Pelvic floor muscle rehabilitation training management system and method based on data analysis

Abstract

The invention relates to a pelvic floor muscle rehabilitation training management system and method based on data analysis, pelvic floor muscle rehabilitation training data obtained by performing pelvic floor muscle rehabilitation training on a patient to be subjected to contrast management by using an original training task are obtained, contrast analysis is performed based on the pelvic floor muscle rehabilitation training data, and a pelvic floor muscle rehabilitation training result is further obtained based on a contrast analysis result. According to the method, the original training task of the patient to be contrasted and managed is optimized to obtain the optimized training task, so that a new training task can be formulated for the patient to be contrasted and managed based on the optimized training task and the contrasting analysis result. According to the technical scheme, the patient is managed in a control group mode, the training task of the patient is optimized through control type data analysis, effective and flexible management of the pelvic floor muscle rehabilitation training task of the patient can be achieved, and therefore the pelvic floor muscle rehabilitation training experience of the patient is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a pelvic floor muscle rehabilitation training management system and method based on data analysis. Background Art

[0002] Pelvic floor muscle rehabilitation training is an important rehabilitation method, primarily used to prevent and treat pelvic floor dysfunction disorders such as urinary incontinence and pelvic organ prolapse. Pelvic floor muscle rehabilitation training management technology is a management method for pelvic floor muscle rehabilitation training. Through effective management of pelvic floor muscle rehabilitation training, it can help improve the effectiveness of pelvic floor muscle rehabilitation training and thus enhance the patient's rehabilitation training experience.

[0003] Currently, pelvic floor muscle rehabilitation training can be managed through the configuration of corresponding management applications. For example, patent CN115602285A designs a pelvic floor muscle rehabilitation training information management system that can effectively manage various relevant data related to pelvic floor muscle rehabilitation training. However, this management system mainly designs a variety of flexible management functions and lacks effective data analysis methods. As a result, it cannot achieve effective and flexible management of patients' training tasks. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a pelvic floor muscle rehabilitation training management system and method based on data analysis. The pelvic floor muscle rehabilitation training management system and method based on data analysis can realize the effective and flexible management of patients' pelvic floor muscle rehabilitation training tasks, thereby improving patients' pelvic floor muscle rehabilitation training experience.

[0005] In order to achieve the above-mentioned objectives, in a first aspect, the present disclosure provides a pelvic floor muscle rehabilitation training management method based on data analysis, including: obtaining pelvic floor muscle rehabilitation training data obtained by patients to be controlled and managed using original training tasks for pelvic floor muscle rehabilitation training, wherein the patients to be controlled and managed include at least two patients who meet preset control management conditions; performing control analysis on the pelvic floor muscle rehabilitation training data corresponding to the patients to be controlled and managed to obtain control analysis results; optimizing the original training tasks of the patients to be controlled and managed based on the control analysis results to obtain optimized training tasks; determining new training tasks based on the control analysis results and the optimized training tasks, and feeding back the new training tasks to the patients to be controlled and managed, so that the patients to be controlled and managed use the new training tasks for pelvic floor muscle rehabilitation training.

[0006] Optionally, the pelvic floor muscle rehabilitation training management method further includes: obtaining patient information and original training tasks corresponding to multiple patients, wherein the original training tasks are training tasks formulated by doctors for patients; determining at least two patients who meet preset control management conditions from the multiple patients based on the patient information and original training tasks corresponding to the multiple patients, wherein the preset control management conditions include conditions related to the training tasks and conditions related to the patient information; determining the at least two patients as the patients to be controlled; obtaining pelvic floor muscle rehabilitation training data obtained by the patients to be controlled using the original training tasks for pelvic floor muscle rehabilitation training, including: feeding back the original training tasks corresponding to the patients to be controlled to the patients to be controlled, so that the patients to be controlled use the original training tasks for pelvic floor rehabilitation training; receiving pelvic floor muscle rehabilitation training data uploaded by the patients to be controlled or pelvic floor muscle rehabilitation trainers.

[0007] Optionally, the pelvic floor muscle rehabilitation training data includes first pelvic floor muscle contraction data collected during the process of the patient to be controlled and managed performing pelvic floor muscle rehabilitation training using the original training task, and second pelvic floor muscle contraction data collected after the patient to be controlled and managed completes the pelvic floor muscle rehabilitation training using the original training task. The comparative analysis of the pelvic floor muscle rehabilitation training data corresponding to the patient to be controlled and managed to obtain a comparative analysis result includes: determining first control pelvic floor muscle contraction data from the first pelvic floor muscle contraction data corresponding to the at least two patients respectively; determining second control pelvic floor muscle contraction data from the second pelvic floor muscle contraction data corresponding to the at least two patients respectively; determining third control pelvic floor muscle contraction data corresponding to the at least two patients respectively from the first pelvic floor muscle contraction data and the second pelvic floor muscle contraction data corresponding to the at least two patients respectively; for any one of the at least two patients, determining a predicted training effect corresponding to the patient according to the first control pelvic floor muscle contraction data, the second control pelvic floor muscle contraction data and the third control pelvic floor muscle contraction data corresponding to the patient through a pre-trained prediction model; and determining the comparative analysis result according to the predicted training effects corresponding to the at least two patients respectively.

[0008] Optionally, the pelvic floor muscle rehabilitation training management method also includes: acquiring training data, the training data including multiple training samples, each training sample including: a first control sample, a second control sample, a third control sample and a sample label, wherein the first control sample is determined based on the first pelvic floor muscle contraction data sample, the second control sample is determined based on the second pelvic floor muscle contraction data sample, and the third control sample is determined based on the first pelvic floor muscle contraction data sample and the second pelvic floor muscle contraction data sample, the first pelvic floor muscle contraction data sample is pelvic floor muscle contraction data collected during the patient's pelvic floor muscle rehabilitation training using the training task, the second pelvic floor muscle contraction data sample is pelvic floor muscle contraction data collected after the patient completes the pelvic floor muscle rehabilitation training using the training task, and the sample label is the patient's actual training effect; based on the training data, the prediction model to be trained is trained to obtain a pre-trained prediction model.

[0009] Optionally, the control analysis result is used to characterize the predicted training effects corresponding to the at least two patients respectively, and the predicted training effects include: recovery probability, and the original training tasks of the patients to be controlled and managed are optimized according to the control analysis results to obtain optimized training tasks, including: when there is a recovery probability higher than a preset recovery probability among the recovery probabilities corresponding to the at least two patients respectively, determining the difference between the recovery probabilities corresponding to the at least two patients respectively; when the difference between the recovery probabilities corresponding to the at least two patients respectively is higher than the preset difference, updating the original training tasks corresponding to the patient with the lower recovery probability according to the original training tasks corresponding to the patient with the higher recovery probability to obtain optimized training tasks corresponding to the patient with the lower recovery probability; when the difference between the recovery probabilities corresponding to the at least two patients respectively is lower than the preset difference, determining the difference training tasks between the original training tasks corresponding to the at least two patients respectively, and updating the original training tasks corresponding to the at least two patients respectively according to the difference training tasks to obtain optimized training tasks corresponding to the at least two patients respectively.

[0010] Optionally, the control analysis results are used to characterize the predicted training effects corresponding to the at least two patients respectively, and the predicted training effects include: recovery probability, and based on the control analysis results, the original training tasks of the patients to be controlled and managed are optimized to obtain optimized training tasks, including: when there is no recovery probability higher than a preset recovery probability among the recovery probabilities corresponding to the at least two patients respectively, determining the same training tasks between the original training tasks corresponding to the at least two patients respectively, and based on the same training tasks, updating the original training tasks corresponding to the at least two patients respectively to obtain optimized training tasks corresponding to the at least two patients respectively.

[0011] Optionally, the original training task shown is a training task formulated by the doctor for the patient to be managed in comparison, and determining the new training task based on the comparison analysis results and the optimized training task includes: feeding back the optimized training task and the comparison analysis results to the doctor; receiving the training task fed back by the doctor; determining the new training task based on the training task fed back by the doctor and the optimized training task, wherein the new training task at least includes the optimized training task.

[0012] Optionally, the pelvic floor muscle rehabilitation training management method also includes: in response to detecting the presence of a recovered patient among the patients to be controlled, updating the preset control management conditions according to the patient information and original training tasks corresponding to the recovered patient to obtain updated control management conditions; obtaining patient information and original training tasks corresponding to at least one new patient, the original training task being a training task formulated by a doctor for the new patient; determining a target patient from the at least one new patient according to the patient information and original training tasks corresponding to the at least one new patient, the patient information and original training tasks corresponding to the unrecovered patients among the patients to be controlled, and the updated control management conditions; and updating the patients to be controlled based on the target patient.

[0013] Optionally, the preset control management conditions include conditions related to the type of training task and conditions related to the expected recovery time included in the patient information. The preset control management conditions are updated according to the patient information corresponding to the rehabilitation patient and the original training task, including: updating the conditions related to the expected recovery time included in the patient information corresponding to the rehabilitation patient in the preset control management conditions; updating the conditions related to the task type of the training task in the preset control management conditions according to the task type of the original training task corresponding to the rehabilitation patient.

[0014] In the second aspect, the present disclosure provides a pelvic floor muscle rehabilitation training management system based on data analysis, which is characterized by including: a data acquisition device for collecting pelvic floor muscle rehabilitation training data; and a data processing device for executing the pelvic floor muscle rehabilitation training management method based on data analysis as described in the first aspect of the present disclosure.

[0015] The technical solution disclosed herein obtains pelvic floor muscle rehabilitation training data obtained by patients to be managed in a controlled manner using original training tasks for pelvic floor muscle rehabilitation training, performs a controlled analysis based on the pelvic floor muscle rehabilitation training data, and further optimizes the original training tasks of the patients to be managed in a controlled manner based on the controlled analysis results to obtain optimized training tasks. Thus, based on the optimized training tasks and the controlled analysis results, new training tasks can be formulated for the patients to be managed in a controlled manner. This technical solution manages patients in a controlled manner, and optimizes the patients' training tasks through controlled data analysis, which can achieve effective and flexible management of the patients' pelvic floor muscle rehabilitation training tasks, thereby improving the patients' pelvic floor muscle rehabilitation training experience.

[0016] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0018] Figure 1 This is an example diagram showing an application scenario according to an exemplary embodiment.

[0019] Figure 2 The present invention is a flowchart of a pelvic floor muscle rehabilitation training management method based on data analysis according to an exemplary embodiment.

[0020] Figure 3 The figure is a schematic diagram showing a process of determining a control management patient according to an exemplary embodiment.

[0021] Figure 4 The present invention is a block diagram of a pelvic floor muscle rehabilitation training management device based on data analysis according to an exemplary embodiment.

[0022] Figure 5 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0023] The following describes the specific embodiments of the present disclosure in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not intended to limit the present disclosure.

[0024] Pelvic floor muscle rehabilitation training is an important rehabilitation method, primarily used to prevent and treat pelvic floor dysfunction disorders such as urinary incontinence and pelvic organ prolapse. Pelvic floor muscle rehabilitation training management technology is a management method for pelvic floor muscle rehabilitation training. Through effective management of pelvic floor muscle rehabilitation training, it can help improve the effectiveness of pelvic floor muscle rehabilitation training and thus enhance the patient's rehabilitation training experience.

[0025] Currently, pelvic floor muscle rehabilitation training can be managed through the configuration of corresponding management applications. For example, patent CN115602285A designs a pelvic floor muscle rehabilitation training information management system that can effectively manage various relevant data related to pelvic floor muscle rehabilitation training. However, this management system mainly designs a variety of flexible management functions and lacks effective data analysis methods. As a result, it cannot achieve effective and flexible management of patients' training tasks.

[0026] Based on this, the embodiment of the present disclosure provides a technical solution, which obtains pelvic floor muscle rehabilitation training data obtained by the patient to be controlled for management using the original training task for pelvic floor muscle rehabilitation training, performs a control analysis based on the pelvic floor muscle rehabilitation training data, and further optimizes the original training task of the patient to be controlled for management based on the control analysis results to obtain an optimized training task. Therefore, based on the optimized training task and the control analysis results, a new training task can be formulated for the patient to be controlled for management.

[0027] This technical solution manages patients through a control group and optimizes patients' training tasks through comparative data analysis, which can achieve effective and flexible management of patients' pelvic floor muscle rehabilitation training tasks, thereby improving patients' pelvic floor muscle rehabilitation training experience.

[0028] The technical solutions of the embodiments of the present disclosure can be applied to various management scenarios involving pelvic floor muscle rehabilitation training. In these application scenarios, the technical solutions of the embodiments of the present disclosure can assist in formulating training tasks.

[0029] Regarding the training tasks, they may be training tasks based on pelvic floor muscle rehabilitation training equipment.

[0030] Figure 1 is an example diagram showing an application scenario according to an exemplary embodiment. Figure 1 As shown, this application scenario involves data acquisition equipment and data processing equipment.

[0031] The data acquisition device may be a pelvic floor muscle rehabilitation training device, or a terminal device that interacts with the pelvic floor muscle rehabilitation training device and can acquire pelvic floor muscle rehabilitation training data.

[0032] The data processing device can be a host computer, a server, etc., which is connected to the data acquisition device and is used to perform data analysis based on the data collected by the data acquisition device, thereby realizing rehabilitation training management.

[0033] Regarding pelvic floor muscle rehabilitation training devices, for example, electrical stimulation devices can stimulate the pelvic floor muscles through electrical signals, enhancing muscle strength and coordination. Biofeedback training devices work by using sensors to convert the contraction and relaxation of the pelvic floor muscles into visual signals, helping patients more accurately perceive and control pelvic floor muscle movements. Method: Electrode patches or probes detect the electrical activity of the pelvic floor muscles and display it on a screen. Patients then adjust their muscle contraction and relaxation based on the feedback signals.

[0034] Figure 2 This is a flowchart of a pelvic floor muscle rehabilitation training management method based on data analysis according to an exemplary embodiment. The method can be applied to Figure 1 The data processing device shown, the method comprises the following steps:

[0035] Step S21 : obtaining pelvic floor muscle rehabilitation training data obtained by patients to be controlled for management using the original training task to perform pelvic floor muscle rehabilitation training. The patients to be controlled for management include at least two patients who meet preset control management conditions.

[0036] Step S22: performing a comparative analysis on the pelvic floor muscle rehabilitation training data corresponding to the patients under control management to obtain a comparative analysis result.

[0037] Step S23 , optimizing the original training task of the patient to be managed as a control according to the control analysis result to obtain an optimized training task.

[0038] Step S24, determining a new training task based on the control analysis results and the optimized training task, and feeding back the new training task to the patient to be controlled, so that the patient to be controlled can use the new training task to perform pelvic floor muscle rehabilitation training.

[0039] In the disclosed embodiment, patients who need pelvic floor muscle rehabilitation training are managed in a control group manner, wherein each control group of patients can be regarded as patients to be managed as a control group, and patients in different control groups are managed separately.

[0040] Therefore, in step S21, the patients to be managed in a control group can be understood as all patients in a control group. Different control groups can be managed in the same manner. In the embodiment of the present disclosure, one control group is taken as an example for introduction.

[0041] In some embodiments, the patients in the control group can be at least two patients who meet the preset control management conditions. For example, a management method of 2 patients as a group or a management method of 4 patients as a group can be adopted. For ease of management, the number of patients in a control group can be small.

[0042] Therefore, patients can be divided into multiple control groups before management.

[0043] As an optional implementation, determining the patients to be managed (i.e., the patient control group) includes: obtaining patient information and original training tasks corresponding to multiple patients, where the original training tasks are training tasks formulated by doctors for patients; based on the patient information and original training tasks corresponding to the multiple patients, determining at least two patients who meet preset control management conditions from the multiple patients, wherein the preset control management conditions include conditions related to the training tasks and conditions related to the patient information; and determining at least two patients as patients to be managed in control.

[0044] In this embodiment, the original training tasks for the multiple patients are formulated by doctors, and the patient information is information related to the patients and can be directly obtained from the corresponding database.

[0045] In some embodiments, the preset control management conditions include conditions related to the training task and conditions related to the patient information.

[0046] Regarding the training tasks, for example, training task one: training through training exercises, three sets every morning, noon and evening; training task two: training through electrical stimulation, training once every other day; training task three: training through biofeedback, training once every other day.

[0047] Patient information may include basic information such as patient age and severity of the disease, as well as the estimated recovery time determined at the time of diagnosis.

[0048] For example, the conditions related to the training task may limit the type of training task, the intensity of the training task, the number of training tasks, etc. Only when these conditions are met can the patient be included in the control management.

[0049] The conditions related to the patient information can limit the severity of the patient, the age of the patient, and the expected recovery time of the patient (determined by the doctor at the time of diagnosis). Only when these conditions are met can the patient be included in the control management.

[0050] In some embodiments, the preset control management conditions can be configured according to the needs of different application scenarios.

[0051] For example, the conditions related to the training tasks may be: the types of the training tasks are the same or similar; the total number of the training tasks is the same or similar; the intensity of the training tasks is the same or similar.

[0052] For example, the conditions related to the patient information may be: the severity of the patients is the same or similar, the age of the patients is similar, and the expected recovery time of the patients is similar.

[0053] Compare the patient information corresponding to multiple patients with the original training tasks, and determine whether each patient meets the conditions for being a control patient through the preset control management conditions. If so, they can be determined as patients to be controlled.

[0054] Figure 3 FIG. 1 is a schematic diagram showing a process for determining a control management patient according to an exemplary embodiment. Figure 3 As shown, there are currently nine patients requiring pelvic floor muscle rehabilitation training, Patients 1 to 9. The patient information and original training tasks for these nine patients are retrieved. Next, by comparing the preset control management conditions, four control management patient groups are obtained: [Patient 1, Patient 3], [Patient 2, Patient 7], [Patient 4, Patient 6], and [Patient 8, Patient 9].

[0055] Among them, each control management patient group includes two patients, and the remaining patient 5 does not meet the corresponding conditions and needs to be managed separately.

[0056] Furthermore, in step S11, the original training task corresponding to the patient to be managed is fed back to the patient to be managed, so that the patient to be managed can use the original training task to perform pelvic floor rehabilitation training; and the pelvic floor muscle rehabilitation training data uploaded by the patient to be managed is received.

[0057] In this embodiment, after the patient to be managed is determined, the original training task can be fed back to the patient to be managed; and the patient to be managed can subsequently upload the pelvic floor muscle rehabilitation training data. For example, the patient can independently record the pelvic floor muscle rehabilitation training data and upload it.

[0058] In some embodiments, pelvic floor muscle rehabilitation training usually involves pelvic floor muscle rehabilitation trainers, and data can also be uploaded by pelvic floor muscle rehabilitation trainers, which is not limited here.

[0059] In some embodiments, the pelvic floor muscle rehabilitation training data includes first pelvic floor muscle contraction data collected during the pelvic floor muscle rehabilitation training of the patient to be managed using the original training task, and second pelvic floor muscle contraction data collected after the patient to be managed completes the pelvic floor muscle rehabilitation training using the original training task.

[0060] In the embodiment of the present disclosure, a comparative analysis is performed based on pelvic floor muscle contraction data, wherein the pelvic floor muscle contraction data may include: muscle contraction strength and contraction frequency determined based on electrical signals converted from pelvic floor muscle contraction.

[0061] In some embodiments, during pelvic floor muscle rehabilitation training, the contraction of the pelvic floor muscles can be detected to obtain first pelvic floor muscle contraction data. Furthermore, after completing the pelvic floor muscle rehabilitation training, the autonomous contraction of the pelvic floor muscles can be detected over a period of time to obtain second pelvic floor muscle contraction data.

[0062] Furthermore, in step S22, the control analysis process may include: determining first control pelvic floor muscle contraction data from the first pelvic floor muscle contraction data corresponding to at least two patients respectively; determining second control pelvic floor muscle contraction data from the second pelvic floor muscle contraction data corresponding to at least two patients respectively; determining third control pelvic floor muscle contraction data corresponding to at least two patients respectively from the first pelvic floor muscle contraction data and the second pelvic floor muscle contraction data corresponding to at least two patients respectively; for any one of the at least two patients, determining the predicted training effect corresponding to the patient according to the first control pelvic floor muscle contraction data, the second control pelvic floor muscle contraction data and the third control pelvic floor muscle contraction data corresponding to the patient through a pre-trained prediction model; and determining the control analysis result according to the predicted training effects corresponding to the at least two patients respectively.

[0063] In some embodiments, taking the number of patients as 2 as an example, patient A corresponds to first pelvic floor muscle contraction data F1 and second pelvic floor muscle contraction data S1; patient B corresponds to first pelvic floor muscle contraction data F2 and second pelvic floor muscle contraction data S2.

[0064] Then, the first control pelvic floor muscle contraction data can be data determined from F1 and F2; the second control pelvic floor muscle contraction data can be data determined from S1 and S2; and the third control pelvic floor muscle contraction data corresponding to patient A can be data determined from F1 and S1, and the third control pelvic floor muscle contraction data corresponding to patient B can be data determined from F2 and S2.

[0065] It can be seen that the first control pelvic floor muscle contraction data and the second control pelvic floor muscle contraction data are not for specific patients and can be understood as control data between patients; the third control pelvic floor muscle contraction data is for a specific patient and can be understood as control data of the patient himself.

[0066] In some embodiments, the first control pelvic floor muscle contraction data may be data showing differences among first pelvic floor muscle contraction data corresponding to at least two patients. The second control pelvic floor muscle contraction data may be data showing differences among second pelvic floor muscle contraction data corresponding to at least two patients. The third control pelvic floor muscle contraction data may be data showing differences among first pelvic floor muscle contraction data and second pelvic floor muscle contraction data corresponding to any patient.

[0067] Furthermore, for any patient, a training effect prediction can be performed based on the first control pelvic floor muscle contraction data, the second control pelvic floor muscle contraction data and the third control pelvic floor muscle contraction data corresponding to the patient to obtain a predicted training effect.

[0068] Regarding the pre-trained prediction model, its training process may include: obtaining training data, the training data includes multiple training samples, each training sample includes: a first control sample, a second control sample, a third control sample and a sample label, wherein the first control sample is determined based on the first pelvic floor muscle contraction data sample, the second control sample is determined based on the second pelvic floor muscle contraction data sample, and the third control sample is determined based on the first pelvic floor muscle contraction data sample and the second pelvic floor muscle contraction data sample, the first pelvic floor muscle contraction data sample is pelvic floor muscle contraction data collected during the patient's pelvic floor muscle rehabilitation training using the training task, the second pelvic floor muscle contraction data sample is pelvic floor muscle contraction data collected after the patient completes the pelvic floor muscle rehabilitation training using the training task, and the sample label is the patient's actual training effect; based on the training data, the prediction model to be trained is trained to obtain a pre-trained prediction model.

[0069] In some embodiments, the implementation of the first control sample, the second control sample, and the third control sample can refer to the implementation of the three types of data described above. The difference is that the three types of data described above are determined based on the patients to be managed, while the three types of sample data here can be determined from a large amount of patient data. Furthermore, the sample label can be the actual training effect of the patient. Here, the sample label can be 0 or 1, with 1 representing recovery after training and 0 representing non-recovery after training.

[0070] In some embodiments, the pre-trained prediction model can be a model based on a multi-layer neural network, and different layers of neural networks are used to predict training effects based on different control samples; finally, the classification layer obtains the final predicted training effect based on the predicted training effect output by the multi-layer neural network.

[0071] As an example, the loss function of a multi-layer neural network or classification layer can be: Where N is the number of samples. K is the number of categories. ij is the label (0 or 1) that the i-th sample belongs to the j-th class. is the predicted probability that the i-th sample belongs to the j-th class.

[0072] In some embodiments, by extracting differential data between patients, as well as extracting differential data of the patient before and after training, the training effect of the patient can be effectively and accurately predicted.

[0073] In some embodiments, the predicted training effects corresponding to at least two patients can be used as control analysis results. Thus, the control analysis results are used to characterize the predicted training effects corresponding to the at least two patients, where the predicted training effects include the probability of recovery, i.e., the final output of the pre-trained prediction model is the probability of recovery.

[0074] In step S23, the original training task of the patient to be managed under control is optimized according to the control analysis result to obtain an optimized training task.

[0075] As an optional implementation, step S23 includes: when there is a recovery probability higher than a preset recovery probability among the recovery probabilities corresponding to at least two patients, determining the difference between the recovery probabilities corresponding to at least two patients; when the difference between the recovery probabilities corresponding to at least two patients is higher than the preset difference, updating the original training task corresponding to the patient with a lower recovery probability according to the original training task corresponding to the patient with a higher recovery probability, to obtain an optimized training task corresponding to the patient with a lower recovery probability; when the difference between the recovery probabilities corresponding to at least two patients is lower than the preset difference, determining the difference training task between the original training tasks corresponding to the at least two patients, and updating the original training tasks corresponding to the at least two patients according to the difference training task, to obtain the optimized training tasks corresponding to the at least two patients.

[0076] It can be understood that when one of the recovery probabilities corresponding to at least two patients is higher than the preset recovery probability, it means that there is a patient with better recovery condition. At this time, the training task can be optimized through differential analysis of the training tasks.

[0077] In some embodiments, the preset recovery probability may be a preconfigured recovery probability indicating a higher likelihood of recovery. For example, the preset recovery probability may be a value between 70% and 80%.

[0078] In some embodiments, the preset difference value may be a pre-configured difference value indicating a large difference in the probability of recovery. For example, the preset difference value may be a value between 5% and 10%.

[0079] In some embodiments, updating the original training tasks corresponding to patients with a lower probability of recovery based on the original training tasks corresponding to patients with a higher probability of recovery can include: adding the original training tasks corresponding to patients with a higher probability of recovery to the original training tasks corresponding to patients with a lower probability of recovery. When updating, the update can be based on the principle of non-duplication.

[0080] In some embodiments, updating the original training tasks corresponding to at least two patients according to the differential training tasks may include: analyzing the importance of the differential training tasks in the corresponding original training tasks, retaining the differential training tasks with higher importance, and replacing the differential training tasks with lower importance with differential training tasks with higher importance.

[0081] For example, if Patient A's Training Task 1 and Patient B's Training Task 2 are different training tasks, but all other training tasks are the same, then the importance of Training Task 1 in Patient A's training tasks is analyzed, and the importance of Training Task 2 in Patient B's training tasks is analyzed. If the analysis determines that Training Task 1 is more important than Patient A's training tasks, then Patient A's training tasks do not need to be updated, and Patient B's Training Task 2 needs to be replaced with Training Task 1.

[0082] In some embodiments, the importance of a training task can be determined based on the order of the training task, the frequency of training, the intensity of the training task, etc. Generally speaking, the higher the order, the higher the frequency of training, the greater the intensity, and the higher the importance.

[0083] As an optional implementation, step S23 includes: when there is no recovery probability higher than the preset recovery probability among the recovery probabilities corresponding to at least two patients, determining the same training tasks between the original training tasks corresponding to at least two patients, and updating the original training tasks corresponding to at least two patients based on the same training tasks to obtain optimized training tasks corresponding to at least two patients.

[0084] In this embodiment, when there is no recovery probability higher than the preset recovery probability among the recovery probabilities corresponding to at least two patients, it means that the recovery conditions of at least two patients are poor. At this time, training task optimization can be performed based on the same training task.

[0085] In some embodiments, the same training tasks for at least two patients may be directly adjusted to achieve task updating. For example, the intensity of the same training task may be optimized, and the frequency of the same training task may be optimized to increase the probability of recovery.

[0086] In some embodiments, the original training task is a training task formulated by a doctor for a patient to be managed under control.

[0087] As an optional implementation, step S24 includes: feeding back the optimized training task and control analysis results to the doctor; receiving the training task fed back by the doctor; and determining a new training task based on the training task fed back by the doctor and the optimized training task, wherein the new training task includes at least the optimized training task.

[0088] In this embodiment, the doctor can determine the doctor-specific training tasks based on the optimized training tasks based on the comparative analysis results. The training tasks reported by the doctor can be optimized training tasks, indicating no new task optimization suggestions. The training tasks reported by the doctor can be modified training tasks, which may include new training tasks or reduced training tasks, and may include training task suggestions given by the doctor.

[0089] In some embodiments, duplicates are removed from the training tasks provided by the physician and the optimized training tasks to obtain a final new training task. It is necessary to ensure that the new training task includes at least the optimized training task to achieve the corresponding training task optimization effect.

[0090] In some embodiments, the control patient group needs to be updated in a timely manner based on the patient's recovery status.

[0091] Therefore, as an optional embodiment, the method also includes: in response to detecting the presence of a recovered patient among the patients to be controlled, updating the preset control management conditions according to the patient information and original training tasks corresponding to the recovered patient to obtain updated control management conditions; obtaining the patient information and original training tasks corresponding to at least one new patient, where the original training tasks are the training tasks formulated by the doctor for the new patients; determining a target patient from at least one new patient according to the patient information and original training tasks corresponding to the at least one new patient, the patient information and original training tasks corresponding to the unrecovered patients among the patients to be controlled, and the updated control management conditions; and updating the patients to be controlled based on the target patient.

[0092] In this embodiment, the presence of recovered patients among the patients to be managed as controls indicates that the control patient group needs to be updated.

[0093] In some embodiments, the preset control management conditions are updated according to the patient information and original training task corresponding to the rehabilitation patient, which may include: updating the conditions in the preset control management conditions related to the expected rehabilitation time included in the patient information corresponding to the rehabilitation patient; updating the conditions in the preset control management conditions related to the task type of the training task according to the task type of the original training task corresponding to the rehabilitation patient.

[0094] In some embodiments, the accuracy of the estimated recovery time can be determined based on the actual recovery time of the recovering patient. For example, the greater the difference between the actual recovery time and the estimated recovery time, the lower the accuracy of the estimated recovery time.

[0095] Furthermore, if the accuracy of the estimated recovery time is low, the difference condition of the estimated recovery time in the preset control management conditions can be adjusted to be more relaxed. For example, the original conditions require that the difference in the estimated recovery time be within 1 day, then the difference in the estimated recovery time can be within 2 days in the updated conditions. If the accuracy of the estimated recovery time is high, the difference condition of the estimated recovery time in the preset control management conditions can be adjusted to be more stringent. For example, the original conditions require that the difference in the estimated recovery time be within 1 day, then the difference in the estimated recovery time can be within 0.5 days in the updated conditions.

[0096] In some embodiments, if the task type in the preset control management conditions includes the task type of the original training task corresponding to the rehabilitation patient, the conditions related to the task type in the preset control management conditions may not be updated.

[0097] If the task type in the preset control management conditions does not include the task type of the original training task corresponding to the rehabilitation patient, the task type of the original training task corresponding to the rehabilitation patient can be added to the preset control management conditions.

[0098] For example, the original preset control management conditions only limit control management to the same task type. Now it can be changed to allow control management when the task type includes the original training task corresponding to the rehabilitation patient.

[0099] Alternatively, other updating methods may be used, which are not limited here.

[0100] Figure 4 is a block diagram of a pelvic floor muscle rehabilitation training management device 400 based on data analysis according to an exemplary embodiment. Figure 4 As shown, the device includes:

[0101] The acquisition module 401 is used to obtain pelvic floor muscle rehabilitation training data obtained by patients to be controlled for management using the original training task to perform pelvic floor muscle rehabilitation training, wherein the patients to be controlled for management include at least two patients who meet preset control management conditions.

[0102] The analysis module 402 is used to perform a comparative analysis on the pelvic floor muscle rehabilitation training data corresponding to the patient to be managed, and obtain a comparative analysis result.

[0103] Optimization module 403 is configured to optimize the original training task of the patient to be managed based on the control analysis results to obtain an optimized training task. A new training task is determined based on the control analysis results and the optimized training task, and the new training task is fed back to the patient to be managed so that the patient to be managed can perform pelvic floor muscle rehabilitation training using the new training task.

[0104] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0105] Figure 5 FIG. 5 is a block diagram of an electronic device 500 according to an exemplary embodiment. Figure 5 As shown, the electronic device 500 may include: a processor 501 , a memory 502 , and may further include one or more of a multimedia component 503 , an input / output (I / O) interface 504 , and a communication component 505 .

[0106] The processor 501 is used to control the overall operation of the electronic device 500 to complete all or part of the steps in the pelvic floor muscle rehabilitation training management method based on data analysis. The memory 502 is used to store various types of data to support the operation of the electronic device 500. For example, these data may include instructions for any application or method operating on the electronic device 500, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 502 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The multimedia component 503 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 502 or sent through the communication component 505. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 504 provides an interface between the processor 501 and other interface modules. The above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 505 is used for wired or wireless communication between the electronic device 500 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more thereof, so the corresponding communication component 505 may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0107] In an exemplary embodiment, the electronic device 500 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-mentioned data analysis-based pelvic floor muscle rehabilitation training management method.

[0108] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When executed by a processor, the program instructions implement the steps of the aforementioned data analysis-based pelvic floor muscle rehabilitation training management method. For example, the computer-readable storage medium may be the aforementioned memory 502 including the program instructions. The program instructions may be executed by the processor 501 of the electronic device 500 to implement the aforementioned data analysis-based pelvic floor muscle rehabilitation training management method.

[0109] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program that can be executed by a processor. When the computer program is executed by the processor, the above-mentioned pelvic floor muscle rehabilitation training management method based on data analysis is implemented.

[0110] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program that can be executed by a processor. When the computer program is executed by the processor, the steps of the above-mentioned pelvic floor muscle rehabilitation training management method based on data analysis are implemented.

[0111] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0112] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0113] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A pelvic floor muscle rehabilitation training management method based on data analysis, characterized in that: include: Acquiring pelvic floor muscle rehabilitation training data obtained by patients to be controlled for management using the original training task to perform pelvic floor muscle rehabilitation training, wherein the patients to be controlled for management include at least two patients who meet preset control management conditions; Performing a control analysis on the pelvic floor muscle rehabilitation training data corresponding to the patients to be controlled, to obtain a control analysis result; Optimizing the original training task of the patient to be managed in comparison according to the control analysis results to obtain an optimized training task; Based on the control analysis results and the optimized training tasks, a new training task is determined, and the new training task is fed back to the patient to be controlled, so that the patient to be controlled performs pelvic floor muscle rehabilitation training using the new training task.

2. The pelvic floor muscle rehabilitation training management method according to claim 1, characterized in that: The pelvic floor muscle rehabilitation training management method further includes: Obtaining patient information and original training tasks corresponding to a plurality of patients, wherein the original training tasks are training tasks formulated by doctors for the patients; Determining at least two patients from the plurality of patients that meet preset control management conditions based on the patient information and original training tasks respectively corresponding to the plurality of patients, wherein the preset control management conditions include conditions related to the training tasks and conditions related to the patient information; determining the at least two patients as the patients to be managed as controls; The pelvic floor muscle rehabilitation training data obtained by the patient to be controlled undergoing pelvic floor muscle rehabilitation training using the original training task includes: Feeding back the original training task corresponding to the patient to be managed as a control to the patient to be managed as a control, so that the patient to be managed as a control performs pelvic floor rehabilitation training using the original training task; Receive pelvic floor muscle rehabilitation training data uploaded by the patient to be managed or the pelvic floor muscle rehabilitation training personnel.

3. The pelvic floor muscle rehabilitation training management method according to claim 1, characterized in that: The pelvic floor muscle rehabilitation training data includes first pelvic floor muscle contraction data collected during the process of the patient to be controlled using the original training task to perform pelvic floor muscle rehabilitation training and second pelvic floor muscle contraction data collected after the patient to be controlled completes the pelvic floor muscle rehabilitation training using the original training task. The comparative analysis of the pelvic floor muscle rehabilitation training data corresponding to the patient to be controlled is performed to obtain a comparative analysis result, including: Determining first control pelvic floor muscle contraction data from the first pelvic floor muscle contraction data corresponding to the at least two patients; Determining second control pelvic floor muscle contraction data from the second pelvic floor muscle contraction data corresponding to the at least two patients; Determining third control pelvic floor muscle contraction data corresponding to the at least two patients respectively from the first pelvic floor muscle contraction data and the second pelvic floor muscle contraction data corresponding to the at least two patients respectively; For any one of the at least two patients, determining a predicted training effect corresponding to the patient by using a pre-trained prediction model based on the first control pelvic floor muscle contraction data, the second control pelvic floor muscle contraction data, and the third control pelvic floor muscle contraction data corresponding to the patient; A control analysis result is determined based on the predicted training effects corresponding to the at least two patients.

4. The pelvic floor muscle rehabilitation training management method according to claim 3, characterized in that: The pelvic floor muscle rehabilitation training management method further includes: Acquire training data, where the training data includes multiple training samples, each training sample includes: a first control sample, a second control sample, a third control sample, and a sample label, wherein the first control sample is determined based on a first pelvic floor muscle contraction data sample, the second control sample is determined based on a second pelvic floor muscle contraction data sample, and the third control sample is determined based on the first pelvic floor muscle contraction data sample and the second pelvic floor muscle contraction data sample, the first pelvic floor muscle contraction data sample is pelvic floor muscle contraction data collected during a patient performing pelvic floor muscle rehabilitation training using a training task, the second pelvic floor muscle contraction data sample is pelvic floor muscle contraction data collected after the patient completes pelvic floor muscle rehabilitation training using the training task, and the sample label is the patient's actual training effect; The prediction model to be trained is trained according to the training data to obtain a pre-trained prediction model.

5. The pelvic floor muscle rehabilitation training management method according to claim 1, characterized in that: The control analysis results are used to characterize the predicted training effects corresponding to the at least two patients, the predicted training effects including: recovery probability. The original training tasks of the patients to be controlled are optimized based on the control analysis results to obtain optimized training tasks, including: In a case where one of the recovery probabilities corresponding to the at least two patients has a recovery probability higher than a preset recovery probability, determining a difference between the recovery probabilities corresponding to the at least two patients; When the difference between the recovery probabilities corresponding to the at least two patients is higher than a preset difference, the original training task corresponding to the patient with the lower recovery probability is updated according to the original training task corresponding to the patient with the higher recovery probability, to obtain an optimized training task corresponding to the patient with the lower recovery probability; When the difference between the recovery probabilities corresponding to the at least two patients is lower than a preset difference, a difference training task between the original training tasks corresponding to the at least two patients is determined, and based on the difference training task, the original training tasks corresponding to the at least two patients are updated to obtain optimized training tasks corresponding to the at least two patients.

6. The pelvic floor muscle rehabilitation training management method according to claim 1, characterized in that: The control analysis results are used to characterize the predicted training effects corresponding to the at least two patients, the predicted training effects including: recovery probability. The original training tasks of the patients to be controlled are optimized based on the control analysis results to obtain optimized training tasks, including: When there is no recovery probability higher than the preset recovery probability among the recovery probabilities corresponding to the at least two patients, the same training tasks between the original training tasks corresponding to the at least two patients are determined, and based on the same training tasks, the original training tasks corresponding to the at least two patients are updated to obtain optimized training tasks corresponding to the at least two patients.

7. The pelvic floor muscle rehabilitation training management method according to claim 1, characterized in that: The original training task is a training task formulated by a doctor for the patient to be managed under control. The new training task is determined based on the control analysis results and the optimized training task, including: Feedback the optimized training task and the control analysis result to the doctor; receiving training tasks fed back by the doctor; The new training task is determined according to the training task fed back by the doctor and the optimized training task, and the new training task at least includes the optimized training task.

8. The pelvic floor muscle rehabilitation training management method according to claim 1, characterized in that: The pelvic floor muscle rehabilitation training management method further includes: In response to detecting that a recovered patient appears among the patients to be controlled, the preset control management conditions are updated according to the patient information and original training tasks corresponding to the recovered patient to obtain updated control management conditions; Obtaining patient information and an original training task corresponding to at least one new patient, where the original training task is a training task formulated by a doctor for the new patient; Determine a target patient from the at least one new patient based on the patient information and original training task corresponding to the at least one new patient, the patient information and original training task corresponding to the unrecovered patient among the patients to be controlled, and the updated control management condition; According to the target patient, the patients to be managed under control are updated.

9. The pelvic floor muscle rehabilitation training management method according to claim 8, characterized in that: The preset control management conditions include conditions related to the type of training task and conditions related to the expected recovery time included in the patient information. The updating of the preset control management conditions according to the patient information and the original training task corresponding to the rehabilitation patient includes: According to the estimated recovery time included in the patient information corresponding to the recovered patient, updating the conditions related to the estimated recovery time included in the patient information in the preset control management conditions; According to the task type of the original training task corresponding to the rehabilitation patient, the conditions related to the task type of the training task in the preset control management conditions are updated.

10. A pelvic floor muscle rehabilitation training management system based on data analysis, characterized in that: include: Data acquisition equipment, used to collect pelvic floor muscle rehabilitation training data; A data processing device for executing the pelvic floor muscle rehabilitation training management method based on data analysis as described in any one of claims 1 to 9.

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