Rehabilitation management method and system for Parkinson's disease patient

Abstract

The invention discloses a Parkinson's disease patient rehabilitation management method and system, and relates to the technical field related to rehabilitation management, and the method comprises the steps: integrating and inputting rehabilitation guidance information of a user; obtaining a first time sequence rehabilitation medication management chain and a first time sequence rehabilitation training management chain; setting a tolerable deviation interval, and processing and outputting a second time sequence rehabilitation medication management chain and a second time sequence rehabilitation training management chain; collecting real-time rehabilitation medication data and real-time rehabilitation training data of the user; abnormal event detection is carried out respectively, and corresponding abnormal reminding signals are generated according to abnormal event detection results to be fed back to a user. The technical problems that in the prior art, out-of-hospital rehabilitation of the Parkinson's disease patient lacks real-time guidance, medication and training management is not standard, rehabilitation data is difficult to effectively analyze, and rehabilitation management accuracy, timeliness and accuracy of the Parkinson's disease patient are poor are solved. The technical effects of improving the timeliness and accuracy of rehabilitation guidance and reducing the abnormal risk of rehabilitation are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rehabilitation management, and particularly relates to a Parkinson's disease patient rehabilitation management method and system. BACKGROUND

[0002] Parkinson's disease is a common neurodegenerative disease, and its pathological characteristics are progressive degeneration of dopaminergic neurons in the substantia nigra of the midbrain, which leads to motor symptoms such as bradykinesia, tremor, and muscle rigidity in patients, as well as non-motor symptoms such as constipation, insomnia, and depression. The traditional rehabilitation management mode for Parkinson's disease mainly relies on patients regularly visiting hospitals for reexamination, and doctors adjusting medication regimens according to experience and giving rehabilitation training suggestions. However, patients lack real-time and effective rehabilitation guidance outside the hospital, and problems such as non-standard medication time and dosage and substandard rehabilitation training frequently occur. In addition, due to the large individual differences in the progression of Parkinson's disease, fixed rehabilitation programs are difficult to adapt to the dynamic changes of patients. Furthermore, the rehabilitation data of patients are difficult to be systematically recorded and analyzed, and doctors cannot timely grasp the real rehabilitation status of patients, resulting in low efficiency of rehabilitation management and difficulty in guaranteeing the rehabilitation effect of patients.

[0003] In the related art at the present stage, there are technical problems of poor accuracy, timeliness and precision of Parkinson's disease patient rehabilitation management due to lack of real-time guidance for Parkinson's disease patient rehabilitation outside the hospital, non-standard medication and training management, and difficulty in effective analysis of rehabilitation data. SUMMARY

[0004] The present application provides a Parkinson's disease patient rehabilitation management method and system, which solves the technical problems of lack of real-time guidance for Parkinson's disease patient rehabilitation outside the hospital, non-standard medication and training management, and difficulty in effective analysis of rehabilitation data in the prior art, resulting in poor accuracy, timeliness and precision of Parkinson's disease patient rehabilitation management, and achieves the technical effects of improving the timeliness and accuracy of rehabilitation guidance and reducing the risk of rehabilitation abnormalities.

[0005] The application provides a Parkinson's disease patient rehabilitation management method, comprising: integrating input rehabilitation guidance information of a user, wherein the rehabilitation guidance information comprises rehabilitation medication information and rehabilitation training information, and the user is a Parkinson's disease patient; performing time sequence processing on the rehabilitation guidance information to obtain a first time sequence rehabilitation medication management chain and a first time sequence rehabilitation training management chain; setting a tolerable deviation interval, processing the first time sequence rehabilitation medication management chain and the first time sequence rehabilitation training management chain according to the tolerable deviation interval, and outputting a second time sequence rehabilitation medication management chain and a second time sequence rehabilitation training management chain; collecting real-time rehabilitation medication data and real-time rehabilitation training data of the user; performing abnormal event detection on the real-time rehabilitation medication data and the real-time rehabilitation training data based on the second time sequence rehabilitation medication management chain and the second time sequence rehabilitation training management chain respectively, and feeding back corresponding abnormal reminding signals to the user according to the abnormal event detection results.

[0006] In a possible implementation, the Parkinson's disease patient rehabilitation management method further performs the following processing: connecting a rehabilitation training device, wherein the rehabilitation training device guides the user to perform rehabilitation training according to rehabilitation training data pre-stored in each time sequence node of the second time sequence rehabilitation medication management chain; comparing the real-time rehabilitation training data with the rehabilitation training data pre-stored in each time sequence node of the second time sequence rehabilitation medication management chain to obtain a training quality evaluation result; feeding back the training quality evaluation result to the rehabilitation training device to perform feedback optimization on the second time sequence rehabilitation training management chain, and obtaining a third time sequence rehabilitation training management chain.

[0007] In a possible implementation, the Parkinson's disease patient rehabilitation management method further performs the following processing: the rehabilitation training data pre-stored in each time sequence node of the second time sequence rehabilitation medication management chain comprises a training type, a target action, a recommended frequency, a predicted execution time, a completion state and patient feedback information field.

[0008] In a possible implementation, the Parkinson's disease patient rehabilitation management method further performs the following processing: extracting action execution trajectory, action execution time and action execution force feedback information under each training type in the real-time rehabilitation training data, performing similarity comparison with pre-stored rehabilitation training data, and obtaining action completion degree, execution delay time and frequency compliance rate; performing weight calculation on the action completion degree, execution delay time and frequency compliance rate to obtain a training quality evaluation result.

[0009] In a possible implementation, the Parkinson's disease patient rehabilitation management method further performs the following processing: the rehabilitation medication data pre-stored in each time sequence node of the second time sequence rehabilitation medication management chain comprises a medication type, a dose, a predicted intake time, a state identifier and an actual intake time field.

[0010] In a possible implementation, the Parkinson's disease patient rehabilitation management method further performs the following processing: a tolerable deviation interval is set, including a medication tolerable deviation interval and a training tolerable deviation interval; wherein the medication tolerable deviation interval includes a medication time deviation range, and the training tolerable deviation interval includes a training time deviation range and a training action deviation range.

[0011] In a possible implementation, the Parkinson's disease patient rehabilitation management method further performs the following processing: the abnormal event detection result includes a medication abnormal event and / or a training abnormal event, wherein the medication abnormal event includes missed medication, wrong medication and delayed medication, and the training abnormal event includes non-execution, interruption of execution and deviation of execution; a voice conversion module is established based on an NLP recognition technology, used for converting a voice early warning signal of the medication abnormal event and / or the training abnormal event, and generating an abnormal reminding signal to feed back a voice to the user.

[0012] In a possible implementation, the Parkinson's disease patient rehabilitation management method further performs the following processing: a multi-modal information integration channel is obtained; multi-modal rehabilitation guidance information is obtained, and the multi-modal rehabilitation guidance information is input into the multi-modal information integration channel for rehabilitation task semantic extraction to construct a rehabilitation task metadata set; the rehabilitation task metadata set is divided to obtain rehabilitation medication information and rehabilitation training information.

[0013] In a possible implementation, the Parkinson's disease patient rehabilitation management method further performs the following processing: a drug possession management module is set, and the method includes: entering current possession drug information of the user in the drug possession management module, and generating a drug vacancy reminding signal when the user is managed for rehabilitation medication according to the first time sequence rehabilitation medication management chain and the possession drug is insufficient.

[0014] The application also provides a Parkinson's disease patient rehabilitation management system, comprising: a rehabilitation guidance information integration module, configured to integrate rehabilitation guidance information of a user, wherein the rehabilitation guidance information comprises rehabilitation medication information and rehabilitation training information, and the user is a Parkinson's disease patient; a time sequence processing module, configured to perform time sequence processing on the rehabilitation guidance information to obtain a first time sequence rehabilitation medication management chain and a first time sequence rehabilitation training management chain; a management chain processing module, configured to set a tolerable deviation interval, process the first time sequence rehabilitation medication management chain and the first time sequence rehabilitation training management chain according to the tolerable deviation interval, and output a second time sequence rehabilitation medication management chain and a second time sequence rehabilitation training management chain; a real-time data acquisition module, configured to acquire real-time rehabilitation medication data and real-time rehabilitation training data of the user; and an abnormal event detection module, configured to perform abnormal event detection on the real-time rehabilitation medication data and the real-time rehabilitation training data based on the second time sequence rehabilitation medication management chain and the second time sequence rehabilitation training management chain respectively, and generate corresponding abnormal reminding signals according to the abnormal event detection results to feed back to the user.

[0015] The Parkinson's disease patient rehabilitation management method and system provided by the application integrate rehabilitation guidance information of a user, obtain a first time sequence rehabilitation medication management chain and a first time sequence rehabilitation training management chain, set a tolerable deviation interval, process and output a second time sequence rehabilitation medication management chain and a second time sequence rehabilitation training management chain, acquire real-time rehabilitation medication data and real-time rehabilitation training data of the user, perform abnormal event detection respectively, and generate corresponding abnormal reminding signals according to the abnormal event detection results to feed back to the user. The technical problems that Parkinson's disease patient rehabilitation is lack of real-time guidance, medication and training management are not standardized, and rehabilitation data is difficult to effectively analyze, resulting in poor accuracy, timeliness and accuracy of Parkinson's disease patient rehabilitation management in the prior art are solved, and the technical effects of improving the timeliness and accuracy of rehabilitation guidance and reducing rehabilitation abnormal risk are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings of the embodiments of the application will be briefly introduced below. The flowcharts are used to illustrate the operations performed by the system according to the embodiments of the application in the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously according to needs. Meanwhile, other operations can be added to these processes, or one or more steps of operations can be removed from these processes.

[0017] Figure 1 A Parkinson's disease patient rehabilitation management method process schematic diagram is provided for the embodiments of the application.

[0018] Figure 2A structure schematic diagram of a Parkinson's disease patient rehabilitation management system provided by an embodiment of the present application.

[0019] Reference signs: rehabilitation guidance information integration module 10, timing processing module 20, management chain processing module 30, real-time data acquisition module 40, and abnormal event detection module 50. DETAILED DESCRIPTION

[0020] The above description is only a summary of the technical scheme of the present application. In order to make the technical means of the present application more clear, the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described.

[0021] In order to make the purposes, technical schemes and advantages of the present application more clear, the following will further describe the present application in combination with the drawings, and the described embodiments should not be regarded as limiting the present application. All other embodiments obtained by those skilled in the art without making creative labor belong to the scope of protection of the present application.

[0022] In the following description, "some embodiments" are related to a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The term "first\second" is only to distinguish similar objects, and does not represent the specific order of the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, systems, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0023] The present application provides a Parkinson's disease patient rehabilitation management method, as shown in the method comprises: Figure 1 The method comprises the following steps:

[0024] Step S100, the rehabilitation guidance information of the user is integrated, and the rehabilitation guidance information includes rehabilitation medication information and rehabilitation training information, wherein the user is a Parkinson's disease patient.

[0025] Preferably, the rehabilitation drug information and rehabilitation training information related to Parkinson's disease patients are integrated to obtain the rehabilitation guidance information of the user, and are input into the rehabilitation management system. Specifically, the rehabilitation drug information and rehabilitation training information of Parkinson's disease patients are collected and input, wherein the rehabilitation drug information includes detailed information of the drugs used by Parkinson's disease patients, such as drug names (e.g. levodopa, dopamine receptor agonists, etc.), drug doses (how many milligrams or milliliters are taken each time), drug taking times (specific taking time each day, such as 8 am, 2 pm, etc.), drug taking frequency (how many times a day), drug side effect information (such as possible symptoms of nausea, dizziness, etc.), drug precautions (such as whether to take on an empty stomach, etc.), and drug adjustment strategies at different stages (changes in drug dosage or type as the disease progresses). Rehabilitation training information includes various rehabilitation training content related information for Parkinson's disease patients, such as training items (such as limb movement training, including joint range of motion training, balance training, gait training, etc.; language training, such as pronunciation, speech rate, tone training; swallowing training, etc.), training intensity (length of each training, number of times for each group of training, etc.), training frequency (how many times a week), specific steps and methods of training (specific operation method of each training action), goals and adjustment plans for rehabilitation training at different stages (adjustment of training items and intensity as the rehabilitation progresses), etc.

[0026] Further, step S100 further comprises step S110, obtaining a multi-modal information integration channel; step S120, obtaining multi-modal rehabilitation guidance information, inputting the multi-modal rehabilitation guidance information into the multi-modal information integration channel for rehabilitation task semantic extraction, and constructing a rehabilitation task metadata set; step S130, dividing the rehabilitation task metadata set to obtain rehabilitation drug information and rehabilitation training information.

[0027] Preferably, the multi-modal information integration channel is used to receive and integrate different modalities of patient rehabilitation information, such as text, image, audio, video, etc., and then collect multi-modal rehabilitation guidance information related to Parkinson's disease patients from multiple channels, including obtaining text data from rehabilitation guidance manuals written by doctors, rehabilitation recommendations in academic literature; collecting image data from pictures showing rehabilitation training actions, brain images, etc.; obtaining audio data from doctors' voice explanations of rehabilitation precautions; collecting video data from demonstration videos of rehabilitation training, records of patients' treatment processes; and inputting the multi-modal rehabilitation guidance information into the multi-modal information integration channel.

[0028] Preferably, natural language processing technology, image recognition technology, audio processing technology, etc. are used for semantic extraction of rehabilitation tasks, that is, the semantic content in the multi-modal rehabilitation guidance information is analyzed and extracted, for example, for text information, through morphological analysis, syntactic analysis, semantic role labeling, etc. The key information related to rehabilitation tasks is found, such as drug name, medication time, training action name, training frequency, etc. For image information, through image recognition, the key features of rehabilitation training action posture, angle, etc. are determined. For audio and video information, they are first converted into text form (such as speech to text), and then similar semantic extraction is performed.

[0029] Preferably, the extracted semantic information is organized to form a structured rehabilitation task metadata set, for example, it may contain "drug: levodopa, dose: 100mg each time, 3 times a day" "training action: upper limb stretching, frequency: 2 times a day, 10 sets each time" and the like. Finally, the rehabilitation task metadata set is divided according to the category of information. Specifically, the information related to drug treatment is extracted to form rehabilitation medication information, including the name of the drug, the dose, the medication time, the medication frequency, etc. At the same time, the information related to rehabilitation training is extracted to form rehabilitation training information, including the name of the training action, the time arrangement of the training, the intensity of the training, the frequency of the training, etc. According to the rehabilitation medication information, the medication of the patient can be monitored, and according to the rehabilitation training information, the training progress and effect of the patient can be evaluated, so as to provide more accurate and effective rehabilitation guidance and treatment scheme adjustment for Parkinson's disease patients.

[0030] Step S200, time-series processing of the rehabilitation guidance information is performed to obtain a first time-series rehabilitation medication management chain and a first time-series rehabilitation training management chain.

[0031] Preferably, the rehabilitation guidance information is time-sequenced, that is, the rehabilitation medication information and the rehabilitation training information of the Parkinson's disease patient are analyzed and arranged in chronological order to form a rehabilitation management chain with time sequence characteristics. Specifically, the time-sequencing of the rehabilitation medication information includes extracting and arranging the information related to time in the input rehabilitation medication information, such as the specific time point of each medication of the patient, the change of the drug dose in different stages, etc. in chronological order; then analyzing the time sequence data to mine the medication rules of the patient, such as observing the change of the patient's demand for drug dose in different stages of disease and the adjustment of the medication time over time, etc. to find the medication mode and trend of the patient; then associating the medication information at each time point according to the obtained medication rules and trends to form a complete management chain, that is, a medication management chain, which not only contains the medication history of the patient, but also can predict the future medication, for example, according to the current condition and medication effect of the patient, combined with the past medication data, the drug dose and medication time required in the next stage are predicted.

[0032] Preferably, the time-sequencing of the rehabilitation training information includes time-sequencing the training time, project, intensity, etc. in the rehabilitation training information, arranging the specific time of each training, the arrangement of training projects and the change of training intensity in chronological order to form a time sequence of training information; then analyzing the training progress of the patient in different stages according to the time sequence data, such as observing the improvement of the patient's limb motor function and the improvement of the patient's language expression ability in a period of time, etc. to evaluate the effect of the rehabilitation training, and analyze the training intensity and the rehabilitation effect to determine the most suitable training intensity and frequency for the patient; then integrating and associating the training information at each time point to build a management chain reflecting the whole process of the patient's rehabilitation training, which can clearly show the training course of the patient from the beginning of the training to the current stage, including the adjustment of the training project, the increase or decrease of the training intensity, etc. Through the analysis of the training management chain, doctors and rehabilitation therapists can find the problems in the training process in time, such as poor training effect or excessive training intensity leading to patient fatigue, etc. so as to adjust the training scheme in time and improve the effect of the rehabilitation training.

[0033] Step S300, setting a tolerable deviation interval, processing the first time-sequenced rehabilitation medication management chain and the first time-sequenced rehabilitation training management chain according to the tolerable deviation interval, and outputting the second time-sequenced rehabilitation medication management chain and the second time-sequenced rehabilitation training management chain.

[0034] The step S300 further comprises setting a tolerable deviation interval, the tolerable deviation interval comprising a medication tolerable deviation interval and a training tolerable deviation interval; wherein the medication tolerable deviation interval comprises a medication time deviation range, and the training tolerable deviation interval comprises a training time deviation range and a training action deviation range.

[0035] Preferably, setting the tolerable deviation interval refers to setting the medication tolerable deviation interval and the training tolerable deviation interval, wherein the medication tolerable deviation interval comprises a medication time deviation range, and the tolerable deviation interval setting needs to be comprehensively adjusted and optimized according to the individual differences of the patient, the severity of the disease, and the requirements of rehabilitation treatment, etc. Specifically, taking the commonly used Parkinson's disease treatment drug levodopa as an example, it needs to reach a stable blood drug concentration within a certain time to effectively control the symptoms. The doctor will develop a detailed medication plan according to the patient's condition and physical condition, such as taking 4 times a day, with an interval of about 6 hours. Due to various situations that the patient may encounter in daily life, it is difficult to accurately take medicine on time every 6 hours. After comprehensive consideration, the medication time deviation range is set to ± 30 minutes, that is, the patient takes medicine within 30 minutes before and after the prescribed medication time, which is considered to be within the tolerable deviation interval.

[0036] Preferably, the training tolerable deviation interval comprises a training time deviation range and a training action deviation range. Specifically, assuming that the rehabilitation training plan requires the patient to perform limb movement training 3 times a day, in the morning, afternoon and evening respectively, and each training lasts for 30 minutes. Considering the patient's physical condition and daily activity arrangement, such as adjusting the training time due to fatigue, or slightly delaying the training due to other matters, the time deviation is set, that is, the training time deviation range is set to ± 1 hour, that is, the patient can start training within 1 hour before and after the planned training time. For example, the morning training plan starts at 10 o'clock, and the patient starts training between 9 o'clock and 11 o'clock, which is considered to meet the requirements. Taking the upper limb stretching training as an example, the standard action requires the patient to stretch the arm straight forward to 90° angle with the body. Considering that Parkinson's disease patients may have symptoms such as limb stiffness and tremor, it is difficult to achieve an accurate 90°. According to the average disease severity of the patient, the training action deviation range is set to ± 15°, that is, the angle of the patient's arm lifting between 75° and 105° is considered to be within the tolerable deviation interval, so as to ensure that the patient can complete the training action and achieve a certain rehabilitation effect.

[0037] Preferably, the first time sequence rehabilitation medication management chain is processed according to the allowable deviation interval. For each medication time point in the first time sequence rehabilitation medication management chain, it is judged whether it is within the set medication time deviation range. If it is within the range, the medication information is retained and marked as normal. If the medication is outside the deviation range, the impact on the overall treatment effect is evaluated. For example, if it is occasionally slightly outside the deviation range, it is compensated by subsequent normal medication. However, if it is frequently or severely outside the deviation range, it is judged whether the medication regimen needs to be adjusted in combination with the patient's condition, other medication information, and rehabilitation progress, etc. The relevant information is entered into the second time sequence rehabilitation medication management chain to provide a more comprehensive medication management perspective, so as to more accurately adjust the medication regimen and improve the effect of drug treatment.

[0038] Preferably, the first time sequence rehabilitation training management chain is processed according to the allowable deviation interval. For training time, it is also judged whether each training time point is within the training time deviation range. If it is within the range, the training record is entered into the second time sequence rehabilitation training management chain. If it is outside the range, the impact on the training effect is evaluated according to the degree and frequency of the deviation. If the deviation is relatively serious, the patient is communicated to understand the reason, such as whether it is caused by physical discomfort or other special circumstances, and whether the training plan needs to be adjusted. For training actions, the patient's training actions are monitored during the training process through motion sensors, video analysis systems, etc. If the action deviation is within the allowable range, it means that the patient's training actions basically meet the requirements. If the action deviation is outside the range, the patient is reminded to correct the action in time, and the situation is recorded. For training action deviations that are difficult to correct, the patient's physical condition is re-evaluated, the training method is adjusted or the training difficulty is reduced to ensure the safety and effectiveness of the training. The second time sequence rehabilitation training management chain is finally output to comprehensively understand the patient's training situation, which helps to correct the patient's wrong actions in time, avoid poor rehabilitation effect or increased risk of injury caused by wrong actions, and improve the quality and effect of rehabilitation training.

[0039] Step S400, collecting real-time rehabilitation medication data and real-time rehabilitation training data of the user.

[0040] Preferably, real-time collection of actual medication-related information and training-related information in the user's rehabilitation process obtains real-time rehabilitation medication data and real-time rehabilitation training data. The real-time rehabilitation medication data can include medication time, drug type and dosage, medication method, etc. Specifically, the specific time of each medication of the patient is recorded to understand whether the patient takes medication on time according to the prescribed time, compared with the set medication time tolerance interval to determine whether it is within a reasonable range. The specific drug name, dosage form (such as tablets, capsules, oral liquid, etc.) and dosage of each medication are determined for the patient. For patients with Parkinson's disease, they may take multiple drugs such as levodopa and dopamine receptor agonists. Real-time collection helps doctors master the execution of the patient's medication regimen. At the same time, it is determined whether the patient takes medication orally or by injection, and the frequency of medication, such as several times a day or several times a week, etc.

[0041] Preferably, the real-time rehabilitation training data can include training time, training action and training intensity, i.e. the time when the patient starts and ends training is recorded to obtain the actual training duration, which is compared with the training time tolerance interval to check whether it meets the training plan requirements. With the help of sensors, cameras and other devices, specific action information during training is collected, such as joint activity angle, limb movement trajectory, muscle contraction data, etc. for limb movement training to determine whether the patient's training action is within the training action deviation range and whether it meets the specifications and requirements of rehabilitation training. Through heart rate monitoring devices, motion sensors, etc., the patient's exercise intensity information during training is obtained, such as heart rate changes, exercise speed, strength, etc. to assess whether the patient's training intensity is appropriate and whether the expected rehabilitation training effect is achieved. By obtaining real-time rehabilitation data, doctors, rehabilitation therapists, etc. are provided with accurate and timely information to better understand the patient's rehabilitation progress, adjust the rehabilitation program in a timely manner, improve the effect and quality of rehabilitation treatment, and scientifically manage the rehabilitation process.

[0042] Further, step S400 further includes step S410 of connecting a rehabilitation training device, wherein the rehabilitation training device guides the user to perform rehabilitation training according to the pre-stored rehabilitation training data of each time sequence node in the second time sequence rehabilitation medication management chain; step S420 compares the real-time rehabilitation training data with the pre-stored rehabilitation training data of each time sequence node in the second time sequence rehabilitation medication management chain to obtain a training quality evaluation result; and step S430 feeds back the training quality evaluation result to the rehabilitation training device to perform feedback optimization on the second time sequence rehabilitation training management chain to obtain a third time sequence rehabilitation training management chain.

[0043] The pre-stored rehabilitation training data of each time sequence node in the second time sequence rehabilitation medication management chain includes training type, target action, recommended frequency, estimated execution time, completion status, and patient feedback information fields.

[0044] Preferably, the rehabilitation management system is connected to the Parkinson's disease patient rehabilitation training device, such as a smart rehabilitation robot, a wearable training device, a training instrument with a sensor, etc., through wired (such as USB, Ethernet) or wireless (such as Bluetooth, Wi-Fi) connection, to ensure that the rehabilitation management system can transmit instructions to the training device and receive data feedback from the device; and the rehabilitation training device guides the user to perform rehabilitation training according to the pre-stored rehabilitation training data of each time sequence node in the second time sequence rehabilitation medication management chain.

[0045] Preferably, the pre-stored rehabilitation training data of each time sequence node in the second time sequence rehabilitation medication management chain includes training type, target action, recommended frequency, estimated execution time, completion status, and patient feedback information fields. Specifically, the training type specifies the specific category of the patient's current training, such as limb movement training, language training, cognitive training, etc.; the target action describes the specific action that the patient needs to complete, such as the angle of arm extension, the degree of leg flexion, etc. in joint activity training; the recommended frequency, such as the number of times a certain training is performed per day, the number of repetitions per training, etc.; the estimated execution time provides the estimated time required for each training; the completion status and patient feedback information field record the completion of the patient's training, such as whether the training is completed on time, the quality of the completion, etc.; and the patient feedback information field allows the patient to input their feelings and problems encountered during the training.

[0046] Preferably, the real-time rehabilitation training data may include the patient's actual training action data (such as motion trajectory, speed, strength, etc.), training time data (actual training start and end time, training duration, etc.), training frequency data (actual training frequency, etc.), and physiological indicator data (such as heart rate, blood pressure, etc.); the acquired real-time rehabilitation training data is compared and analyzed with the pre-stored rehabilitation training data in the second time sequence rehabilitation medication management chain, for example, the difference between the actual training action and the target action is compared to determine whether the patient's action is standard; the actual training time and the estimated execution time are compared to see if the training is completed on time; the actual training frequency and the recommended frequency are analyzed for compliance, etc., to comprehensively evaluate the training quality of the patient, and the evaluation results may include training action accuracy score, training time rationality judgment, training frequency compliance, etc. For example, if the patient's actual action deviates greatly from the target action, the training quality evaluation result may show that the action accuracy is low; if the actual training time is much lower than the estimated execution time, it may indicate that the training intensity is insufficient, etc.

[0047] Preferably, the training quality evaluation result is fed back to the rehabilitation training device, and the rehabilitation training device can provide more personalized training guidance for the patient according to the evaluation result, including adjusting the difficulty of the training action, increasing or reducing the training intensity, etc. For example, if the evaluation result shows that the patient has difficulty in completing a certain action, the rehabilitation training device can reduce the difficulty of the action or provide more detailed action guidance. According to the training quality evaluation result, the training data in the second time sequence rehabilitation training management chain is adjusted and optimized to form a third time sequence rehabilitation training management chain, which is more in line with the actual training situation and rehabilitation needs of the patient, thereby providing more scientific and effective rehabilitation training guidance for the patient and improving the effect of rehabilitation treatment.

[0048] Further, step S420 further comprises step S421 of extracting the action execution trajectory, action execution time and action execution force feedback information under each training type in the real-time rehabilitation training data, and performing similarity comparison with the pre-stored rehabilitation training data to obtain the action completion degree, execution delay time and frequency compliance rate. Step S422 performs weight calculation on the action completion degree, execution delay time and frequency compliance rate to obtain the training quality evaluation result.

[0049] Preferably, the action execution trajectory, action execution time and action execution force feedback information under each training type in the real-time rehabilitation training data are extracted, wherein the action execution trajectory refers to the path passed by the body part or training equipment during rehabilitation training, such as the movement path of the arm from the starting position to the end position during arm stretching training, including the angle change of the arm, the range of joint movement, etc. The action execution time is the time spent by the patient to complete each training action, such as the time experienced from the start of leg bending to complete bending. The action execution force feedback information mainly refers to the size of the force exerted by the patient during the execution of the training action, the change of the force, etc. For example, during grip training, the device records the value of each grip of the patient and the change trend of the grip during the entire action process, thereby reflecting the muscle strength and force control of the patient with Parkinson's disease.

[0050] Preferably, the real-time collected action execution trajectory is compared with the pre-stored target action execution trajectory, the similarity of the two is calculated, and the action completion degree is obtained, for example, the pre-stored target action is to stretch the arm and rotate 360 degrees, and the action trajectory actually performed by the patient may have deviation, and the degree of coincidence or proximity between the actual trajectory and the target trajectory is calculated, such as 80%, indicating that the action of the patient has 80% similarity with the target action; the action execution time is compared with the pre-stored expected action execution time, and the execution delay time is obtained. If the pre-stored expected time is 5 seconds to complete a certain action, and the patient actually takes 7 seconds, the execution delay time is 2 seconds, reflecting the difference between the patient in the action execution speed and the expectation; the number of times of actually completing the training action of the patient within a certain time is compared with the pre-stored recommended training frequency, and the frequency compliance rate is obtained, such as the pre-stored recommended frequency is to complete 10 training actions per minute, and the patient actually completes 7 times, and the frequency compliance rate is 70%.

[0051] Preferably, different weights are set for the action completion degree, the execution delay time and the frequency compliance rate according to the target and key point of the rehabilitation training. Generally, the weight of the action completion degree may be higher, and the weights of the execution delay time and the frequency compliance rate are adjusted according to the specific training condition and individual difference of the patient, for example, the weight of the action completion degree may be set as 0.6, the weight of the execution delay time is 0.2, and the weight of the frequency compliance rate is 0.2. Then the action completion degree, the execution delay time and the frequency compliance rate are multiplied by the respective weights, and then added to obtain the training quality evaluation result, so as to intuitively understand the overall performance and training quality level of the patient in the rehabilitation training.

[0052] Preferably, the step S400 further comprises that the pre-stored rehabilitation medicine data under each time sequence node in the second time sequence rehabilitation medicine management chain comprises a medicine type, a dose, a pre-estimated intake time, a state identifier and an actual intake time field.

[0053] Preferably, the type of medication is recorded to determine the type of medication that the patient needs to use, such as antibiotics, painkillers, vitamin drugs, etc., so as to ensure that the patient uses the correct medication for treatment and recovery, and to avoid medication errors; the dose specifies the amount of each medication, and different medications have different appropriate dose ranges. The doctor will determine the specific medication dosage according to the patient's condition, physical condition, age, weight, etc., to achieve the best therapeutic effect while minimizing side effects; the expected intake time refers to the specific time point or time period at which the patient should take the medication according to the treatment plan and the characteristics of the medication, which helps to maintain a stable blood drug concentration in the body and maintain effective therapeutic effects. For example, some medications need to be taken before meals to facilitate absorption, while others need to be taken after meals to reduce irritation to the gastrointestinal tract; the status identifier is used to record the status of the medication, such as whether it has been taken, whether it has been taken on time, whether it needs to be adjusted, etc., so as to timely understand the medication usage; the actual intake time records the time at which the patient actually takes the medication. By comparing the actual intake time with the expected intake time, it can be determined whether the patient takes the medication on time and the deviation of the medication time, so as to evaluate the therapeutic effect of the medication and analyze the patient's medication compliance.

[0054] In step S500, the real-time rehabilitation medication data and the real-time rehabilitation training data are subjected to abnormal event detection based on the second time sequence rehabilitation medication management chain and the second time sequence rehabilitation training management chain, respectively, and corresponding abnormal reminding signals are generated according to the abnormal event detection results to feed back to the user.

[0055] Preferably, the real-time rehabilitation medication data is subjected to abnormal event detection based on the second time sequence rehabilitation medication management chain, that is, the real-time acquired patient medication data is compared with the pre-stored data in the management chain, including checking whether the actual medication type taken by the patient is consistent with the pre-stored medication type, whether the actual dose is within the specified range, and whether the deviation between the actual intake time and the expected intake time exceeds the allowed threshold, etc. If it is found that the real-time data does not match the pre-stored data, such as the patient taking the wrong medication, the dose being wrong, or the medication time being seriously delayed, it is determined as an abnormal event. Similarly, the real-time rehabilitation training data is subjected to abnormal event detection based on the second time sequence rehabilitation training management chain, that is, the real-time collected rehabilitation training data (such as the actual training type performed by the patient, the motion execution trajectory, the execution time, the frequency, etc.) is compared with the pre-stored data in the management chain, including checking whether the actual training type performed by the patient conforms to the plan, whether the motion execution conforms to the requirements of the target motion, whether the motion execution time is within a reasonable range, and whether the training frequency meets the recommended frequency, etc. If it is found that the training behavior of the patient is significantly different from the pre-stored training data, such as performing the wrong training type, having a very low motion completion degree, having a too long execution delay time, or having a seriously substandard frequency, it is determined as an abnormal event.

[0056] Preferably, a corresponding abnormal reminder signal is then generated according to the type and severity of the abnormality, such as a sound reminder, a pop-up message box, a mobile phone text message or a push notification, and the abnormal reminder signal contains specific information about the abnormal event, and is finally sent to the user, so that the user can promptly understand the abnormal situation that occurs during the rehabilitation medication or rehabilitation training process, so as to take corresponding measures in time, such as taking the medicine as soon as possible, adjusting the training method, etc., so as to ensure the smooth progress of the rehabilitation process and avoid affecting the rehabilitation effect due to the continued existence of the abnormal situation.

[0057] Furthermore, step S500 also includes step S510, wherein the abnormal event detection results include abnormal medication events and / or abnormal training events, wherein abnormal medication events include missed doses, misdosing, and delayed medication, and abnormal training events include non-execution, execution interruption, and execution deviation; step S520, establishing a voice conversion module based on NLP recognition technology, for converting the abnormal medication events and / or the abnormal training events into voice warning signals, and generating abnormal reminder signals to provide voice feedback to the user.

[0058] Preferably, the abnormal event detection results include at least abnormal medication events or abnormal training events, wherein abnormal medication events include missed doses, mistaken doses and delayed medication. Specifically, missed doses refer to patients not taking medications according to the medication time and dosage specified in the second time-series rehabilitation medication management chain, that is, the medications that should be taken are not taken; mistaken doses mean that the patient took the wrong type of medication or the wrong dosage, for example, the doctor prescribed medication A, but the patient mistakenly took medication B, or the prescribed medication was one tablet, but the patient mistakenly took multiple tablets; delayed medication means that the patient did not take the medication on time at the expected intake time, but took the medication beyond the allowed time range. For example, the medication is required to be taken at 9 a.m., and a certain time deviation is allowed, but if the patient takes the medication at a time beyond the deviation range, such as after 9:30 a.m., it is considered delayed medication.

[0059] Preferably, abnormal training events include non-execution, execution interruption and execution deviation. Specifically, non-execution means that the patient does not perform rehabilitation training according to the training plan specified in the second time-series rehabilitation training management chain; execution interruption means that during the rehabilitation training, the patient does not complete the entire training process and stops training midway. For example, the training time is stipulated to be 30 minutes, and the patient stops after 10 minutes of training, which is an execution interruption; execution deviation means that when the patient is undergoing rehabilitation training, the actual actions, training frequency, training intensity, etc. performed are different from the prescribed rehabilitation training data. For example, the prescribed training action is to stretch the arm to 90 degrees, but the patient can only stretch it to 60 degrees, or the prescribed training is three times a day, but the patient only trains once. These are all execution deviations.

[0060] Preferably, a voice conversion module is constructed using natural language processing (NLP) recognition and integrated into the rehabilitation management system to interact with the abnormal event detection module for data. Specifically, the text information is analyzed, understood and processed, and the relevant information is converted into a voice signal. When an abnormal medication event or an abnormal training event is detected, the voice conversion module converts the relevant information of the abnormal event, such as the abnormal type, specific situation, etc., into a voice warning signal. For example, for the abnormal event of missed medication, the voice conversion module will convert the text information "You have not taken the [drug name] that you should have taken at [specific time], please take it as soon as possible" into a voice signal; then the generated voice warning signal is sent to the user through a rehabilitation training device, mobile phone application or other terminal device, and the voice broadcast reminds the user of the abnormal situation, so that the user can more intuitively understand the problems encountered in the rehabilitation medication or rehabilitation training process, and take corresponding measures to make adjustments in time, thereby improving the efficiency and effectiveness of rehabilitation management.

[0061] Furthermore, a rehabilitation management method for Parkinson's disease patients also includes setting up a drug holding management module, entering the user's current drug holding information into the drug holding management module, and when the user's rehabilitation medication is managed according to the first time-series rehabilitation medication management chain, if the drugs held are insufficient, a drug shortage reminder signal is generated.

[0062] Preferably, a drug holding management module is set up to record and manage the drug information currently owned by the user, that is, the user enters the relevant information of his existing drugs into the module, which may include the name, dosage form, specifications, quantity, expiration date and other details of the drug, so as to clearly understand the current drug situation in the user's hands; when the user's rehabilitation medication is managed according to the first time-series rehabilitation medication management chain, the user's current drug information is compared with the medication needs specified in the management chain. If the user's current number of drugs is not enough to meet the medication needs of the current rehabilitation stage in the first time-series rehabilitation medication management chain, a drug shortage reminder signal is generated to inform the user that the existing number of drugs is insufficient and that the drugs need to be replenished in time to avoid affecting the normal progress of rehabilitation treatment due to drug shortages.

[0063] In the above, refer to Figure 1 A method for rehabilitation management of Parkinson's disease patients according to an embodiment of the present invention is described in detail. Figure 2 A Parkinson's disease patient rehabilitation management system according to an embodiment of the present invention is described.

[0064] According to an embodiment of the present invention, a Parkinson's disease patient rehabilitation management system is used to solve the technical problems existing in the prior art, such as the lack of real-time guidance for Parkinson's disease patients' out-of-hospital rehabilitation, irregular medication and training management, and difficulty in effectively analyzing rehabilitation data, which lead to poor precision, timeliness, and accuracy in Parkinson's disease patients' rehabilitation management. This achieves the technical effect of improving the timeliness and accuracy of rehabilitation guidance and reducing the risk of rehabilitation abnormalities. Figure 2 As shown, a rehabilitation management system for Parkinson's disease patients includes: a rehabilitation guidance information integration module 10, a time sequence processing module 20, a management chain processing module 30, a real-time data acquisition module 40, and an abnormal event detection module 50.

[0065] A rehabilitation guidance information integration module 10 is used to integrate and input the user's rehabilitation guidance information, wherein the rehabilitation guidance information includes rehabilitation medication information and rehabilitation training information, wherein the user is a Parkinson's disease patient; a timing processing module 20 is used to perform timing processing on the rehabilitation guidance information to obtain a first timing rehabilitation medication management chain and a first timing rehabilitation training management chain; a management chain processing module 30 is used to set a tolerable deviation interval, process the first timing rehabilitation medication management chain and the first timing rehabilitation training management chain according to the tolerable deviation interval, and output a second timing rehabilitation medication management chain and a second timing rehabilitation training management chain; a real-time data acquisition module 40 is used to collect the user's real-time rehabilitation medication data and real-time rehabilitation training data; an abnormal event detection module 50 is used to perform abnormal event detection on the real-time rehabilitation medication data and the real-time rehabilitation training data based on the second timing rehabilitation medication management chain and the second timing rehabilitation training management chain, and generate a corresponding abnormal reminder signal based on the abnormal event detection result to provide feedback to the user.

[0066] The specific configuration of the real-time data acquisition module 40 will be described in detail below. The real-time data acquisition module 40 further includes: connecting to a rehabilitation training device, wherein the rehabilitation training device guides the user to perform rehabilitation training according to the rehabilitation training data pre-stored at each sequential node in the second sequential rehabilitation medication management chain; comparing the real-time rehabilitation training data with the rehabilitation training data pre-stored at each sequential node in the second sequential rehabilitation medication management chain to obtain a training quality assessment result; and feeding back the training quality assessment result to the rehabilitation training device, performing feedback optimization on the second sequential rehabilitation training management chain, and obtaining a third sequential rehabilitation training management chain.

[0067] The specific configuration of the real-time data acquisition module 40 will be described in detail below. The real-time data acquisition module 40 further includes: the rehabilitation training data pre-stored at each time-series node in the second time-series rehabilitation medication management chain includes fields for training type, target action, recommended frequency, expected execution time, completion status, and patient feedback information.

[0068] Next, the specific configuration of the real-time data acquisition module 40 will be described in detail. The real-time data acquisition module 40 further includes: extracting the action execution trajectory, action execution time, and action execution force feedback information under each training type in the real-time rehabilitation training data, performing similarity comparison with the pre-stored rehabilitation training data, obtaining the action completion degree, execution delay time, and frequency compliance rate; performing weight calculation on the action completion degree, execution delay time, and frequency compliance rate to obtain the training quality evaluation result.

[0069] Next, the specific configuration of the real-time data acquisition module 40 will be described in detail. The real-time data acquisition module 40 further includes: the pre-stored rehabilitation medication data in each time sequence node in the second time sequence rehabilitation medication management chain includes medication type, dose, expected intake time, state identifier, and actual intake time fields.

[0070] Next, the specific configuration of the management chain processing module 30 will be described in detail. The management chain processing module 30 can further include: setting a tolerable deviation interval, the tolerable deviation interval including a medication tolerable deviation interval and a training tolerable deviation interval; wherein the medication tolerable deviation interval includes a medication time deviation range, and the training tolerable deviation interval includes a training time deviation range and a training action deviation range.

[0071] Next, the specific configuration of the abnormal event detection module 50 will be described in detail. The abnormal event detection module 50 can further include: the abnormal event detection result includes medication abnormal events and / or training abnormal events, wherein the medication abnormal events include missed medication, incorrect medication, and delayed medication, and the training abnormal events include non-execution, execution interruption, and execution deviation; a voice conversion module is established based on NLP recognition technology, which is used for voice warning signal conversion of the medication abnormal events and / or the training abnormal events, and generates an abnormal reminder signal for voice feedback to the user.

[0072] Next, the specific configuration of the rehabilitation guidance information integration module 10 will be described in detail. The rehabilitation guidance information integration module 10 can further include: obtaining a multi-modal information integration channel; obtaining multi-modal rehabilitation guidance information, inputting the multi-modal rehabilitation guidance information into the multi-modal information integration channel for rehabilitation task semantic extraction, and constructing a rehabilitation task metadata set; dividing the rehabilitation task metadata set to obtain rehabilitation medication information and rehabilitation training information.

[0073] Below, a specific configuration of a Parkinson's disease patient rehabilitation management system will be described in detail. It includes: setting a medicine possession management module, entering the current medicine possession information of the user in the medicine possession management module, and generating a medicine vacancy reminding signal if the medicine possession is insufficient when the user is managed for rehabilitation medicine according to the first time sequence rehabilitation medicine management chain.

[0074] The above detailed description does not constitute a limitation on the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application. In some cases, the actions or steps described in the present application can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

Claims

1. A method for rehabilitation management of Parkinson's disease patients, characterized in that: The method comprises: Integrate and input the user's rehabilitation guidance information, the rehabilitation guidance information including rehabilitation medication information and rehabilitation training information, wherein the user is a Parkinson's disease patient; Performing time-series processing on the rehabilitation guidance information to obtain a first time-series rehabilitation medication management chain and a first time-series rehabilitation training management chain; Setting a tolerable deviation interval, processing the first sequential rehabilitation medication management chain and the first sequential rehabilitation training management chain according to the tolerable deviation interval, and outputting a second sequential rehabilitation medication management chain and a second sequential rehabilitation training management chain; Collecting real-time rehabilitation medication data and real-time rehabilitation training data of the user; Based on the second time-series rehabilitation medication management chain and the second time-series rehabilitation training management chain, abnormal event detection is performed on the real-time rehabilitation medication data and the real-time rehabilitation training data respectively, and corresponding abnormal reminder signals are generated according to the abnormal event detection results to provide feedback to the user.

2. The method according to claim 1, wherein After collecting the real-time rehabilitation training data, the method further includes: connecting a rehabilitation training device, wherein the rehabilitation training device guides the user to perform rehabilitation training according to the rehabilitation training data pre-stored at each time sequence node in the second time sequence rehabilitation medication management chain; Comparing the real-time rehabilitation training data with the rehabilitation training data pre-stored at each time series node in the second time series rehabilitation medication management chain to obtain a training quality evaluation result; The training quality evaluation result is fed back to the rehabilitation training device, and the second time-sequential rehabilitation training management chain is optimized to obtain a third time-sequential rehabilitation training management chain.

3. The method according to claim 2, wherein The rehabilitation training data pre-stored in each time sequence node in the second time sequence rehabilitation medication management chain includes fields such as training type, target action, recommended frequency, estimated execution time, completion status and patient feedback information.

4. The method according to claim 2, wherein Comparing the real-time rehabilitation training data with the rehabilitation training data pre-stored at each time series node in the second time series rehabilitation medication management chain to obtain a training quality evaluation result, the method comprising: Extracting the action execution trajectory, action execution time, and action execution force feedback information for each training type from the real-time rehabilitation training data, performing a similarity comparison with the pre-stored rehabilitation training data, and obtaining the action completion degree, execution delay time, and frequency compliance rate; The action completion degree, execution delay time and frequency compliance rate are weighted to obtain a training quality evaluation result.

5. The method according to claim 1, wherein The rehabilitation medication data pre-stored under each time sequence node in the second time sequence rehabilitation medication management chain includes medication type, dosage, expected intake time, status identification and actual intake time fields.

6. The method according to claim 1, wherein Setting a tolerable deviation interval, wherein the tolerable deviation interval includes a medication tolerable deviation interval and a training tolerable deviation interval; The medication tolerance deviation interval includes a medication time deviation range, and the training tolerance deviation interval includes a training time deviation range and a training movement deviation range.

7. The method according to claim 1, wherein The method includes: generating a corresponding abnormality reminder signal according to the abnormal event detection result and providing feedback to the user; The abnormal event detection results include abnormal medication events and / or abnormal training events, wherein abnormal medication events include missed medication, mistaken medication, and delayed medication, and abnormal training events include non-execution, execution interruption, and execution deviation; A voice conversion module is established based on NLP recognition technology to convert the abnormal medication event and / or the abnormal training event into a voice warning signal, and generate an abnormal reminder signal to provide voice feedback to the user.

8. The method according to claim 1, wherein Integrate and input the user's rehabilitation guidance information, including the following methods: Obtain multimodal information integration channels; Acquiring multimodal rehabilitation guidance information, inputting the multimodal rehabilitation guidance information into the multimodal information integration channel to perform rehabilitation task semantic extraction, and constructing a rehabilitation task metadata set; The rehabilitation task metadata set is divided to obtain rehabilitation medication information and rehabilitation training information.

9. The method according to claim 1, wherein Set up the drug holding management module, including: The user's current drug holding information is entered into the drug holding management module. When the user's rehabilitation medication is managed according to the first time-series rehabilitation medication management chain, if the user's drugs are insufficient, a drug shortage reminder signal is generated.

10. A rehabilitation management system for Parkinson's disease patients, characterized in that: The system is used to implement the rehabilitation management method for Parkinson's disease patients according to any one of claims 1 to 9, and the system comprises: A rehabilitation guidance information integration module is used to integrate and input the user's rehabilitation guidance information, wherein the rehabilitation guidance information includes rehabilitation medication information and rehabilitation training information, wherein the user is a Parkinson's disease patient; A time sequence processing module, configured to perform time sequence processing on the rehabilitation guidance information to obtain a first time sequence rehabilitation medication management chain and a first time sequence rehabilitation training management chain; a management chain processing module, configured to set a tolerable deviation interval, process the first sequential rehabilitation medication management chain and the first sequential rehabilitation training management chain according to the tolerable deviation interval, and output a second sequential rehabilitation medication management chain and a second sequential rehabilitation training management chain; A real-time data collection module is used to collect the user's real-time rehabilitation medication data and real-time rehabilitation training data; An abnormal event detection module is used to perform abnormal event detection on the real-time rehabilitation medication data and the real-time rehabilitation training data based on the second time-series rehabilitation medication management chain and the second time-series rehabilitation training management chain, and generate a corresponding abnormal reminder signal according to the abnormal event detection result to provide feedback to the user.

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