Artificial intelligence-based rehabilitation training monitoring system for orthopedic joint replacement surgery

Abstract

The present invention relates to the field of rehabilitation training monitoring technology, and in particular to an artificial intelligence-based postoperative rehabilitation training monitoring system for orthopedic joint replacement, comprising a rehabilitation supervision platform, an information collection unit, a recovery evaluation unit, a training supervision unit, a rehabilitation monitoring unit, a periodic recovery evaluation unit and a management response unit; the present invention analyzes the target patient's rehabilitation joint training from two points of view, namely, the training appearance performance and the internal rehabilitation monitoring, so as to understand the rehabilitation status of the target patient in the current rehabilitation cycle, and performs information fusion-type periodic recovery evaluation analysis by means of information feedback and information fusion, and rationally adjusts and manages subsequent rehabilitation training decisions based on the information feedback, so as to speed up the recovery rate of the target patient's rehabilitation joint, and analyzes from the perspective of training appearance performance, and analyzes in combination with rehabilitation training information, so as to understand the recovery status of the target patient's rehabilitation joint, and then reasonably adjust the rehabilitation training program.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation training monitoring, and in particular to an artificial intelligence-based post-orthopedic joint replacement rehabilitation training monitoring system. Background Art

[0002] Rehabilitation functional training refers to the patient's self-exercise, massage, muscle stretching, and joint movement to restore normal limb movement in addition to doctor's treatment in the case of bone and joint injuries. The purpose of rehabilitation functional training is to restore the inherent functions of the joints of the trunk and limbs, and prevent various changes that are not conducive to joint movement due to bleeding, such as joint contracture, ligament shortening, muscle stiffness, and synovial adhesion. Even if there is hematoma, edema, or ossification around the joint, the scar can be relaxed and softened through movement, and it will no longer affect joint movement.

[0003] However, there are still some shortcomings in the existing monitoring technology. There is a lack of online monitoring and data tracking during the monitoring process. There is a lack of effective and comprehensive supervision of the speed of physical recovery and changes in physical health, which is not conducive to a comprehensive understanding of the patient's recovery status. It is also impossible to make reasonable supervision adjustments based on the performance of the patient's rehabilitation joints during rehabilitation training. At the same time, it is impossible to make rehabilitation training decision adjustments based on the rehabilitation status of the current rehabilitation cycle, which is not conducive to the patient's stable recovery.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an artificial intelligence-based orthopedic joint replacement postoperative rehabilitation training monitoring system to solve the above-mentioned technical defects. The present invention analyzes the target patient's rehabilitation joint training appearance and internal rehabilitation monitoring from two points of view, so as to understand the rehabilitation status of the target patient in the current rehabilitation cycle, and performs information fusion-type periodic recovery evaluation analysis through information feedback and information fusion. According to the information feedback, the subsequent rehabilitation training decisions are rationally adjusted and managed to speed up the recovery rate of the target patient's rehabilitation joints, and recovery score supervision and processing are performed from the perspective of training appearance performance, and combined with rehabilitation training information for analysis, which helps to improve the accuracy of the analysis results and understand the recovery status of the target patient's rehabilitation joints. On the one hand, it helps to reasonably adjust the rehabilitation training program according to the information feedback, and on the other hand, it helps to provide data support for subsequent recovery management, so as to achieve the expected rehabilitation training effect.

[0006] The purpose of the present invention can be achieved by the following technical solutions: an artificial intelligence-based orthopedic joint replacement postoperative rehabilitation training monitoring system, comprising a rehabilitation supervision platform, an information collection unit, a recovery assessment unit, a training supervision unit, a rehabilitation monitoring unit, a periodic recovery assessment unit, and a management response unit;

[0007] The information collection unit is used to collect the training appearance data and rehabilitation training information of the target patient, and send the training appearance data and rehabilitation training information to the recovery evaluation unit and the training supervision unit respectively;

[0008] After receiving the rehabilitation training information, the training supervision unit immediately performs rehabilitation training effectiveness supervision analysis on the rehabilitation training information, compares and analyzes the obtained training evaluation index R, and obtains the recovery impact index HZ;

[0009] After receiving the training appearance data, the restoration evaluation unit immediately performs restoration score supervision processing on the training appearance data, compares and analyzes the obtained restoration score index K, and obtains a standard signal and a deviation signal;

[0010] The rehabilitation monitoring unit is used to respond to the supervision instruction, collect the rehabilitation intrinsic data of the target patient's rehabilitation joint, and perform joint cycle rehabilitation feedback analysis on the rehabilitation intrinsic data to obtain the actual rehabilitation coefficient H;

[0011] The periodic recovery evaluation unit is used to perform information fusion periodic recovery evaluation analysis on the recovery score index K, compare and analyze the obtained periodic rehabilitation score coefficients, and send the obtained expected signal and adjustment signal to the management response unit.

[0012] Preferably, the rehabilitation training effectiveness supervision and analysis process of the training supervision unit is as follows:

[0013] The current rehabilitation cycle of the target patient is collected, and at the same time, the total number of rehabilitation training sessions within the current rehabilitation cycle of the target patient is obtained, the duration between the start time and the end time of each rehabilitation training session of the target patient is collected, and the duration between the start time and the end time of the rehabilitation training is set as the time threshold, and the rehabilitation training information of the rehabilitation joints of the target patient within the time threshold is obtained, and the rehabilitation training information includes the effective rehabilitation training duration and the actual interval duration, and then the difference between the effective rehabilitation training duration and the standard training duration is set as the training time deviation value, the difference between the actual interval duration and the standard interval duration is obtained, and the difference between the actual interval duration and the standard interval duration is set as the training deviation value, and the number of training deviation values ​​greater than the preset training deviation value threshold is set as the deviation impact value;

[0014] The effective rehabilitation training duration represents a value obtained by subtracting the rest duration during the rehabilitation training from the time threshold.

[0015] Preferably, the training time-effectiveness deviation value and the deviation impact value are labeled YK and PY respectively, and the training time-effectiveness deviation value YK and the deviation impact value PY are substituted into the formula The training evaluation index is obtained, where f1 and f2 are respectively the preset weight factor coefficients of the training time deviation value and the deviation impact value, f3 is the preset fault tolerance factor coefficient, f1, f2 and f3 are all greater than zero, and R is the training evaluation index. The training evaluation index R is compared and analyzed with the preset training evaluation index range stored in the training supervision unit:

[0016] If the training evaluation index R is greater than the maximum value in the preset training evaluation index range, it is determined to be a third-level recovery impact;

[0017] If the training evaluation index R falls within the preset training evaluation index range, it is determined to be a secondary recovery impact;

[0018] If the training evaluation index R is less than the minimum value in the preset training evaluation index range, it is determined to be a first-level recovery influence, and the first-level recovery influence, the second-level recovery influence and the third-level recovery influence are set as the recovery influence index HZ, HZ=1, 2, 3.

[0019] Preferably, the recovery score supervision processing process of the recovery assessment unit is as follows:

[0020] The training appearance data of the target patient's rehabilitation joint within the time threshold is obtained, and the training appearance data includes a rehabilitation joint surface feature image and a current joint range of motion. The rehabilitation joint surface feature image is preprocessed, and the preprocessing includes scaling and cropping. The bulge volume of the rehabilitation joint in the surface feature image is obtained, and then the ratio between the bulge volume and the preset bulge volume is obtained, and the ratio between the bulge volume and the preset bulge volume is set as the bulge disappearance value. At the same time, the ratio between the current joint range of motion and the doctor's preset joint range of motion is obtained, and the ratio between the current joint range of motion and the doctor's preset joint range of motion is set as the joint recovery value.

[0021] Preferably, the bulge disappearance value and the joint recovery value are labeled GT and GH respectively, and the bulge disappearance value GT and the joint recovery value GH are substituted into the formula The recovery score index K is obtained, wherein a1, a2 and a3 are preset proportional factor coefficients of the bulge disappearance value, the joint recovery value and the recovery influence index HZ, respectively, and a1, a2 and a3 are all greater than zero. The recovery score index K is compared with the preset recovery score index threshold value stored in the recovery evaluation unit for analysis:

[0022] If the recovery score index K is greater than or equal to the preset recovery score index threshold, a standard signal is generated;

[0023] If the recovery score index K is less than a preset recovery score index threshold, a deviation signal is generated.

[0024] Preferably, the joint cycle rehabilitation feedback analysis process of the rehabilitation monitoring unit is as follows:

[0025] Obtain the target patient's internal rehabilitation data during the current rehabilitation cycle. The internal rehabilitation data includes pain information and muscle strength recovery information. The pain information and muscle strength recovery information are labeled as TT and JH respectively. Substitute the pain information TT and muscle strength recovery information JH into the formula The actual recovery coefficient H is obtained, wherein α and β are preset weighting coefficients of pain information and muscle strength recovery information respectively, and η is a preset correction factor coefficient, and α, β and η are all greater than zero;

[0026] The pain information indicates the amount of reduction in pain level of the current rehabilitation joint during the current rehabilitation cycle; the muscle strength recovery information indicates the amount of change in muscle strength level of the current rehabilitation joint before and after the current rehabilitation cycle;

[0027] Preferably, the information fusion periodic recovery evaluation analysis process of the periodic recovery evaluation unit is as follows:

[0028] A rectangular coordinate system is established with the number of rehabilitation training as the X-axis and the recovery score index K as the Y-axis, and a recovery score index curve is drawn by plotting points. At the same time, a preset recovery score index curve is drawn in the coordinate system, and then the corresponding plotted points located above or above the preset recovery score index curve in the recovery score index curve are obtained, and the corresponding plotted points located above or above the preset recovery score index curve in the recovery score index curve are set as effective recovery points, and then the number of effective recovery points is obtained, and the number of effective recovery points is set as the effective recovery value F. It should be noted that the larger the value of the effective recovery value, the better the expected effect of the current rehabilitation training;

[0029] The value obtained by multiplying the effective recovery value F and the actual recovery coefficient H after data normalization is set as the periodic rehabilitation score coefficient, and the periodic rehabilitation score coefficient is compared and analyzed with the preset periodic rehabilitation score coefficient threshold stored in the periodic recovery evaluation unit:

[0030] If the ratio between the cycle rehabilitation score coefficient and the preset cycle rehabilitation score coefficient threshold is less than 1, an adjustment signal is generated;

[0031] If the ratio between the periodic rehabilitation score coefficient and the preset periodic rehabilitation score coefficient threshold is greater than or equal to 1, an expected signal is generated.

[0032] The beneficial effects of the present invention are as follows:

[0033] (1) The present invention analyzes the target patient's rehabilitation joint training appearance and internal rehabilitation monitoring to understand the target patient's rehabilitation status in the current rehabilitation cycle, and performs information fusion-based periodic recovery evaluation and analysis through information feedback and information fusion to understand whether the target patient's recovery in the current rehabilitation cycle has achieved the expected effect. The subsequent rehabilitation training decisions are rationally adjusted and managed based on the information feedback to accelerate the recovery rate of the target patient's rehabilitation joints.

[0034] (2) The present invention performs recovery score supervision from the perspective of training appearance performance, and analyzes it in combination with rehabilitation training information, which helps to improve the accuracy of the analysis results and understand the recovery status of the target patient's rehabilitation joints. On the one hand, it helps to reasonably adjust the rehabilitation training program according to the information feedback, and on the other hand, it helps to provide data support for subsequent recovery management. By analyzing the rehabilitation training information, it is possible to understand the impact of rehabilitation training on the rehabilitation joints, so as to adjust the rehabilitation training of the target patient according to the information feedback, so as to achieve the expected rehabilitation training effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings;

[0036] Figure 1 It is a flowchart of the system of the present invention;

[0037] Figure 2 This is a local analysis diagram of Example 1 of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Embodiment 1:

[0040] See also Figure 1 to Figure 2As shown, the present invention is an artificial intelligence-based postoperative rehabilitation training monitoring system for orthopedic joint replacement, including a rehabilitation supervision platform, an information collection unit, a recovery assessment unit, a training supervision unit, a rehabilitation monitoring unit, a periodic recovery assessment unit and a management response unit, the rehabilitation supervision platform is connected to the information collection unit and the rehabilitation monitoring unit in a one-way communication, the information collection unit is connected to the training supervision unit and the recovery assessment unit in a one-way communication, the training supervision unit is connected to the recovery assessment unit in a one-way communication, the rehabilitation monitoring unit and the recovery assessment unit are connected to the periodic recovery assessment unit in a one-way communication, and the recovery assessment unit and the periodic recovery assessment unit are connected to the management response unit in a one-way communication;

[0041] When the rehabilitation supervision platform generates a supervision instruction, it sends the supervision instruction to the information collection unit and the rehabilitation monitoring unit. After receiving the supervision instruction, the information collection unit immediately collects the training appearance data and rehabilitation training information of the target patient, and sends the training appearance data and rehabilitation training information to the recovery evaluation unit and the training supervision unit respectively. After receiving the rehabilitation training information, the training supervision unit immediately conducts rehabilitation training effectiveness supervision analysis on the rehabilitation training information to understand the rehabilitation effect of the rehabilitation training on the rehabilitation joint, so as to provide data support for subsequent rehabilitation management. The specific rehabilitation training effectiveness supervision analysis process is as follows:

[0042] The current rehabilitation cycle of the target patient is collected, and the total number of rehabilitation training sessions in the current rehabilitation cycle of the target patient is obtained. The duration between the start time and the end time of each rehabilitation training session of the target patient is collected and set as a time threshold, and rehabilitation training information of the rehabilitation joints of the target patient within the time threshold is obtained. The rehabilitation training information includes an effective rehabilitation training duration and an actual interval duration, and then the difference between the effective rehabilitation training duration and the standard training duration is set as a training time deviation value, the difference between the actual interval duration and the standard interval duration is obtained, and the difference between the actual interval duration and the standard interval duration is set as a training deviation value, and the number of training deviation values ​​greater than a preset training deviation value threshold is set as a deviation impact value;

[0043] In the embodiment of the present invention, the effective rehabilitation training time refers to the value obtained by subtracting the rest time during the rehabilitation training from the time threshold. It should be noted that the more the effective rehabilitation training time conforms to the standard training time, the better the effect of the rehabilitation training is, and the smaller the rehabilitation effect on the rehabilitation joint is;

[0044] The training time-effectiveness deviation value and the deviation impact value are labeled YK and PY respectively. Substitute the training time-effectiveness deviation value YK and the deviation impact value PY into the formula The training evaluation index is obtained, where f1 and f2 are respectively the preset weight factor coefficients of the training time deviation value and the deviation impact value, f3 is the preset fault tolerance factor coefficient, f1, f2 and f3 are all greater than zero, and R is the training evaluation index. The training evaluation index R is compared and analyzed with the preset training evaluation index range stored in the training supervision unit:

[0045] If the training evaluation index R is greater than the maximum value in the preset training evaluation index range, it is determined to be a third-level recovery impact;

[0046] If the training evaluation index R falls within the preset training evaluation index range, it is determined to be a secondary recovery impact;

[0047] If the training evaluation index R is less than the minimum value in the preset training evaluation index range, it is determined to be a first-level recovery impact, wherein the first-level recovery impact, the second-level recovery impact and the third-level recovery impact have a greater degree of rehabilitation training impact, and the first-level recovery impact, the second-level recovery impact and the third-level recovery impact are set as the recovery impact index HZ, HZ=1, 2, 3, that is, when the recovery impact index HZ=1, it indicates a first-level recovery impact, when the recovery impact index HZ=2, it indicates a second-level recovery impact, when the recovery impact index HZ=3, it indicates a third-level recovery impact, the recovery impact index HZ is sent to the management response unit via the recovery evaluation unit, and after receiving the recovery impact index HZ, the management response unit immediately displays the preset warning text of the recovery impact index HZ, so as to adjust the rehabilitation training of the target patient according to the information feedback, so as to achieve the expected rehabilitation training effect;

[0048] After receiving the training appearance data, the recovery assessment unit immediately performs recovery score supervision processing on the training appearance data to understand the recovery status of the target patient's rehabilitation joints, so as to provide data support for subsequent recovery management. The specific recovery score supervision processing process is as follows:

[0049] Acquire the training appearance data of the rehabilitation joint of the target patient within the time threshold, the training appearance data includes the rehabilitation joint surface feature image and the current joint range of motion, preprocess the rehabilitation joint surface feature image, the preprocessing includes scaling, cropping, etc., obtain the bulge volume of the rehabilitation joint in the surface feature image, and then obtain the ratio between the bulge volume and the preset bulge volume, and set the ratio between the bulge volume and the preset bulge volume as the bulge disappearance value, and at the same time obtain the ratio between the current joint range of motion and the doctor's preset joint range of motion, and set the ratio between the current joint range of motion and the doctor's preset joint range of motion as the joint recovery value;

[0050] In the embodiment of the present invention, the target patient uploads the video of the rehabilitation exercise to the server through the existing electronic device, obtains the surface feature image of the rehabilitation joint and the current joint range of motion from the video of the rehabilitation exercise, and performs quantitative analysis on the surface feature image of the rehabilitation joint and the current joint range of motion, so as to set rehabilitation goals and precautions for subsequent rehabilitation training;

[0051] The bulge disappearance value and joint recovery value are labeled GT and GH respectively. Substitute the bulge disappearance value GT and the joint recovery value GH into the formula The recovery score index is obtained, wherein a1, a2 and a3 are preset proportional factor coefficients of the bulge disappearance value, joint recovery value and recovery influence index HZ respectively. The proportional factor coefficient is used to correct the deviation of various parameters in the formula calculation process, so as to make the calculation result more accurate. a1, a2 and a3 are all greater than zero, K is the recovery score index, and the recovery score index K is compared with the preset recovery score index threshold value stored in the recovery evaluation unit for analysis:

[0052] If the recovery score index K is greater than or equal to the preset recovery score index threshold, it is determined to be qualified training and a standard signal is generated;

[0053] If the recovery score index K is less than the preset recovery score index threshold, it is judged as deviation training, a deviation signal is generated, and the standard signal and the deviation signal are sent to the management response unit. After receiving the standard signal and the deviation signal, the management response unit immediately displays the preset warning text of the standard signal and the deviation signal, so as to understand the recovery status of the target patient's rehabilitation joints based on the information feedback, and at the same time provide indirect feedback on whether the current rehabilitation training of the target patient's rehabilitation joints meets expectations, so as to reasonably adjust the rehabilitation training program.

[0054] Embodiment 2:

[0055] The rehabilitation monitoring unit is used to respond to supervision instructions, collect the rehabilitation intrinsic data of the target patient's rehabilitation joints, and perform joint cycle rehabilitation feedback analysis on the rehabilitation intrinsic data in order to understand the rehabilitation status of the target patient in the current rehabilitation cycle. The specific joint cycle rehabilitation feedback analysis process is as follows:

[0056] Obtain the target patient's internal rehabilitation data during the current rehabilitation cycle. The internal rehabilitation data includes pain information and muscle strength recovery information. The pain information and muscle strength recovery information are labeled as TT and JH respectively. Substitute the pain information TT and muscle strength recovery information JH into the formula The actual rehabilitation coefficient is obtained, wherein α and β are respectively the preset weighting coefficients of pain information and muscle strength recovery information, η is the preset correction factor coefficient, α, β and η are all greater than zero, and H is the actual rehabilitation coefficient;

[0057] In the embodiment of the present invention, the pain information indicates the reduction in the pain level of the current rehabilitation joint in the current rehabilitation cycle. It should be noted that the pain information reflects the rehabilitation status of the current rehabilitation joint. The greater the pain information, the better the recovery.

[0058] In the embodiment of the present invention, the muscle strength recovery information indicates the change in muscle strength level of the current rehabilitation joint before and after the current rehabilitation cycle. It should be noted that the larger the value of the muscle strength recovery information is, the better the muscle strength recovery is in the current rehabilitation cycle.

[0059] The periodic recovery evaluation unit is used to perform information fusion periodic recovery evaluation analysis on the recovery score index K, so as to understand whether the recovery of the target patient's current rehabilitation cycle has achieved the expected effect through information feedback. The specific information fusion periodic recovery evaluation analysis process is as follows:

[0060] A rectangular coordinate system is established with the number of rehabilitation training as the X-axis and the recovery score index K as the Y-axis, and a recovery score index curve is drawn by plotting points. At the same time, a preset recovery score index curve is drawn in the coordinate system, and then the corresponding plotted points located above or above the preset recovery score index curve in the recovery score index curve are obtained, and the corresponding plotted points located above or above the preset recovery score index curve in the recovery score index curve are set as effective recovery points, and then the number of effective recovery points is obtained, and the number of effective recovery points is set as the effective recovery value F. It should be noted that the larger the value of the effective recovery value, the better the expected effect of the current rehabilitation training;

[0061] The value obtained by multiplying the effective recovery value F and the actual recovery coefficient H after data normalization is set as the periodic rehabilitation score coefficient, and the periodic rehabilitation score coefficient is compared and analyzed with the preset periodic rehabilitation score coefficient threshold stored in the periodic recovery evaluation unit:

[0062] If the ratio between the cycle rehabilitation score coefficient and the preset cycle rehabilitation score coefficient threshold is less than 1, an adjustment signal is generated;

[0063] If the ratio between the cycle rehabilitation score coefficient and the preset cycle rehabilitation score coefficient threshold is greater than or equal to 1, an expected signal is generated, and the expected signal and the adjustment signal are sent to the management response unit. After receiving the expected signal and the adjustment signal, the management response unit immediately displays the preset warning text of the expected signal and the adjustment signal, so as to intuitively understand the recovery status of the target patient's rehabilitation joint according to the information feedback, and at the same time, rationally adjust the subsequent rehabilitation training decision according to the information feedback, so as to speed up the recovery rate of the target patient's rehabilitation joint;

[0064] In summary, the present invention analyzes the target patient's rehabilitation joint training appearance and internal rehabilitation monitoring to understand the target patient's rehabilitation status in the current rehabilitation cycle, and performs information fusion periodic recovery evaluation analysis through information feedback and information fusion to understand whether the recovery of the target patient's current rehabilitation cycle has achieved the expected effect, and rationally adjusts and manages subsequent rehabilitation training decisions based on the information feedback to speed up the recovery rate of the target patient's rehabilitation joints, and performs recovery score supervision processing from the perspective of training appearance performance, and analyzes in combination with rehabilitation training information, which helps to improve the accuracy of the analysis results, and at the same time understands the recovery status of the target patient's rehabilitation joints. On the one hand, it helps to reasonably adjust the rehabilitation training program according to the information feedback, and on the other hand, it helps to provide data support for subsequent recovery management, and by analyzing the rehabilitation training information, it is possible to understand the impact of rehabilitation training on the rehabilitation of the rehabilitation joints, so that the target patient's rehabilitation training can be adjusted according to the information feedback to achieve the expected rehabilitation training effect.

[0065] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0066] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. An artificial intelligence-based orthopedic joint replacement postoperative rehabilitation training monitoring system, characterized in that: It includes a rehabilitation supervision platform, an information collection unit, a recovery assessment unit, a training supervision unit, a rehabilitation monitoring unit, a periodic recovery assessment unit, and a management response unit; The information collection unit is used to collect the training appearance data and rehabilitation training information of the target patient, and send the training appearance data and rehabilitation training information to the recovery evaluation unit and the training supervision unit respectively; After receiving the rehabilitation training information, the training supervision unit immediately performs rehabilitation training effectiveness supervision analysis on the rehabilitation training information, compares and analyzes the obtained training evaluation index R, and obtains the recovery impact index HZ; After receiving the training appearance data, the restoration evaluation unit immediately performs restoration score supervision processing on the training appearance data, compares and analyzes the obtained restoration score index K, and obtains a standard signal and a deviation signal; The rehabilitation monitoring unit is used to respond to the supervision instruction, collect the rehabilitation intrinsic data of the rehabilitation joint of the target patient, and perform joint cycle rehabilitation feedback analysis on the rehabilitation intrinsic data to obtain the actual rehabilitation coefficient H; The periodic recovery evaluation unit is used to perform information fusion periodic recovery evaluation analysis on the recovery score index K, compare and analyze the obtained periodic rehabilitation score coefficient, and send the obtained expected signal and adjustment signal to the management response unit; The rehabilitation training effectiveness supervision and analysis process of the training supervision unit is as follows: The current rehabilitation cycle of the target patient is collected, and at the same time, the total number of rehabilitation training sessions in the current rehabilitation cycle of the target patient is obtained, the duration between the start time and the end time of each rehabilitation training session of the target patient is collected, and the duration between the start time and the end time of the rehabilitation training is set as the time threshold, and the rehabilitation training information of the rehabilitation joints of the target patient within the time threshold is obtained, and the rehabilitation training information includes the effective rehabilitation training duration and the actual interval duration, and then the difference between the effective rehabilitation training duration and the standard training duration is set as the training time deviation value, the difference between the actual interval duration and the standard interval time is obtained, and the difference between the actual interval duration and the standard interval time is set as the training deviation value, and the number of training deviation values ​​greater than the preset training deviation value threshold is set as the deviation impact value; The effective rehabilitation training duration represents the value obtained by subtracting the rest duration during the rehabilitation training from the time threshold; The training time-effectiveness deviation value and the deviation impact value are labeled YK and PY respectively. Substitute the training time-effectiveness deviation value YK and the deviation impact value PY into the formula The training evaluation index is obtained, where f1 and f2 are respectively the preset weight factor coefficients of the training time deviation value and the deviation impact value, f3 is the preset fault tolerance factor coefficient, f1, f2 and f3 are all greater than zero, and R is the training evaluation index. The training evaluation index R is compared and analyzed with the preset training evaluation index range stored in the training supervision unit: If the training evaluation index R is greater than the maximum value in the preset training evaluation index range, it is determined to be a third-level recovery impact; If the training evaluation index R falls within the preset training evaluation index range, it is determined to be a secondary recovery impact; If the training evaluation index R is less than the minimum value in the preset training evaluation index range, it is determined to be a first-level recovery impact, and the first-level recovery impact, the second-level recovery impact, and the third-level recovery impact are set as the recovery impact index HZ, HZ=1, 2, 3; The recovery score supervision process of the recovery assessment unit is as follows: Acquire the training appearance data of the rehabilitation joint of the target patient within the time threshold, the training appearance data includes the rehabilitation joint surface feature image and the current joint range of motion, preprocess the rehabilitation joint surface feature image, the preprocessing includes scaling and cropping, obtain the bulge volume of the rehabilitation joint in the surface feature image, and then obtain the ratio between the bulge volume and the preset bulge volume, and set the ratio between the bulge volume and the preset bulge volume as the bulge disappearance value, and at the same time obtain the ratio between the current joint range of motion and the doctor's preset joint range of motion, and set the ratio between the current joint range of motion and the doctor's preset joint range of motion as the joint recovery value; The bulge disappearance value and the joint recovery value are labeled GT and GH respectively. Substitute the bulge disappearance value GT and the joint recovery value GH into the formula The recovery score index K is obtained, wherein a1, a2 and a3 are preset proportional factor coefficients of the bulge disappearance value, the joint recovery value and the recovery influence index HZ, respectively, and a1, a2 and a3 are all greater than zero. The recovery score index K is compared with the preset recovery score index threshold value stored in the recovery evaluation unit for analysis: If the recovery score index K is greater than or equal to the preset recovery score index threshold, a standard signal is generated; If the recovery score index K is less than the preset recovery score index threshold, a deviation signal is generated; The joint cycle rehabilitation feedback analysis process of the rehabilitation monitoring unit is as follows: Obtain the target patient's internal rehabilitation data during the current rehabilitation cycle. The internal rehabilitation data includes pain information and muscle strength recovery information. The pain information and muscle strength recovery information are labeled as TT and JH respectively. Substitute the pain information TT and muscle strength recovery information JH into the formula The actual recovery coefficient H is obtained, wherein α and β are preset weighting coefficients of pain information and muscle strength recovery information respectively, and η is a preset correction factor coefficient, and α, β and η are all greater than zero; The pain information indicates the amount of reduction in pain level of the current rehabilitation joint during the current rehabilitation cycle; the muscle strength recovery information indicates the amount of change in muscle strength level of the current rehabilitation joint before and after the current rehabilitation cycle; The information fusion periodic recovery evaluation analysis process of the periodic recovery evaluation unit is as follows: A rectangular coordinate system is established with the number of rehabilitation training as the X-axis and the recovery score index K as the Y-axis, and a recovery score index curve is drawn by plotting points. At the same time, a preset recovery score index curve is drawn in the coordinate system, and then the corresponding plotted points located above or above the preset recovery score index curve in the recovery score index curve are obtained, and the corresponding plotted points located above or above the preset recovery score index curve in the recovery score index curve are set as effective recovery points, and then the number of effective recovery points is obtained, and the number of effective recovery points is set as the effective recovery value F. It should be noted that the larger the value of the effective recovery value, the better the expected effect of the current rehabilitation training; The value obtained by multiplying the effective recovery value F and the actual recovery coefficient H after data normalization is set as the periodic rehabilitation score coefficient, and the periodic rehabilitation score coefficient is compared and analyzed with the preset periodic rehabilitation score coefficient threshold stored in the periodic recovery evaluation unit: If the ratio between the periodic rehabilitation score coefficient and the preset periodic rehabilitation score coefficient threshold is less than 1, an adjustment signal is generated; If the ratio between the periodic rehabilitation score coefficient and the preset periodic rehabilitation score coefficient threshold is greater than or equal to 1, an expected signal is generated.

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