of each spinal segment. S12: Based on the inertial sensor data obtained in S11, the Kalman filter algorithm is used to reduce the noise in the acceleration and angular velocity measurements. In combination with the zero-speed update technology of the fixed reference point, the drift error in low-dynamic motion is compensated. The displacement, rotation angle, and angular velocity of each spinal segment relative to the initial reference posture are then calculated in real time to form a complete motion trajectory data set. S13: Deploy a flexible pressure sensor array in the target area. The flexible pressure sensor array is composed of a 16×16 grid of PVDF thin film piezoelectric sensors with a spatial resolution of 5 mm×5 mm and collects contact pressure distribution data at a frequency of 50 Hz. S14: Based on the pressure distribution data obtained in S13, the stress gradient of the soft tissue at different positions is obtained using a gradient calculation method, and the local strain rate of the soft tissue is calculated in combination with the deformation model to form a soft tissue deformation data set; S15: The motion trajectory dataset obtained in S12 is time-synchronized with the soft tissue deformation dataset obtained in S14, and the data timestamp error is corrected based on the linear interpolation algorithm to ensure the time alignment of the motion trajectory and the stress-deformation data.

[0007] Optionally, the S14 specifically includes: S141: first, obtaining discrete stress values ​​collected by the flexible pressure sensor array at each grid position, and distinguishing different grid positions using row and column coordinates; S142: performing a differential operation on the stress values ​​measured at adjacent grid positions and, in combination with the center distances of the sensor units in the horizontal and vertical directions, calculating the stress change rates in the horizontal and vertical directions, respectively, thereby obtaining the stress gradient of the soft tissue at each grid position; S143: Based on the deformation model, the local displacement of each grid position in the horizontal and vertical directions is extracted, and the rate of change of the displacement in these two directions is calculated using the differential method to determine the local strain of the soft tissue; S144: By comparing the adjacent local strains before and after and combining the sampling time interval, the local strain rate of the soft tissue is obtained, and the stress gradient and strain rate data of all grid positions are integrated to form a soft tissue deformation dataset.

[0008] Optionally, the S2 specifically includes: S21: Based on the three-dimensional motion trajectory data of each spinal segment obtained in S1, a segmented curve model of the spinal motion trajectory is established according to the anatomical structure characteristics of the spinal segment, and the curvature of the trajectory data of each spinal segment is calculated to obtain the curvature distribution information of the spine in the current state; S22: calling a preset spinal physiological curvature standard template and constructing a standard curve of the spinal physiological curvature using a curve fitting method; and based on a morphological matching algorithm, comparing the spinal curvature distribution information obtained in S21 with the standard curve, calculating the deviation between the current spinal morphology and the standard curve, and obtaining the curvature error distribution for different spinal segments; S23: Based on the soft tissue stress gradient distribution data obtained in S14, coordinate matching is performed on the stress data to align it with the curvature error distribution calculated in S22, the effect of stress gradient changes on spinal morphological deviation is analyzed, and the correlation coefficient between curvature error and stress gradient is calculated; S24: Combined with the spinal morphological deviation-stress distribution mapping model constructed in S23, the traction adjustment parameters are calculated based on the mechanical optimization method, and based on the traction adjustment parameters, coordinated adjustment instructions of traction force, angle and frequency are generated.

[0009] Optionally, the S24 specifically includes: S241: When the absolute value of the correlation coefficient between the curvature error and the stress gradient is greater than a preset threshold, the adjustment amount of the traction force is calculated using the formula: ,in, For the The amount of traction adjustment for each spinal segment, is the curvature error of the segment, is the local stress gradient of the segment, and is the traction adjustment coefficient; S242: Calculate the traction angle adjustment based on the spinal curvature deviation direction and soft tissue stress distribution. The traction angle adjustment calculation formula is: ,in, For the The amount of traction angle adjustment for each spinal segment, is the gradient change of spinal curvature, Represents the ratio of the local stress gradient of the corresponding segment to the maximum stress gradient; S243: Based on the traction force and traction angle adjustment parameters calculated in S241 and S242, combined with the time variation characteristics of the soft tissue stress gradient, the traction frequency adapted to the soft tissue strain rate is calculated. The formula is: ,in, For the The traction frequency of each spinal segment, is the basic traction frequency, is the curvature error change rate of the segment, is the stress gradient change rate, and is the frequency adjustment coefficient; S244: Based on the traction adjustment parameters calculated in S241 to S243, a coordinated adjustment instruction of traction force, angle and frequency is generated.

[0010] Optionally, the S3 specifically includes: S31: Receive the traction adjustment command generated by S2 and input it to the servo motor control system to automatically correct the output of the servo motor so that the actual traction is consistent with the command value; S32: parsing the angle adjustment instruction provided by S2, and converting the angle adjustment instruction into the target rotation parameters of each joint; S33: sending the target rotation parameter to the robotic arm controller to drive the joints of the robotic arm to move so that the traction direction of the end of the traction device matches the normal direction of the target spinal segment; S34: Synchronously coordinate the traction force adjustment of the servo motor and the angle adjustment of the robotic arm to ensure that while adjusting the traction force, the traction direction changes are matched in real time, and the overall traction output is controlled within the safety threshold range.

[0011] Optionally, the S4 specifically includes: S41: Based on the traction frequency adjustment instruction generated in S2 and combined with the soft tissue stress gradient change data obtained in S14, the soft tissue stress relaxation characteristics are analyzed to determine the target traction frequency range so that the traction frequency matches the soft tissue strain rate change law; S42: Using a pulse width modulation method to control the driving signal of the traction device, dividing the traction cycle into a traction loading phase and a traction unloading phase, and adjusting the duty cycle of the pulse width according to the target traction frequency to control the intermittent time of the traction action; S43: Smoothing the pulse width modulation control signal to prevent sudden frequency changes from impacting soft tissue.

[0012] Optionally, the S41 specifically includes: S411: Based on the soft tissue stress gradient change data obtained in S14, the stress-strain relationship of the soft tissue during the traction process is extracted. Assume that the soft tissue The stress value at the moment is ; S412: Calculate the strain rate variation of soft tissue. Assume that the instantaneous strain of soft tissue is , and its rate of change is defined as: ,in, is the elastic modulus of soft tissue, is the linear damping coefficient, represents the time rate of change of stress; S413: Define the optimal traction frequency based on the soft tissue strain rate calculated in S412 ; S414: Based on the optimal traction frequency calculated in S413, set the target traction frequency range .

[0013] Optionally, the S5 specifically includes: S51: Calculate the deformation rate of soft tissue in a time series based on the soft tissue deformation data measured by the flexible pressure sensor array ; S52: Set the safe deformation rate threshold to , when the following conditions are met, the soft tissue deformation rate is determined to be out of limit: ; S53: When S52 determines that the deformation rate exceeds the safety threshold, the dynamic compensation mechanism is triggered to reduce the traction force and adjust the traction angle simultaneously.

[0014] Optionally, the S6 specifically includes: S61: Continuously monitor the deviation between the current spinal motion trajectory and the preset physiological curvature standard template, and confirm whether the deviation has fallen within the preset tolerance range; when the displacement and rotation angle of each spinal segment reach the target accuracy, the spinal motion trajectory is considered to have achieved the expected shape; S62: Observe whether the soft tissue deformation rate has been maintained within a safe range for a period of time. When the deformation rate is lower than the safety threshold within the set time window, it is determined that the traction load on the soft tissue is stable and there is no risk of excessive stress or tensile injury. S63: Under the conditions of S61 and S62, the current combination parameters of traction force, angle and frequency are locked; the traction force value during actual execution, the traction angle of the end of the multi-degree-of-freedom manipulator and the traction frequency of pulse width modulation are set as fixed control quantities, and recorded in the parameter library of the system control end to form a personalized traction plan for the current patient.

[0015] Beneficial effects of the present invention: The present invention realizes refined control of the traction process by real-time monitoring of the motion trajectory of each spinal segment and the stress distribution of soft tissue, combined with dynamic adjustment of traction force, traction angle and traction frequency. On the one hand, the inertial sensor array and the flexible pressure sensor array are used to obtain real-time status data of the spine and soft tissue. Combined with morphological matching and mechanical optimization methods, when spinal deviation or soft tissue stress exceeds the limit, the dynamic compensation mechanism is immediately triggered to reduce the traction force and adjust the traction angle to ensure a safe and smooth traction process. On the other hand, the intermittent frequency of the traction action is controlled by pulse width modulation, fully considering the stress relaxation characteristics of the soft tissue, so that the traction frequency can match the natural strain rate change law of the soft tissue, avoiding excessive stretching or ineffective traction, and improving the comfort and stability of treatment.

[0016] The present invention automatically generates personalized traction plans based on the spinal morphological deviations and soft tissue characteristics of different patients, and performs real-time correction and constraints on various parameters during the traction process to avoid the risk of tissue damage caused by inappropriate parameters. By locking the current traction parameter combination when the spinal motion trajectory deviation and soft tissue deformation rate both reach a safe state, better correction effects and faster recovery processes can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 Schematic diagram of a method for coordinated control of dynamic parameters of massage and traction according to an embodiment of the present invention; Figure 2 Schematic diagram of the process of forming a soft tissue deformation dataset according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0020] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0021] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1-Figure 2 As shown, the massage and traction dynamic parameter coordinated control method includes the following steps: S1: The inertial sensor array is used to collect the real-time three-dimensional motion trajectory of each spinal segment, and the flexible pressure sensor array is used to obtain the stress distribution and deformation data of the soft tissue in the target area; S2: The spinal motion trajectory data obtained in S1 is morphologically matched with the preset spinal physiological curvature standard template, the deviation between the current traction action and the target state is calculated, and the stress gradient distribution in the soft tissue deformation data is combined to generate coordinated adjustment instructions for traction force, angle and frequency; S3: According to the traction force adjustment instruction in S2, the servo motor dynamically adjusts the traction force. At the same time, the multi-degree-of-freedom manipulator is controlled to adjust the traction direction based on the angle adjustment instruction to ensure that the traction force vector direction is consistent with the normal direction of the target spinal segment. S4: Based on the frequency adjustment command generated in S2 and combined with the current traction force and angle parameters, the intermittent frequency of the traction action is controlled through pulse width modulation (PWM) to match the traction frequency with the stress relaxation characteristics of the soft tissue; S5: Real-time monitoring of the soft tissue deformation rate during S3 and S4. If the deformation rate exceeds the preset safety threshold, the dynamic compensation mechanism is triggered to reduce the traction force and the traction angle simultaneously. S6: When the spinal motion trajectory deviation is reduced to within the tolerance range and the soft tissue deformation rate is stable in the safe range, the current traction force, angle and frequency parameter combination is locked to form a personalized traction plan for the current patient.

[0023] S1 specifically includes: S11: Deploy an inertial sensor array in the target area. Each inertial sensor includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. The accelerometer is used to obtain linear motion information, the gyroscope obtains angular velocity information, and the magnetometer is used for heading angle correction. Through combined measurement, real-time motion data of each spinal segment in a six-degree-of-freedom space is obtained. Error compensation is performed on the sensor data based on a quaternion attitude fusion algorithm to obtain the three-dimensional motion trajectory of each spinal segment. The attitude calculation based on inertial sensors uses the quaternion fusion algorithm to define the quaternion , and its update formula is as follows: ,in, For the current moment The attitude quaternion of for Updated quaternion at the moment; is the three-axis angular velocity vector, where Represents the angular velocity of the X, Y, and Z axes respectively; Represents quaternion multiplication; is the sampling period of the sensor; In addition, the accelerometer and magnetometer are used for error correction, and the attitude quaternion is optimized based on gradient descent. The formula is: ,in, is the optimized attitude quaternion; is the correction step length; is the error gradient calculation function, which is obtained based on the gravitational acceleration and the magnetic field vector; S12: Based on the inertial sensor data acquired in S11, the Kalman filter algorithm is used to reduce the noise in the acceleration and angular velocity measurements. In combination with the zero-speed update technique (ZUPT) of a fixed reference point, drift errors in low-dynamic motion conditions are compensated. The displacement, rotation angle, and angular velocity of each spinal segment relative to the initial reference posture are then calculated in real time to form a complete motion trajectory dataset. Kalman filter (KF) is used for state estimation, and its prediction update equation is as follows: ; ;in, For the current moment The state vector, including the position ,speed and acceleration ; is the state vector at the previous moment; is the state transfer matrix, which describes the physical relationship of state change; is the control input matrix; For control input, including external force and inertial force; is the process noise; To measure vectors, include readings from accelerometers and gyroscopes; is the measurement matrix; To measure noise; In the case of low dynamic motion, zero speed update (ZUPT) is used for drift compensation. The compensation formula is: ,in, is the corrected speed; is the speed of the original calculation; is the Kalman gain; Update the assumed speed for zero speed; S13: Deploy a flexible pressure sensor array in the target area. The flexible pressure sensor array is composed of a 16×16 grid of PVDF thin film piezoelectric sensors with a spatial resolution of 5 mm×5 mm and collects contact pressure distribution data at a frequency of 50 Hz. S14: Based on the pressure distribution data obtained in S13, the stress gradient of the soft tissue at different positions is obtained using a gradient calculation method, and the local strain rate of the soft tissue is calculated in combination with the deformation model to form a soft tissue deformation data set; S15: The motion trajectory dataset obtained by S12 and the soft tissue deformation dataset obtained by S14 are time-synchronized, and the data timestamp error is corrected based on the linear interpolation algorithm to ensure the time alignment of the motion trajectory and the stress-deformation data, so as to improve the accuracy of subsequent traction force adjustment; the above steps improve the accuracy of spinal motion trajectory data through the combined measurement of the inertial sensor array and the quaternion posture fusion algorithm, and at the same time use Kalman filtering and zero-speed update technology to reduce the inertial measurement error; in addition, the flexible pressure sensor array is combined with the finite element analysis method and stress gradient calculation to achieve the precise acquisition of soft tissue stress distribution and deformation data, and ensure the consistency of the data through time synchronization processing, providing a reliable parameter basis for subsequent traction adjustment.

[0024] S14 specifically includes: S141: first, obtaining discrete stress values ​​collected by the flexible pressure sensor array at each grid position, and distinguishing different grid positions using row and column coordinates; S142: performing a differential operation on the stress values ​​measured at adjacent grid positions and, in combination with the center distances of the sensor units in the horizontal and vertical directions, calculating the stress change rates in the horizontal and vertical directions, respectively, thereby obtaining the stress gradient of the soft tissue at each grid position; S143: Based on the deformation model, the local displacement of each grid position in the horizontal and vertical directions is extracted, and the rate of change of the displacement in these two directions is calculated using the differential method to determine the local strain of the soft tissue; S144: By comparing the adjacent local strains before and after and combining them with the sampling time interval, the local strain rate of the soft tissue is obtained, and the stress gradient and strain rate data of all grid positions are integrated to form a soft tissue deformation data set. Through the above steps, the stress gradient and local strain rate obtained based on the finite difference method can more accurately reflect the stress-strain changes of the soft tissue during the traction process, providing data support for the subsequent real-time adjustment of the traction force and traction angle.

[0025] First, discrete stress data at different positions are obtained from the flexible pressure sensor array. Indicates time At this moment, the grid coordinates are The stress value measured at the sensor unit, where and Represents the index of the sensor grid in the row and column directions respectively; Then, the finite difference method is used to calculate the stress gradient of the soft tissue, and the horizontal spacing of the sensor grid is defined as , the vertical spacing is defined as , then at time At this moment, location The stress gradient can be expressed as a two-dimensional vector , its components are as follows according to the central difference formula: ; ,in, and Respectively indicate the position The measured stress value at ; The adjacent sensor units are Center distance of direction; Then, the local strain rate of the soft tissue is calculated by combining the deformation model, and and Respectively indicate time Time position Soft tissue Directional displacement, defining local strain for: ,in, ; , and then obtain the local strain rate by time difference , the expression is: ,in, is the local strain at the previous moment, is the sampling period; Finally, each Positional and The data are summarized to obtain a soft tissue deformation data set for subsequent traction force adjustment.

[0026] S2 specifically includes: S21: Based on the three-dimensional motion trajectory data of each spinal segment obtained in S1, a segmented curve model of the spinal motion trajectory is established according to the anatomical structure characteristics of the spinal segment, and the curvature of the trajectory data of each spinal segment is calculated to obtain the curvature distribution information of the spine in the current state; Specifically, the three-dimensional trajectory curve of the spine is set to ,in is the arc length parameter, the spinal curvature The calculation formula is as follows: ,in, is the first-order derivative of the spine trajectory, is the second-order derivative, Represents the cross product operation; obtained by calculation Indicates the curvature distribution of each spinal segment, which is used for subsequent matching calculation with the standard curve; S22: Calling a preset spinal physiological curvature standard template, which is composed of multiple sets of normal spinal curvature data, and constructing a standard curve of the spinal physiological curvature using a curve fitting method; and based on a morphological matching algorithm, comparing the spinal curvature distribution information obtained in S21 with the standard curve, calculating the deviation value between the current spinal morphology and the standard curve, and obtaining the curvature error distribution for different spinal segments; Specifically, the standard curve of spinal physiological curvature is set as , and its curvature distribution is , then the curvature error of the current spine is The calculation is as follows: , where a positive curvature error indicates that the curvature at that location is larger than the standard value, and a negative curvature indicates that the curvature is smaller than the standard value; for each spinal segment , defining the overall deviation , the expression is: ,in, For the The arc length range of the spinal segments is calculated Represents the overall deviation of the segment from the standard curve; S23: Based on the soft tissue stress gradient distribution data obtained in S14, coordinate matching is performed on the stress data to align it with the curvature error distribution calculated in S22, the effect of stress gradient changes on spinal morphological deviation is analyzed, and the correlation coefficient between curvature error and stress gradient is calculated; Specifically, the soft tissue stress gradient obtained by S14 is set to ,in Represents the coordinates of different measurement points on the spine surface. In order to match the stress gradient data with the curvature error data, a coordinate mapping function is used. To do the conversion: , the stress gradient is expressed in the arc length coordinate system ; Then, calculate the curvature error and stress gradient The correlation formula is: ,in, It represents the correlation coefficient between the two, which is used to judge the influence of stress distribution on spinal morphological deviation; if A larger absolute value indicates that the stress gradient change has a significant impact on the spinal curvature error. This data is used to subsequently calculate the traction adjustment parameters. S24: Combined with the spinal morphological deviation-stress distribution mapping model constructed in S23, the traction adjustment parameters are calculated based on the mechanical optimization method, and based on the traction adjustment parameters, coordinated adjustment instructions of traction force, angle and frequency are generated.

[0027] S24 specifically includes: S241: When the absolute value of the correlation coefficient between the curvature error and the stress gradient is greater than a preset threshold, the adjustment amount of the traction force is calculated, which is defined as the target traction force, which is the optimal force value required for spinal morphology recovery. The formula is: ,in, For the The amount of traction adjustment for each spinal segment, is the curvature error of the segment, is the local stress gradient of the segment, and is the traction force adjustment coefficient, which is determined based on the biomechanical characteristics of the spine; S242: Based on the spinal curvature deviation direction and soft tissue stress distribution, calculate the traction angle adjustment amount so that the traction force vector direction is consistent with the normal direction of the target spinal morphology. The traction angle adjustment calculation formula is: ,in, For the The amount of traction angle adjustment for each spinal segment, is the gradient change of spinal curvature, Represents the ratio of the local stress gradient of the corresponding segment to the maximum stress gradient; S243: Based on the traction force and traction angle adjustment parameters calculated in S241 and S242, combined with the time variation characteristics of the soft tissue stress gradient, the traction frequency adapted to the soft tissue strain rate is calculated. The formula is: ,in, For the The traction frequency of each spinal segment, is the basic traction frequency, is the curvature error change rate of the segment, is the stress gradient change rate, and is the frequency adjustment coefficient; S244: Based on the traction adjustment parameters calculated from S241 to S243, generate coordinated adjustment instructions for traction force, angle and frequency; the above steps calculate the traction adjustment parameters based on the real-time data of spinal morphological deviation and soft tissue stress distribution through a mechanical optimization method to ensure that the adjustment of traction force, angle and frequency matches the biomechanical characteristics of the spine.

[0028] S3 specifically includes: S31: Receive the traction adjustment command generated by S2 and input it to the servo motor control system to automatically correct the output of the servo motor so that the actual traction is consistent with the command value; S32: parsing the angle adjustment instruction provided by S2, and converting the angle adjustment instruction into the target rotation parameters of each joint; S33: sending the target rotation parameter to the robotic arm controller to drive the joints of the robotic arm to move so that the traction direction of the end of the traction device matches the normal direction of the target spinal segment; S34: Synchronously coordinate the traction force adjustment of the servo motor and the angle adjustment of the robotic arm to ensure that while adjusting the traction force, the traction direction changes are matched in real time, and the overall traction output is controlled within the safety threshold range; through the above steps, the traction force vector direction can always be consistent with the normal direction of the target spinal segment. At the same time, by synchronously coordinating the operation of the servo motor and the robotic arm, the traction adjustment process is ensured to be smooth, the stress mutation of the spine and soft tissue is reduced, and the safety and stability of the traction process are improved.

[0029] S4 specifically includes: S41: Based on the traction frequency adjustment instruction generated in S2 and combined with the soft tissue stress gradient change data obtained in S14, the soft tissue stress relaxation characteristics are analyzed to determine the target traction frequency range so that the traction frequency matches the soft tissue strain rate change law; S42: Using a pulse width modulation (PWM) method to control the driving signal of the traction device, dividing the traction cycle into a traction loading phase and a traction unloading phase, and adjusting the duty cycle of the pulse width according to the target traction frequency to control the intermittent time of the traction action; S43: Smoothing the pulse width modulation control signal to prevent frequency mutations from impacting the soft tissue and ensuring the stability of the traction loading and unloading processes, thereby optimizing the traction effect and bioadaptability; the above steps accurately control the intermittent frequency of the traction action through the pulse width modulation method, so that the traction frequency can adapt to the stress relaxation characteristics of the soft tissue, ensuring that the rhythm of the traction process conforms to the natural recovery law of the soft tissue, and by smoothing the PWM control signal, reducing the impact of frequency mutations on the soft tissue, thereby improving the safety and comfort of the spinal traction process.

[0030] S41 specifically includes: S411: Based on the soft tissue stress gradient change data obtained in S14, the stress-strain relationship of the soft tissue during the traction process is extracted, and a stress relaxation characteristic model is established. The stress value at the moment is Based on the stress relaxation characteristics of viscoelastic materials, an exponential decay model is used to describe the stress relaxation process: ,in, is the initial stress value, is the soft tissue relaxation time constant, which determines the rate of stress decay; S412: Calculate the strain rate variation of soft tissue. Assume that the instantaneous strain of soft tissue is , and its rate of change is defined as: In order to reflect the viscoelastic properties of soft tissue, a generalized viscoelastic model is introduced to express the change of strain rate with time: ,in, is the elastic modulus of soft tissue, is the linear damping coefficient, represents the time rate of change of stress; S413: Define the optimal traction frequency based on the soft tissue strain rate calculated in S412 , whose expression is: ,in, Calculated from S411, it represents the stress relaxation time constant of the soft tissue. This frequency corresponds to the optimal loading rhythm of the soft tissue, ensuring that the traction action is adapted to the natural recovery characteristics of the soft tissue. S414: Based on the optimal traction frequency calculated in S413, set the target traction frequency range ,in, , ,in, and They are empirical adjustment coefficients, which are set according to soft tissue type and individual characteristics to ensure that the traction frequency is within the tolerable range of the soft tissue and conforms to the relaxation characteristics.

[0031] S5 specifically includes: S51: Calculate the deformation rate of soft tissue in a time series based on the soft tissue deformation data measured by the flexible pressure sensor array , let the soft tissue deformation be , and its calculation formula is: ,in, For time The soft tissue deformation rate at a given moment, is the sampling period, is the deformation at the previous moment; S52: Set the safe deformation rate threshold to , when the following conditions are met, the soft tissue deformation rate is determined to be out of limit: ; S53: When S52 determines that the deformation rate exceeds the safety threshold, the dynamic compensation mechanism is triggered to reduce the traction force and adjust the traction angle simultaneously; The traction adjustment formula is: ,in, is the adjusted traction force, is the original traction force, The traction force adjustment coefficient ensures that the traction force reduction is proportional to the overrun range, thus achieving flexible adjustment. The formula for adjusting the traction angle is: ,in, is the adjusted traction angle, is the original traction angle, is the angle adjustment coefficient; the above steps calculate the soft tissue deformation rate in real time and make a judgment based on the set safety threshold. When the deformation rate exceeds the safety range, the dynamic compensation mechanism is automatically triggered to reduce the traction force and synchronously adjust the traction angle. This mechanism can prevent excessive stretching or damage of the soft tissue and improve the safety and bioadaptability of the traction process.

[0032] S6 specifically includes: S61: Continuously monitor the deviation between the current spinal motion trajectory and the preset physiological curvature standard template, and confirm whether the deviation has fallen within the preset tolerance range; when the displacement and rotation angle of each spinal segment reach the target accuracy, the spinal motion trajectory is considered to have achieved the expected shape; S62: Observe whether the soft tissue deformation rate has been maintained within a safe range for a period of time. When the deformation rate is lower than the safety threshold within the set time window, it is determined that the traction load on the soft tissue is stable and there is no risk of excessive stress or tensile injury. S63: Under the conditions of S61 and S62, the current combination parameters of traction force, angle and frequency are locked; the traction force value during actual execution, the traction angle of the end of the multi-degree-of-freedom manipulator and the traction frequency of pulse width modulation are set as fixed control quantities, and recorded in the parameter library of the system control end to form a personalized traction plan for the current patient; through the above steps, under the conditions that the spinal motion trajectory deviation and the soft tissue deformation rate meet the safety and accuracy requirements, the current combination parameters of traction force, angle and frequency are locked, avoiding unnecessary frequent adjustments and reducing the risk of patient discomfort or tissue damage; at the same time, a personalized traction plan is formed to provide a precise and stable traction execution mode for the subsequent treatment process, thereby improving the efficiency and safety of spinal massage and traction.

[0033] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0034] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for collaborative control of dynamic parameters of massage and traction, characterized in that: The following steps are involved: S1: The inertial sensor array is used to collect the real-time three-dimensional motion trajectory of each spinal segment, and the flexible pressure sensor array is used to obtain the stress distribution and deformation data of the soft tissue in the target area; S2: The spinal motion trajectory data obtained in S1 is morphologically matched with the preset spinal physiological curvature standard template, the deviation between the current traction action and the target state is calculated, and the stress gradient distribution in the soft tissue deformation data is combined to generate coordinated adjustment instructions for traction force, angle and frequency; S3: According to the traction force adjustment instruction in S2, the servo motor dynamically adjusts the traction force. At the same time, the multi-degree-of-freedom manipulator is controlled to adjust the traction direction based on the angle adjustment instruction to ensure that the traction force vector direction is consistent with the normal direction of the target spinal segment. S4: Based on the frequency adjustment command generated in S2, combined with the current traction force and angle parameters, the intermittent frequency of the traction action is controlled through pulse width modulation to match the traction frequency with the stress relaxation characteristics of the soft tissue; S5: Real-time monitoring of the soft tissue deformation rate during S3 and S4. If the deformation rate exceeds the preset safety threshold, the dynamic compensation mechanism is triggered to reduce the traction force and the traction angle simultaneously. S6: When the spinal motion trajectory deviation is reduced to within the tolerance range and the soft tissue deformation rate is stable in the safe range, the current traction force, angle and frequency parameter combination is locked to form a personalized traction plan for the current patient.

2. The massage and traction dynamic parameter coordinated control method according to claim 1, characterized in that: Said S1 specifically includes: S11: An inertial sensor array is deployed in the target area. Each inertial sensor includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. Real-time motion data of each spinal segment in a six-degree-of-freedom space is obtained through combined measurement. Error compensation of the sensor data is performed based on a quaternion attitude fusion algorithm to obtain the three-dimensional motion trajectory of each spinal segment. S12: Based on the inertial sensor data obtained in S11, the Kalman filter algorithm is used to reduce the noise in the acceleration and angular velocity measurements. In combination with the zero-speed update technology of the fixed reference point, the drift error in low-dynamic motion is compensated. The displacement, rotation angle, and angular velocity of each spinal segment relative to the initial reference posture are then calculated in real time to form a complete motion trajectory data set. S13: Deploy a flexible pressure sensor array in the target area. The flexible pressure sensor array is composed of a 16×16 grid of PVDF thin film piezoelectric sensors with a spatial resolution of 5 mm×5 mm and collects contact pressure distribution data at a frequency of 50 Hz. S14: Based on the pressure distribution data obtained in S13, the stress gradient of the soft tissue at different positions is obtained using a gradient calculation method, and the local strain rate of the soft tissue is calculated in combination with the deformation model to form a soft tissue deformation data set; S15: The motion trajectory dataset obtained in S12 is time-synchronized with the soft tissue deformation dataset obtained in S14, and the data timestamp error is corrected based on the linear interpolation algorithm to ensure the time alignment of the motion trajectory and the stress-deformation data.

3. The massage and traction dynamic parameter coordinated control method according to claim 1, characterized in that: The S14 specifically includes: S141: first, obtaining discrete stress values ​​collected by the flexible pressure sensor array at each grid position, and distinguishing different grid positions using row and column coordinates; S142: performing a differential operation on the stress values ​​measured at adjacent grid positions and, in combination with the center distances of the sensor units in the horizontal and vertical directions, calculating the stress change rates in the horizontal and vertical directions, respectively, thereby obtaining the stress gradient of the soft tissue at each grid position; S143: Based on the deformation model, the local displacement of each grid position in the horizontal and vertical directions is extracted, and the rate of change of the displacement in these two directions is calculated using the differential method to determine the local strain of the soft tissue; S144: By comparing the adjacent local strains before and after and combining the sampling time interval, the local strain rate of the soft tissue is obtained, and the stress gradient and strain rate data of all grid positions are integrated to form a soft tissue deformation dataset.

4. The massage and traction dynamic parameter coordinated control method according to claim 1, characterized in that: The S2 specifically includes: S21: Based on the three-dimensional motion trajectory data of each spinal segment obtained in S1, a segmented curve model of the spinal motion trajectory is established according to the anatomical structure characteristics of the spinal segment, and the curvature of the trajectory data of each spinal segment is calculated to obtain the curvature distribution information of the spine in the current state; S22: calling a preset spinal physiological curvature standard template and constructing a standard curve of the spinal physiological curvature using a curve fitting method; and based on a morphological matching algorithm, comparing the spinal curvature distribution information obtained in S21 with the standard curve, calculating the deviation between the current spinal morphology and the standard curve, and obtaining the curvature error distribution for different spinal segments; S23: Based on the soft tissue stress gradient distribution data obtained in S14, coordinate matching is performed on the stress data to align it with the curvature error distribution calculated in S22, the effect of stress gradient changes on spinal morphological deviation is analyzed, and the correlation coefficient between curvature error and stress gradient is calculated; S24: Combined with the spinal morphological deviation-stress distribution mapping model constructed in S23, the traction adjustment parameters are calculated based on the mechanical optimization method, and based on the traction adjustment parameters, coordinated adjustment instructions of traction force, angle and frequency are generated.

5. The massage and traction dynamic parameter coordinated control method according to claim 4, characterized in that: The S24 specifically includes: S241: When the absolute value of the correlation coefficient between the curvature error and the stress gradient is greater than a preset threshold, the adjustment amount of the traction force is calculated using the formula: ,in, For the The amount of traction adjustment for each spinal segment, is the curvature error of the segment, is the local stress gradient of the segment, and is the traction adjustment coefficient; S242: Calculate the traction angle adjustment based on the spinal curvature deviation direction and soft tissue stress distribution. The traction angle adjustment calculation formula is: ,in, For the The amount of traction angle adjustment for each spinal segment, is the gradient change of spinal curvature, Represents the ratio of the local stress gradient of the corresponding segment to the maximum stress gradient; S243: Based on the traction force and traction angle adjustment parameters calculated in S241 and S242, combined with the time variation characteristics of the soft tissue stress gradient, the traction frequency adapted to the soft tissue strain rate is calculated. The formula is: ,in, For the The traction frequency of each spinal segment, is the basic traction frequency, is the curvature error change rate of the segment, is the stress gradient change rate, and is the frequency adjustment coefficient; S244: Based on the traction adjustment parameters calculated in S241 to S243, a coordinated adjustment instruction of traction force, angle and frequency is generated.

6. The massage and traction dynamic parameter coordinated control method according to claim 1, characterized in that: The S3 specifically includes: S31: Receive the traction adjustment command generated by S2 and input it to the servo motor control system to automatically correct the output of the servo motor so that the actual traction is consistent with the command value; S32: parsing the angle adjustment instruction provided by S2, and converting the angle adjustment instruction into the target rotation parameters of each joint; S33: sending the target rotation parameter to the robotic arm controller to drive the joints of the robotic arm to move so that the traction direction of the end of the traction device matches the normal direction of the target spinal segment; S34: Synchronously coordinate the traction force adjustment of the servo motor and the angle adjustment of the robotic arm to ensure that while adjusting the traction force, the traction direction changes are matched in real time, and the overall traction output is controlled within the safety threshold range.

7. The massage and traction dynamic parameter coordinated control method according to claim 1, characterized in that: The S4 specifically includes: S41: Based on the traction frequency adjustment instruction generated in S2 and combined with the soft tissue stress gradient change data obtained in S14, the soft tissue stress relaxation characteristics are analyzed to determine the target traction frequency range so that the traction frequency matches the soft tissue strain rate change law; S42: Using a pulse width modulation method to control the driving signal of the traction device, dividing the traction cycle into a traction loading phase and a traction unloading phase, and adjusting the duty cycle of the pulse width according to the target traction frequency to control the intermittent time of the traction action; S43: Smoothing the pulse width modulation control signal to prevent sudden frequency changes from impacting soft tissue.

8. The massage and traction dynamic parameter coordinated control method according to claim 7, characterized in that: The S41 specifically includes: S411: Based on the soft tissue stress gradient change data obtained in S14, the stress-strain relationship of the soft tissue during the traction process is extracted. Assume that the soft tissue The stress value at the moment is ; S412: Calculate the strain rate variation of soft tissue. Assume that the instantaneous strain of soft tissue is , and its rate of change is defined as: ,in, is the elastic modulus of soft tissue, is the linear damping coefficient, represents the time rate of change of stress; S413: Define the optimal traction frequency based on the soft tissue strain rate calculated in S412 ; S414: Based on the optimal traction frequency calculated in S413, set the target traction frequency range .

9. The massage and traction dynamic parameter coordinated control method according to claim 1, characterized in that: The S5 specifically includes: S51: Calculate the deformation rate of soft tissue in a time series based on the soft tissue deformation data measured by the flexible pressure sensor array ; S52: Set the safe deformation rate threshold to , when the following conditions are met, the soft tissue deformation rate is determined to be out of limit: ; S53: When S52 determines that the deformation rate exceeds the safety threshold, the dynamic compensation mechanism is triggered to reduce the traction force and adjust the traction angle simultaneously.

10. The massage and traction dynamic parameter coordinated control method according to claim 1, characterized in that: The S6 specifically includes: S61: Continuously monitor the deviation between the current spinal motion trajectory and the preset physiological curvature standard template, and confirm whether the deviation has fallen within the preset tolerance range; when the displacement and rotation angle of each spinal segment reach the target accuracy, the spinal motion trajectory is considered to have achieved the expected shape; S62: Observe whether the soft tissue deformation rate has been maintained within a safe range for a period of time. When the deformation rate is lower than the safety threshold within the set time window, it is determined that the traction load on the soft tissue is stable and there is no risk of excessive stress or tensile injury. S63: Under the conditions of S61 and S62, the current combination parameters of traction force, angle and frequency are locked; the traction force value during actual execution, the traction angle of the end of the multi-degree-of-freedom manipulator and the traction frequency of pulse width modulation are set as fixed control quantities, and recorded in the parameter library of the system control end to form a personalized traction plan for the current patient.

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