Orthopedic postoperative rehabilitation monitoring system based on intelligent analysis

Abstract

The invention discloses an orthopedic postoperative rehabilitation monitoring system based on intelligent analysis, and relates to the technical field of rehabilitation monitoring, and the orthopedic postoperative rehabilitation monitoring system comprises a data acquisition module, a data analysis module, a rehabilitation training module, a rehabilitation monitoring module, an early warning terminal and a database. Analyzing the probability of each complication caused by each training action from the information, training action combination and complication detection report of each historical patient in the database, setting the training action combination for the patient, and acquiring each motion parameter and each feedback of the patient in each rehabilitation training stage at the same time; the training effect of the patient in each rehabilitation training stage and the credibility fed back by the patient are analyzed, and whether the patient has complications in each rehabilitation training stage is judged, so that a doctor can adjust the rehabilitation training of the patient in time, and the effectiveness of training actions and the probability of complications are guaranteed; and the timeliness of training result analysis and the accuracy of complication monitoring are ensured.

Description

Technical Field

[0001] This invention relates to the field of rehabilitation monitoring technology, and specifically to an orthopedic postoperative rehabilitation monitoring system based on intelligent analysis. Background Technology

[0002] Postoperative rehabilitation after orthopedic surgery is a crucial step for patients to restore limb function and return to normal life. During rehabilitation training, patients are prone to various complications due to improper movements. Therefore, it is necessary to strictly control the standardization of training movements during postoperative rehabilitation training to avoid secondary injuries and complications.

[0003] Traditional orthopedic postoperative rehabilitation monitoring systems set up rehabilitation exercises based on the patient's age, type of surgery, and surgical location. After the rehabilitation training is completed, a follow-up examination is conducted to analyze the patient's training effect. At the same time, the system analyzes whether the patient has any complications based on the patient's feedback during the training process. Obviously, this type of orthopedic postoperative rehabilitation monitoring system has the following shortcomings: 1. Traditional orthopedic postoperative rehabilitation monitoring systems set up rehabilitation exercises based on the patient's age, type of surgery, and surgical location, but ignore the impact of the training exercises on complications. Therefore, they cannot guarantee the effectiveness of the training exercises, nor can they guarantee the probability of complications.

[0004] Traditional orthopedic postoperative rehabilitation monitoring systems require patients to undergo follow-up examinations after a period of time following the completion of rehabilitation training. The effectiveness of the training is analyzed based on the results of these follow-up examinations. However, there is a time lag between the follow-up examinations and the rehabilitation training period, which cannot guarantee the timeliness of the analysis of training results. Consequently, it cannot ensure that doctors can adjust the patient's rehabilitation training in a timely manner.

[0005] Traditional orthopedic postoperative rehabilitation monitoring systems analyze whether patients have complications based on their feedback during training. However, this process lacks analysis of the reliability of patient feedback, which may lead to inaccurate patient reports and thus cannot guarantee the accuracy of complication monitoring. Summary of the Invention

[0006] In view of the above-mentioned technical shortcomings, the purpose of this invention is to provide an orthopedic postoperative rehabilitation monitoring system based on intelligent analysis.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an orthopedic postoperative rehabilitation monitoring system based on intelligent analysis, including the following modules: data acquisition module, data analysis module, rehabilitation training module, rehabilitation monitoring module, early warning terminal and database.

[0008] The data acquisition module is used to retrieve training movement combinations and complication detection reports of each patient in history from the database, as well as the age, surgical type, surgical location, and diseases of each patient in history.

[0009] The data analysis module is used to obtain the patient's age, type of surgery, surgical location, and various diseases. It also selects historical patients with similar conditions from the database and refers to them as similar patients. Based on the training action combinations and complication detection reports of the similar patients, it analyzes the probability of each training action combination causing each complication.

[0010] The rehabilitation training module is used to obtain the probability of each combination of training movements causing each complication, and to set up the combination of training movements for this patient.

[0011] The rehabilitation monitoring module is used to acquire the patient's various movement parameters and feedback at each stage of rehabilitation training, analyze the patient's training effect at each stage of rehabilitation training, and analyze whether the patient has complications at each stage of rehabilitation training.

[0012] The early warning terminal is used to issue warnings when a patient's training results are poor or complications exist.

[0013] The database is used to store the training movement combinations, complication detection reports, age, surgical type, surgical location and various diseases of each patient in history, as well as the feedback results of each feedback when complications exist, the feedback results of each feedback of each rehabilitation training in the patient's history, each vital sign data, the numerical range of each exercise parameter in each rehabilitation training stage, and the characteristics of vital sign data corresponding to different feedback results of each feedback.

[0014] The beneficial effects of this invention are as follows: 1. This invention provides an orthopedic postoperative rehabilitation monitoring system based on intelligent analysis. It acquires the patient's information and analyzes the probability of each training movement causing each complication from the database of historical patient information, training movement combinations, and complication detection reports. It also sets training movement combinations for the patient and acquires the patient's movement parameters and feedback at each rehabilitation training stage. It analyzes the training effect and the credibility of the patient's feedback at each rehabilitation training stage and determines whether the patient has complications at each rehabilitation training stage. Doctors can adjust the patient's rehabilitation training in a timely manner, ensuring the effectiveness of the training movements and the probability of complications, as well as the timeliness of training result analysis and the accuracy of complication monitoring.

[0015] 2. This invention retrieves the patient's age, surgical type, surgical location, and various diseases from the database and compares them with the age, surgical type, surgical location, and various diseases of historical patients in the database. If the surgical type, surgical location, and various diseases of a historical patient are the same as those of the current patient, it means that the historical patient's condition is similar to that of the current patient, and this historical patient is called a similar patient. This method is used to obtain various similar patients, retrieve the training action combinations and complication detection reports of each similar patient from the database, and analyze the probability of each training action combination causing various complications based on the training action combinations and complication detection reports of each similar patient. Training action combinations are then set for the current patient to ensure the effectiveness of the training actions and the probability of complications.

[0016] 3. In each stage of rehabilitation training, this invention obtains the values ​​of various movement parameters of the patient during the training process, and retrieves the value range of each movement parameter in the rehabilitation training stage from the database. By comparing the values ​​of each movement parameter of the patient during the training process with the value range of each movement parameter in the rehabilitation training stage, the training effect of the patient in each rehabilitation training stage can be analyzed. Doctors can adjust the patient's rehabilitation training in a timely manner, ensuring the timeliness of training result analysis.

[0017] 4. This invention obtains patient feedback during each rehabilitation training stage and retrieves feedback results from the database when complications exist. Simultaneously, it retrieves feedback results and vital sign data from the database for each of the patient's historical rehabilitation training sessions, analyzes the reliability of the patient feedback, and determines whether complications exist at each rehabilitation training stage based on the reliability of the patient feedback and the patient's feedback during each rehabilitation training stage, thus ensuring the accuracy of complication monitoring. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 As shown, the present invention provides an orthopedic postoperative rehabilitation monitoring system based on intelligent analysis, including: a data acquisition module, a data analysis module, a rehabilitation training module, a rehabilitation monitoring module, an early warning terminal, and a database.

[0022] The data acquisition module is connected to the data analysis module, the data analysis module is connected to the rehabilitation training module, the rehabilitation training module is connected to the rehabilitation monitoring module, the rehabilitation monitoring module is connected to the early warning terminal, and the database is connected to the data acquisition module, the data analysis module, and the rehabilitation monitoring module.

[0023] The data acquisition module is used to retrieve training movement combinations and complication detection reports of each patient in history from the database, as well as the age, surgical type, surgical location, and diseases of each patient in history.

[0024] It should be noted that the types of surgery include hip replacement, knee replacement, shoulder replacement, and endoscopic discectomy.

[0025] It should also be noted that when the type of surgery is the same, the surgical location may not be the same. For example, the surgical location for hip replacement surgery includes the posterolateral position of the hip joint and the anterior lateral position of the hip joint. The surgical location is determined according to the surgical approach and the exposure position.

[0026] Among them, each disease refers to other diseases that the patient already has, including diabetes, osteoporosis, and gouty arthritis.

[0027] The data analysis module is used to obtain the patient's age, type of surgery, surgical location, and various diseases. It also selects historical patients with similar conditions from the database and refers to them as similar patients. Based on the training action combinations and complication detection reports of the similar patients, it analyzes the probability of each training action combination causing each complication.

[0028] It should be noted that the complication report includes all complications present in the patient, such as bleeding, hematoma, nerve damage, and prosthesis loosening.

[0029] In a specific embodiment, the data analysis module performs the following process: It retrieves the patient's age, surgical type, surgical location, and diseases from the database and compares these data with those of historical patients in the database. If a historical patient's surgical type, surgical location, and diseases are identical to those of the current patient, and the patient's age is within the same age range as the current patient, then the historical patient's condition is similar to the current patient's, and this historical patient is referred to as a similar patient. This method is used to obtain all similar patients.

[0030] It should be noted that the age groups are divided into 28 days to 1 year old, 1 year to 3 years old, 3 years to 6 years old, 6 years to 10 years old, 10 years to 18 years old, 18 years to 44 years old, 45 years to 59 years old, and 60 years and above, which are based on the doctor's classification of growth and development.

[0031] The training exercise combinations and complication test reports of similar patients were obtained from the database, and the probability of each training exercise combination causing each complication was analyzed based on these data.

[0032] It should be noted that the training exercise combination consists of several different training movements, including slowly flexing and then stepping on the foot, straightening the leg, tightening the front thigh muscles, slowly bending the knee, and raising the leg to the side.

[0033] The above-mentioned analysis of the probability of each training action combination causing each complication is carried out in the following specific process: Obtain each complication from the complication detection reports of each similar patient; integrate the complications of each similar patient to obtain each complication; compare each complication with the complications of each similar patient; if a certain complication is among the complications of a certain similar patient, then the similar patient is called the marked patient of that complication; obtain each marked patient of each complication in this way; compare the training action combinations of each marked patient of each complication; group the marked patients with the same training action combination into a patient group; obtain each patient group of each complication in this way.

[0034] It should be noted that, for example, considering three patients, the complications of the first patient include bleeding, hematoma, and nerve damage; the complications of the second patient include nerve damage and prosthesis loosening; and the complications of the third patient also include bleeding, nerve damage, and prosthesis loosening. Therefore, the complications from all three patients are combined to arrive at the total complications: bleeding, hematoma, nerve damage, and prosthesis loosening. This example is merely illustrative; in reality, there are not only three similar patients, and the complications of similar patients are not limited to bleeding, hematoma, nerve damage, and prosthesis loosening. This example is not the only valid one.

[0035] In each patient group of each complication, the total number of labeled patients in each complication patient group is counted. The training action combinations for each complication patient group are obtained and compared with the training action combinations of similar patients. Similar patients whose training action combinations are the same as those of a certain complication patient group are called secondary labeled patients of that complication patient group. This method is used to obtain secondary labeled patients for each complication patient group, and the total number of secondary labeled patients in each complication patient group is counted. Based on the total number of secondary labeled patients and the total number of labeled patients in each complication patient group, the percentage of labeled patients in each complication patient group is calculated. The percentage of labeled patients in each complication patient group is called the probability of each complication caused by the corresponding training action combination in each patient group. This method is used to analyze the probability of each training action combination causing each complication.

[0036] The rehabilitation training module is used to obtain the probability of each combination of training movements causing each complication, and to set up the combination of training movements for this patient.

[0037] In a specific embodiment, the process of setting training action combinations for the patient is as follows: The probability of each training action combination causing each complication is obtained and compared. The probability with the highest probability of causing a complication is selected and referred to as the maximum probability of each training action combination. Simultaneously, the probability of each training action combination causing each complication is compared with a preset first probability threshold. Probabilities greater than the preset first probability threshold are referred to as labeled probabilities. Labeled probabilities of each training action combination are obtained using this method. The total number of labeled probabilities of each training action combination is counted, and the percentage of labeled probabilities is calculated and referred to as the labeled probability percentage of each training action combination.

[0038] It should be noted that the preset first probability threshold is a critical value used to judge whether the probability of each complication caused by the training action combination is too high, and it is set by medical staff.

[0039] The maximum probability of each training action combination is compared with a preset second probability threshold. Each training action combination with a maximum probability less than the preset second probability threshold is called a labeled training action combination. The percentage of labeled probability of each labeled training action combination is compared, and the labeled training action combination with the smallest percentage of labeled probability is taken as the training action combination for this patient.

[0040] It should be noted that the preset second probability threshold is a critical value used to judge whether the maximum probability of each training action combination is too high, and it is set by medical staff.

[0041] The rehabilitation monitoring module is used to acquire the patient's various movement parameters and feedback at each stage of rehabilitation training, analyze the patient's training effect at each stage of rehabilitation training, and analyze whether the patient has complications at each stage of rehabilitation training.

[0042] In a specific embodiment, the rehabilitation monitoring module operates as follows: when the patient performs rehabilitation training according to the set training action combination, the module acquires the patient's various movement parameters during each rehabilitation training stage and analyzes the patient's training effect in each rehabilitation training stage. When the patient's training effect is poor in a certain rehabilitation training stage, an early warning is issued, and the patient's various movement parameters during the training process in that rehabilitation training stage are fed back to the doctor. The doctor adjusts the patient's rehabilitation training based on the feedback information.

[0043] It should be noted that the exercise parameters include exercise time, joint range of motion, and exercise intensity. The exercise time and intensity are obtained through a fitness tracker, and the joint range of motion is obtained through an electronic goniometer.

[0044] Simultaneously, feedback from patients during each rehabilitation training stage is collected, and feedback results when complications exist are retrieved from the database. Additionally, feedback results and vital sign data from each of the patient's historical rehabilitation training sessions are obtained from the database to analyze the reliability of patient feedback. Based on the reliability of patient feedback and the patient's feedback during each rehabilitation training stage, it is determined whether complications exist at each stage. When complications occur at a particular rehabilitation training stage, an early warning is issued, and the doctor is notified.

[0045] It should be noted that the feedback includes pain feedback, fatigue feedback, physical strength feedback, etc. The patient's feedback is obtained through voice recognition technology. Different feedbacks will have different results. For example, the feedback result of fatigue feedback is muscle soreness and muscle stiffness.

[0046] It should also be noted that patients with only one complication are retrieved from the database, and patients with the same complication are grouped into a complication group. Within each complication group, the feedback results of each patient are retrieved and fused to obtain the feedback results of each patient when the complication is present. This method is used to obtain the feedback results of each patient when each complication is present.

[0047] The vital signs data, including heart rate, blood pressure, body temperature, blood oxygen saturation, and pulse, are obtained through medical instruments.

[0048] The above-mentioned analysis of the patient's training effect in each rehabilitation training stage is specifically carried out as follows: The values ​​of each exercise parameter during the patient's training in each rehabilitation training stage are obtained, and the value range of each exercise parameter in each rehabilitation training stage is obtained from the database. The values ​​of each exercise parameter during the patient's training in each rehabilitation training stage are compared with the value range of each exercise parameter in each rehabilitation training stage. If, in a certain rehabilitation training stage, there are exercise parameters whose values ​​are outside the data range of that rehabilitation training stage, it indicates that the patient's training effect in that rehabilitation training stage is poor. If the values ​​of all exercise parameters are within the value range of that rehabilitation training stage, it indicates that the patient's training effect in that rehabilitation training stage is good. This method is used to analyze the patient's training effect in each rehabilitation training stage.

[0049] It should be noted that, in each rehabilitation training stage, the values ​​of each exercise parameter of each similar patient are obtained from the database for that rehabilitation training stage. The exercise parameter values ​​of each similar patient are compared, and patients with the same exercise parameter value are grouped into a similar patient group. This method is used to obtain each similar patient group, and the total number of similar patient groups is counted. The total number of similar patient groups is compared with a preset total threshold. Similar patient groups whose total number exceeds the preset total threshold are called labeled similar patient groups. The exercise parameter values ​​of each labeled similar patient group are clustered to obtain the numerical range of that exercise parameter in that rehabilitation training stage. This method is used to obtain the numerical range of each exercise parameter in that rehabilitation training stage.

[0050] It should also be noted that the preset total threshold is a cutoff value used to determine whether the total number of similar patient groups in each similar patient group is excessive, and it is set by medical staff.

[0051] The above-mentioned process for analyzing the credibility of patient feedback is as follows: During each historical rehabilitation training session, the feedback results of each patient's feedback and the vital sign data of each patient at the time of each feedback are obtained, and the authenticity of each feedback result is analyzed. Each feedback with an authentic result is called an authentic feedback, and the total number of authentic feedbacks of patients is counted. At the same time, the percentage of authentic feedbacks of patients is calculated, and this percentage of authentic feedbacks is called the credibility of the patient's feedback in the historical rehabilitation training session. The credibility of the patient's feedback in each historical rehabilitation training session is obtained by this method.

[0052] Calculate the average credibility of the patient's feedback in each historical rehabilitation training session and compare it with a preset average threshold. If the average credibility of the patient's feedback in each historical rehabilitation training session is less than the preset average threshold, it means that the credibility of the patient's feedback is low. If the average credibility of the patient's feedback in each historical rehabilitation training session is greater than the preset average threshold, it means that the credibility of the patient's feedback is high.

[0053] It should be noted that the preset average threshold is a critical value used to analyze whether the credibility of patient feedback is high. The credibility of each patient's feedback in history and the degree of influence of each patient's feedback on the treatment effect are obtained from the database. Patients whose feedback results have a low degree of influence on the treatment effect are called credible patients. The credibility of the feedback of each credible patient is compared, and the minimum credibility is used as the preset average threshold.

[0054] The specific process for analyzing the authenticity of each feedback result is as follows: Based on the vital sign data of the patient during each feedback, the characteristics of the patient's vital sign data at each feedback are obtained, the feedback results of each feedback are obtained, and the characteristics of the vital sign data corresponding to different feedback results are obtained from the database. In each feedback, the standard characteristics of the vital sign data of that feedback are obtained from the database based on the feedback result. The standard characteristics of the vital sign data of that feedback are compared with the characteristics of the vital sign data of the patient during that feedback. If the standard characteristics of the vital sign data of that feedback are the same as the characteristics of the vital sign data of the patient during that feedback, it means that the feedback result is authentic. If the standard characteristics of the vital sign data of that feedback are different from the characteristics of the vital sign data of the patient during that feedback, it means that the feedback result is not authentic. This method is used to analyze whether the feedback results of each feedback are authentic.

[0055] It should be noted that the characteristics of the patient's vital signs data during various feedback processes are obtained through convolutional neural networks.

[0056] It should be noted that the feedback result of this feedback is compared with the feedback results of other feedbacks. If the feedback result of this feedback is the same as a certain feedback result of this feedback, the characteristics of the vital signs data corresponding to the same feedback result are retrieved from the database and used as the standard characteristics of the vital signs data of this feedback.

[0057] In the above, when the credibility of patient feedback is high, the process of determining whether the patient has complications in each rehabilitation training stage is as follows: Obtain the feedback results when each complication exists, and call them the standard feedback results for each complication. Simultaneously, obtain the feedback results of each patient's feedback in each rehabilitation training stage and compare them with the standard feedback results for each complication. Obtain the return value of each complication in each rehabilitation training stage. When a complication with a return value of 1 exists in a certain rehabilitation training stage, it means the patient has complications in that stage. When the return values ​​of all complications in a certain rehabilitation training stage are 0, it means the patient does not have complications in that stage. When the credibility of patient feedback is high, this method is used to determine whether the patient has complications in each rehabilitation training stage.

[0058] It should be noted that for each complication, the feedback result at each feedback point is compared with the standard feedback result for that complication. If there is a feedback result that is the same as the standard feedback result, it means that the patient may have that complication, and the return value for that complication is 1. If the feedback result of each feedback point is different from the standard feedback result of each feedback point, it means that the patient does not have that complication, and the return value for that complication is 1. The return value for each complication is obtained in this way.

[0059] In the above, when the credibility of patient feedback is low, the specific process for determining whether the patient has complications in each rehabilitation training stage is as follows: Obtain vital sign data of the patient at each stage of rehabilitation and when providing feedback, and obtain the characteristics of the vital sign data at each stage of rehabilitation and when providing feedback. Simultaneously, based on the characteristics of the vital sign data at each stage of rehabilitation and when providing feedback, obtain the feedback results of the patient at each stage of rehabilitation. Then, using the method of determining whether the patient has complications in each stage of rehabilitation and when the credibility of patient feedback is high, the method is used to determine whether the patient has complications in each stage of rehabilitation and when the credibility of patient feedback is low.

[0060] It should be noted that in each feedback, the characteristics of the patient's vital signs data when the feedback is given are compared with the characteristics of the vital signs data corresponding to different feedback results. If the characteristics of the patient's vital signs data when the feedback is given are the same as the characteristics of the vital signs data corresponding to a certain feedback result, then that feedback result is taken as the feedback result of that feedback. The feedback results of each feedback are obtained in this way.

[0061] The early warning terminal is used to issue warnings when a patient's training results are poor or complications exist.

[0062] The database is used to store the training movement combinations, complication detection reports, age, surgical type, surgical location and various diseases of each patient in history, as well as the feedback results of each feedback when complications exist, the feedback results of each feedback of each rehabilitation training in the patient's history, each vital sign data, the numerical range of each exercise parameter in each rehabilitation training stage, and the characteristics of vital sign data corresponding to different feedback results of each feedback.

[0063] This invention acquires patient information and analyzes the probability of each training movement triggering each complication from historical patient information, training movement combinations, and complication detection reports in the database. It then sets training movement combinations for the patient and acquires the patient's movement parameters and feedback at each rehabilitation training stage. The invention analyzes the training effect and the reliability of patient feedback at each stage and determines whether complications exist. Doctors can adjust the patient's rehabilitation training in a timely manner, ensuring the effectiveness of the training movements and the probability of complications, as well as the timeliness of training result analysis and the accuracy of complication monitoring.

[0064] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A postoperative rehabilitation monitoring system for orthopedic surgery based on intelligent analysis, characterized in that, Includes the following modules: The data acquisition module is used to retrieve training movement combinations and complication detection reports for each patient in history from the database, as well as the patient's age, surgical type, surgical location, and various diseases. The data analysis module is used to obtain the patient's age, type of surgery, surgical location, and various diseases. It also selects historical patients with similar conditions from the database and refers to them as similar patients. Based on the training action combinations and complication detection reports of the similar patients, the module analyzes the probability of each training action combination causing each complication. The rehabilitation training module is used to obtain the probability of various complications caused by each combination of training movements, and to set up the combination of training movements for this patient. The rehabilitation monitoring module is used to acquire the patient's various movement parameters and feedback at each stage of rehabilitation training, analyze the patient's training effect at each stage of rehabilitation training, and analyze whether the patient has complications at each stage of rehabilitation training. The early warning terminal is used to issue warnings when a patient's training results are poor or complications exist. The database is used to store the training movement combinations, complication detection reports, age, surgical type, surgical location and various diseases of each patient in history, as well as the feedback results of each feedback when complications exist, the feedback results of each feedback of each rehabilitation training session in the patient's history, each vital sign data, the numerical range of each exercise parameter in each rehabilitation training stage, and the characteristics of vital sign data corresponding to different feedback results of each feedback.

2. The orthopedic postoperative rehabilitation monitoring system based on intelligent analysis according to claim 1, characterized in that, The data analysis module operates as follows: The patient's age, type of surgery, surgical location, and diseases are obtained from the database and compared with the age, type of surgery, surgical location, and diseases of historical patients in the database. If the type of surgery, surgical location, and diseases of a historical patient are the same as those of the current patient, and the age of the historical patient is in the same age range as the current patient, then the historical patient's condition is similar to that of the current patient, and the historical patient is called a similar patient. Similar patients are obtained in this way. The training exercise combinations and complication test reports of similar patients were obtained from the database, and the probability of each training exercise combination causing each complication was analyzed based on these data.

3. The orthopedic postoperative rehabilitation monitoring system based on intelligent analysis according to claim 2, characterized in that, The analysis of the probability of various complications caused by different training movement combinations is carried out in the following specific process: The complications of each similar patient are obtained from the complication test reports of each similar patient. The complications of each similar patient are integrated to obtain each complication. Each complication is compared with the complications of each similar patient. If a certain complication is found in the complications of a certain similar patient, then that similar patient is called the marked patient for that complication. The marked patients for each complication are obtained in this way. The training action combinations of the marked patients for each complication are compared. Patients with the same training action combination are divided into a patient group. The patient groups for each complication are obtained in this way. In each patient group of each complication, the total number of labeled patients in each complication patient group is counted. The training action combinations for each complication patient group are obtained and compared with the training action combinations of similar patients. Similar patients whose training action combinations are the same as those of a certain complication patient group are called secondary labeled patients of that complication patient group. This method is used to obtain secondary labeled patients for each complication patient group, and the total number of secondary labeled patients in each complication patient group is counted. Based on the total number of secondary labeled patients and the total number of labeled patients in each complication patient group, the percentage of labeled patients in each complication patient group is calculated. The percentage of labeled patients in each complication patient group is called the probability of each complication caused by the corresponding training action combination in each patient group. This method is used to analyze the probability of each training action combination causing each complication.

4. The orthopedic postoperative rehabilitation monitoring system based on intelligent analysis according to claim 1, characterized in that, The training exercise combination was designed for this patient, and the specific process is as follows: The probability of each training action combination causing each complication is obtained and compared. The probability with the highest probability of each training action combination causing a complication is selected and called the maximum probability of each training action combination. At the same time, the probability of each training action combination causing each complication is compared with a preset first probability threshold. The probabilities that are greater than the preset first probability threshold are called the labeled probabilities. The labeled probabilities of each training action combination are obtained in this way. The total number of labeled probabilities of each training action combination is counted and the percentage of labeled probabilities is calculated. This percentage is called the labeled probability percentage of each training action combination. The maximum probability of each training action combination is compared with a preset second probability threshold. Each training action combination whose maximum probability is less than the preset second probability threshold is called a labeled training action combination. The percentage of labeled probability of each labeled training action combination is compared, and the labeled training action combination with the smallest percentage of labeled probability is taken as the training action combination for this patient.

5. The orthopedic postoperative rehabilitation monitoring system based on intelligent analysis according to claim 1, characterized in that, The rehabilitation monitoring module operates as follows: When patients perform rehabilitation training according to the set training exercise combination, the system acquires various movement parameters of the patients during each rehabilitation training stage and analyzes the training effect of the patients in each rehabilitation training stage. When the training effect of the patients is poor in a certain rehabilitation training stage, an early warning is issued and the various movement parameters of the patients during the training in that rehabilitation training stage are fed back to the doctor. The doctor adjusts the patient's rehabilitation training based on the feedback information. Simultaneously, feedback from patients during each rehabilitation training stage is collected, and feedback results when complications exist are retrieved from the database. Additionally, feedback results and vital sign data from each of the patient's historical rehabilitation training sessions are obtained from the database to analyze the reliability of patient feedback. Based on the reliability of patient feedback and the patient's feedback during each rehabilitation training stage, it is determined whether complications exist at each stage. When complications occur at a particular rehabilitation training stage, an early warning is issued, and the doctor is notified.

6. The orthopedic postoperative rehabilitation monitoring system based on intelligent analysis according to claim 5, characterized in that, The analysis of the patient's training effects at each stage of rehabilitation training is conducted as follows: The study obtains the values ​​of various exercise parameters during the patient's training at each rehabilitation training stage, and retrieves the value range of each exercise parameter in each rehabilitation training stage from the database. The values ​​of each exercise parameter during the patient's training at each rehabilitation training stage are compared with the value range of each exercise parameter in each rehabilitation training stage. If there are any exercise parameters whose values ​​are outside the data range of a certain rehabilitation training stage, it means that the patient's training effect in that rehabilitation training stage is poor. If the values ​​of all exercise parameters are within the value range of that rehabilitation training stage, it means that the patient's training effect in that rehabilitation training stage is good. This method is used to analyze the patient's training effect at each rehabilitation training stage.

7. The orthopedic postoperative rehabilitation monitoring system based on intelligent analysis according to claim 5, characterized in that, The process for analyzing the credibility of patient feedback is as follows: During each historical rehabilitation training session, the feedback results of each patient's feedback and the vital sign data of each patient at the time of each feedback were obtained, and the authenticity of each feedback result was analyzed. Each feedback with an authentic result was called an authentic feedback, and the total number of authentic feedbacks of patients was counted. At the same time, the percentage of authentic feedbacks of patients was calculated, and the percentage of authentic feedbacks of patients was called the credibility of the patient's feedback in the historical rehabilitation training session. This method was used to obtain the credibility of the patient's feedback in each historical rehabilitation training session. Calculate the average credibility of the patient's feedback in each historical rehabilitation training session and compare it with a preset average threshold. If the average credibility of the patient's feedback in each historical rehabilitation training session is less than the preset average threshold, it means that the credibility of the patient's feedback is low. If the average credibility of the patient's feedback in each historical rehabilitation training session is greater than the preset average threshold, it means that the credibility of the patient's feedback is high.

8. The orthopedic postoperative rehabilitation monitoring system based on intelligent analysis according to claim 7, characterized in that, The process of analyzing whether the feedback results of each feedback are true is as follows: Based on the vital sign data of the patient at each feedback point, the characteristics of the patient's vital sign data at each feedback point are obtained, and the feedback results of each feedback point are obtained from the database. The characteristics of the vital sign data corresponding to different feedback results of each feedback point are then obtained from the database. In each feedback point, the standard characteristics of the vital sign data for that feedback point are obtained from the database based on the feedback result. The standard characteristics of the vital sign data for that feedback point are compared with the characteristics of the vital sign data of the patient at the time of that feedback point. If the standard characteristics of the vital sign data for that feedback point are the same as the characteristics of the vital sign data of the patient at the time of that feedback point, it means that the feedback result is true. If the standard characteristics of the vital sign data for that feedback point are different from the characteristics of the vital sign data of the patient at the time of that feedback point, it means that the feedback result is not true. This method is used to analyze whether the feedback results of each feedback point are true.

9. The orthopedic postoperative rehabilitation monitoring system based on intelligent analysis according to claim 5, characterized in that, When the patient's feedback is highly reliable, the process for determining whether the patient has complications at each stage of rehabilitation training is as follows: The feedback results for each complication are obtained and referred to as the standard feedback results for each complication. Simultaneously, the feedback results for each complication at each rehabilitation training stage are obtained and compared with the standard feedback results for each complication. The return values ​​for each complication at each rehabilitation training stage are then obtained. If a complication with a return value of 1 exists at a certain rehabilitation training stage, it indicates that the patient has a complication at that stage. If all return values ​​for each complication at a certain rehabilitation training stage are 0, it indicates that the patient does not have a complication at that stage. When the reliability of the patient's feedback is high, this method is used to determine whether the patient has complications at each rehabilitation training stage.

10. The orthopedic postoperative rehabilitation monitoring system based on intelligent analysis according to claim 5, characterized in that, When the reliability of patient feedback is low, the following process is used to determine whether the patient has complications at each stage of rehabilitation training: This study acquires vital sign data of patients at each stage of rehabilitation and during each feedback session, and obtains the characteristics of these vital sign data. Based on these characteristics, the study obtains the feedback results of each feedback session at each stage. When the credibility of the patient's feedback is high, the study uses this method to determine whether the patient has complications at each stage of rehabilitation training. When the credibility of the patient's feedback is low, the study uses the same method to determine whether the patient has complications at each stage of rehabilitation training.

Images