CN118039193A - 5G technology-based extra-hospital rehabilitation guidance and monitoring evaluation system - Google Patents
5G technology-based extra-hospital rehabilitation guidance and monitoring evaluation system Download PDFInfo
- Publication number
- CN118039193A CN118039193A CN202410444485.7A CN202410444485A CN118039193A CN 118039193 A CN118039193 A CN 118039193A CN 202410444485 A CN202410444485 A CN 202410444485A CN 118039193 A CN118039193 A CN 118039193A
- Authority
- CN
- China
- Prior art keywords
- patient
- data
- rehabilitation
- monitoring
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 85
- 238000005516 engineering process Methods 0.000 title claims abstract description 50
- 238000011156 evaluation Methods 0.000 title claims abstract description 22
- 238000012549 training Methods 0.000 claims abstract description 41
- 230000033001 locomotion Effects 0.000 claims description 38
- 238000012706 support-vector machine Methods 0.000 claims description 33
- 238000012545 processing Methods 0.000 claims description 27
- 238000000034 method Methods 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 15
- 238000007726 management method Methods 0.000 claims description 15
- 238000004891 communication Methods 0.000 claims description 14
- 230000010354 integration Effects 0.000 claims description 12
- 230000005540 biological transmission Effects 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 10
- 208000012661 Dyskinesia Diseases 0.000 claims description 9
- 230000036772 blood pressure Effects 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 238000010801 machine learning Methods 0.000 claims description 8
- 238000007405 data analysis Methods 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 7
- 238000012806 monitoring device Methods 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 7
- 238000011084 recovery Methods 0.000 claims description 7
- 238000013480 data collection Methods 0.000 claims description 6
- 238000013500 data storage Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 6
- 230000036541 health Effects 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
- 230000009286 beneficial effect Effects 0.000 claims description 5
- 239000008280 blood Substances 0.000 claims description 5
- 210000004369 blood Anatomy 0.000 claims description 5
- 230000000007 visual effect Effects 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 4
- 230000006872 improvement Effects 0.000 claims description 4
- 230000003993 interaction Effects 0.000 claims description 4
- 230000007774 longterm Effects 0.000 claims description 4
- 238000012986 modification Methods 0.000 claims description 4
- 230000004048 modification Effects 0.000 claims description 4
- 230000000474 nursing effect Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 238000010219 correlation analysis Methods 0.000 claims description 3
- 238000013079 data visualisation Methods 0.000 claims description 3
- 238000013503 de-identification Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims description 3
- 238000012217 deletion Methods 0.000 claims description 3
- 230000037430 deletion Effects 0.000 claims description 3
- 230000006866 deterioration Effects 0.000 claims description 3
- 230000006461 physiological response Effects 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000000513 principal component analysis Methods 0.000 claims description 3
- 238000009877 rendering Methods 0.000 claims description 3
- 238000000528 statistical test Methods 0.000 claims description 3
- 208000001613 Gambling Diseases 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 12
- 230000035876 healing Effects 0.000 abstract description 3
- 238000003745 diagnosis Methods 0.000 abstract description 2
- 230000009471 action Effects 0.000 description 10
- 230000005856 abnormality Effects 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 2
- 230000004899 motility Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 208000006011 Stroke Diseases 0.000 description 1
- 238000013474 audit trail Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000007654 immersion Methods 0.000 description 1
- 210000003205 muscle Anatomy 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 238000000554 physical therapy Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Landscapes
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention relates to an extra-hospital rehabilitation guidance and monitoring and evaluating technology, in particular to an extra-hospital rehabilitation guidance and monitoring and evaluating system based on a 5G technology. Because the patient lacks professional rehabilitation guidance at home, the rehabilitation effect of the patient is greatly reduced, and the healing of the patient is affected. The invention aims to provide an extra-hospital rehabilitation guidance and monitoring evaluation system based on a 5G technology. The system finally generates a real-time rehabilitation training file of the patient by combining doctor diagnosis information and patient basic information, and accurately transmits data to a remote monitoring center in real time through a 5G network, so that a doctor can evaluate the rehabilitation effect of the patient and make a further reasonable rehabilitation training plan.
Description
Technical Field
The invention relates to an extra-hospital rehabilitation guidance and monitoring and evaluating technology, in particular to an extra-hospital rehabilitation guidance and monitoring and evaluating system based on a 5G technology.
Background
In hospitals, stroke patients and disabled patients can well receive professional rehabilitation guidance and treatment. The rehabilitation exercise is a long-term process, and the patient still needs to continue rehabilitation after discharge, but the rehabilitation effect is closely related to the subjective motility of the patient, and the patient is greatly discounted due to lack of professional rehabilitation guidance at home, so that the disease healing of the patient is affected. The invention develops an extra-hospital rehabilitation guidance and monitoring evaluation system based on a 5G technology, and the system can remotely control or remotely control a nerve stimulator to stimulate a patient according to patient rehabilitation data or medical staff when the patient is at home, and can collect movement information of the patient through a sensor. In addition, the device is also provided with a display and a video camera, video guidance is carried out on patient rehabilitation, the video camera collects the movement characteristics of the patient, if the rehabilitation action is not in place, the device can prompt in time to correct the patient rehabilitation action.
The real-time rehabilitation training files of the patient can be finally generated by combining the motion characteristics acquired by the video images and the motion parameters extracted by the sensors with doctor diagnosis information and a patient basic information system, and the data are accurately transmitted to a remote monitoring center in real time through a 5G network, so that the record of the rehabilitation training data of the patient is realized, and a doctor can evaluate the rehabilitation effect of the patient conveniently and make a further reasonable rehabilitation training plan.
Disclosure of Invention
(1) Technical problem to be solved
The invention aims to overcome the defects that the rehabilitation effect is closely related to subjective motility of a patient in the prior art, and the rehabilitation effect of the patient is greatly reduced and the healing of the patient is affected due to lack of professional rehabilitation guidance at home, and the technical problem to be solved by the invention is to provide an extra-hospital rehabilitation guidance and monitoring and evaluating system based on a 5G technology.
(2) Technical proposal
In order to solve the technical problems, the invention provides an off-site rehabilitation guidance and monitoring and assessment system based on a 5G technology, which is characterized by comprising the following components: the system comprises a background server, a foreground terminal, monitoring equipment, rehabilitation equipment and VR equipment;
The background server is used for processing, storing, analyzing, managing and logically controlling medical data; the foreground terminal displays the data processed by the background server; the monitoring equipment is used for monitoring patient data in real time and sending the data to the background server; the rehabilitation equipment is used for remote control and rehabilitation treatment of a patient; VR equipment is used for assisting the patient to carry out rehabilitation training.
Further, the monitoring device comprises a wearable device and a camera, wherein the wearable device comprises a plurality of types, such as a smart watch and a health monitoring belt, and is used for collecting physiological parameters of a patient, such as heart rate, blood pressure and blood sugar, in real time; the camera includes a plurality of cameras for collecting user rehabilitation exercise video data from a plurality of angles.
Further, the background server comprises a data collection and storage module, a data processing and analysis module, a real-time monitoring and feedback module, a user management module, a service logic control module, an interface integration module and a privacy and security module;
the data collection and storage module is responsible for communicating with the monitoring equipment, receiving the uploaded data, and storing, backing up and recovering the data;
The data processing and analyzing module is responsible for preprocessing data, extracting features from the original data, analyzing the data by using statistics and a machine learning algorithm, and generating a rehabilitation progress report from an analysis result;
The real-time monitoring and feedback module is responsible for monitoring the body data of the patient after the pretreatment of the data processing and analyzing module in real time, and triggering an alarm when an abnormal situation exists;
the user management module is responsible for creating, modifying and deleting user accounts and managing access rights of different users;
The service logic control module is responsible for processing various requests of the foreground terminal and completing interaction between the system and the user;
The interface integration module is responsible for data exchange and function integration with other medical systems and provides an API interface accessed by the other medical systems;
The privacy and security module is responsible for ensuring the security of data transmission and storage, limiting access to sensitive data, protecting the system from network attacks and limiting unauthorized data access.
Further, the specific operation steps of the data processing and analyzing module are as follows:
s1, data are cleaned, the data format and abnormal value of errors in the data are corrected, whether the data are input errors or the data are repeated is determined for repeated recording, the operation of re-inputting or deleting is adopted, and different measurement standards of the data are converted into a standard measurement;
s2, extracting characteristic values, and identifying the most important characteristics for predicting patient data through statistical tests, principal component analysis and correlation analysis;
S3, analyzing data, namely, using different machine learning algorithms and deep learning algorithms to recover data of a patient, wherein the data comprise various physiological data of the patient monitored by using monitoring equipment, shooting recovery motions of the patient by using a camera, and performing deep prediction analysis on the data to obtain a patient health prediction result, and evaluating the motion accuracy, stability and motion range of the patient to ensure that the patient correctly executes recovery motions; analyzing various physiological data of the patient, and predicting the rehabilitation state of the patient and physiological changes in the rehabilitation process; setting feasible short-term and long-term targets for the patient according to the rehabilitation history and the current state of the patient; according to the reaction and progress of the patient, automatically adjusting a rehabilitation plan to ensure that the patient obtains the most suitable rehabilitation guidance;
Predicting the rehabilitation state of the patient by using a Support Vector Machine (SVM), constructing an optimization problem according to various physiological data of the patient monitored by monitoring equipment, and finding out a hyperplane with maximized class intervals, wherein the hyperplane can best distinguish different rehabilitation states, and the rehabilitation states are divided into several classes including improvement, no change and deterioration; the optimization problem of the support vector machine SVM model construction is as follows:
Is limited by/>
Wherein,The method is a hyperplane normal vector which is searched by a support vector machine model and is used for determining the importance of different physiological data characteristics of a patient, such as heart rate, blood pressure and blood sugar, in a prediction model; Is the norm of the normal vector, half of the square of which is the objective function to be minimized, a smaller normal vector norm in physiological data analysis means that smoother predictions can be made of the patient's state of recovery; Is a bias item of a hyperplane, and is beneficial to adjusting a judging threshold value of the SVM model so as to better classify the rehabilitation state; Is the first The feature vector of each patient training sample consists of physiological data of the patient, including heart rate, blood pressure and movement range of the patient; Is the first Labels of individual patient training samples representing the rehabilitation status of the patient; is a relaxation variable, meaning that some patient data points, even if not meeting preset classification criteria, can be tolerated by the model; Is a regularization parameter which allows the Support Vector Machine (SVM) model to tolerate misclassification to some extent, helping to avoid overfitting; Is the total number of training samples, representing the size of the patient data set used to train the model; when new physiological data of a patient is input into the support vector machine SVM model, the specific rehabilitation state of the patient is classified by judging the positions of the data points and the hyperplane;
S4, creating an intuitive chart by using a graphic tool to help explain the result of data analysis, and then generating a clear report or summary and a personalized rehabilitation plan of a patient according to the data visualization and the analysis result for medical staff and the patient to check;
and S5, feeding back, and carrying out parameter adjustment on the model and the algorithm according to the model performance and the user feedback.
Further, the real-time monitoring and feedback module is used for customizing an abnormal movement early warning threshold value x for the patient according to medical knowledge of medical staff and specific illness state and rehabilitation progress of the patient, analyzing and predicting movement data of the patient, including heart rate, abnormal movement quantity and falling, of the patient, the real-time monitoring and feedback module is used for obtaining prediction data and monitoring the data, judging whether the data exceed the threshold value x, judging that the patient is performing abnormal movement after the data exceed the threshold value x, and the real-time monitoring and feedback module can autonomously initiate continuous early warning to a foreground terminal through a background server until a monitoring value of the movement data of the patient returns to a normal value.
Further, the privacy and security module ensures that the system uses an end-to-end encryption communication mode when the system performs data communication, and ensures the security of data in the transmission process; meanwhile, when the data processing and analyzing module analyzes the patient data, anonymization or de-identification technology is adopted to protect personal identity information of the patient, so that the data is ensured to be processed and analyzed under the condition of no personal identity information; fine-granularity access control is realized, and the user can only access the data in the authority range; while the privacy and security module records all accesses and operations to the data, including reads, modifications, and deletions, for auditing, if necessary.
Further, the foreground terminal is divided into a doctor terminal, a nursing staff terminal and a patient terminal according to user rights set by user management in the background server, communication requests are sent between medical staff and patients through the respective foreground terminals, a service logic control module of the background server analyzes the communication requests, the medical staff and the foreground terminals of the patients are connected through a 5G technology, so that real-time communication between doctors and patients is realized, and the medical staff guides the patients to perform rehabilitation training on line; the foreground terminal performs visual display on the data, the rehabilitation training program and the rehabilitation file processed and analyzed by the background server, displays the data in a line diagram and a table mode, and optimizes through lazy loading, data caching and front-end rendering, so that waiting time of a user is reduced, and user experience is improved.
Further, a game element is added into the patient terminal, a special medical rehabilitation small game is designed, the game is connected with a patient rehabilitation task, and the patient unlocks the achievement and obtains rewards by completing the rehabilitation task; combining the VR equipment with a motion capture technology, and connecting the VR equipment with a patient terminal so that the patient can perform rehabilitation training in a virtual game environment; the off-hospital rehabilitation guidance and monitoring and evaluation system tracks the movement and physiological response of the patient in real time through monitoring equipment, and adjusts the scene and difficulty in the virtual game environment; the patient can share the progress and experience of the rehabilitation game in the rehabilitation game to the patient community through the patient terminal.
Further, after the background server processes and analyzes the data monitored by the monitoring device, the rehabilitation device judges whether the patient needs to be stimulated and recovered by the rehabilitation device or whether the medical staff gives an operation command at the foreground terminal, and the rehabilitation device is remotely controlled to stimulate and recover the patient through the 5G technology.
Further, the 5G technology-based off-hospital rehabilitation guidance and monitoring and assessment system integrates medical data, rehabilitation records and training plans of patients, and doctors can monitor rehabilitation progress of the patients at any time and adjust the rehabilitation plans as required; meanwhile, the system can automatically adjust the rehabilitation training difficulty and frequency according to the rehabilitation progress of the patient.
(3) Advantageous effects
The 5G internet technology is utilized to develop a hospital and family service mode, the deep fusion of the internet and the in-hospital and out-of-hospital health management of a rehabilitation patient is realized, a doctor is convenient to evaluate the rehabilitation effect of the patient, whether rehabilitation training is effective on the patient is evaluated, a reasonable rehabilitation training plan is further formulated, and meanwhile the back and forth running of the rehabilitation patient to the hospital every week is also solved.
The invention uses the 5G technology to carry out data transmission, improves the efficiency of data transmission, and provides possibility for the patient to provide real-time monitoring and rehabilitation guidance outside the hospital.
According to the invention, the rehabilitation exercise data of the patient is monitored in real time, and the machine learning algorithm and the statistical knowledge are used for analysis and prediction, and meanwhile, in order to predict the accuracy of analysis, the machine learning algorithm and the statistical knowledge are continuously optimized, and an alarm is sent when the rehabilitation exercise of the patient is analyzed and predicted to be abnormal, so that the practicability of the invention is increased.
Drawings
FIG. 1 is a schematic diagram of the modules of the present invention.
Fig. 2 is a schematic diagram of each module in the background server according to the present invention.
FIG. 3 is a schematic diagram of a specific flow chart of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples.
The invention is used as an extra-hospital rehabilitation guidance and monitoring evaluation system based on a 5G technology, a rehabilitation system workstation is firstly established in a hospital rehabilitation department, a worker starts the rehabilitation workstation and logs in a background server of the extra-hospital rehabilitation guidance and monitoring evaluation system, and the server enables medical staff to be connected with a foreground terminal of a patient through the 5G technology. The patient only needs to open recovered APP at home, starts monitoring camera through recovered APP to be connected to the sensor and can carry out recovered motion. When a patient moves in rehabilitation, a sensor and a camera carried on the patient send data to a background server of a rehabilitation workstation in real time through a 5G technology, the background server carries out real-time processing analysis on the rehabilitation action to judge whether the rehabilitation action of the patient is correct or whether the patient has an unexpected condition in the movement process, and the background server can send information to foreground terminals of the patient and a doctor of the patient; when the user only has the wrong rehabilitation action, the background server can send a warning of the wrong rehabilitation action, meanwhile, the foreground terminal of the patient receives the next rehabilitation action request transmitted by the background server, and after receiving the warning, a therapist can conduct video guidance with the patient through the foreground terminal so as to further correct the rehabilitation action of the patient; if the patient body has an accident, a warning that the patient body is abnormal is sent out, the background server sends a warning message for stopping all activities to the patient foreground terminal and guides the patient how to carry out emergency treatment, and after receiving the warning, a therapist takes measures according to the regulation system. Wherein, the warning message of the accident situation of the patient body is prioritized over the warning message of whether the abnormal rehabilitation action of the patient occurs. The extra-hospital rehabilitation guidance and monitoring evaluation system based on the 5G technology is used for preferentially processing the accidents of the patient in the movement process by identifying the accidents, so that more efficient and safer rehabilitation service is provided for the patient; and the patient can start rehabilitation exercise through rehabilitation APP connection monitoring devices, makes things convenient for the patient to operate.
The background server records the rehabilitation data acquired by the sensor and the monitoring camera and the general condition of the patient, and a therapist combines the rehabilitation data with the patient condition record according to the patient condition record and makes a next rehabilitation training plan for the patient according to medical knowledge. Medical staff monitors the rehabilitation actions of the patient in real time through the background server and makes and adjusts personalized rehabilitation training plans for the patient, so that the efficiency and quality of medical service are enhanced.
In order to realize the flow, the 5G technology-based off-hospital rehabilitation guidance and monitoring evaluation system comprises a background server, a foreground terminal, monitoring equipment and rehabilitation equipment; the background server is used for processing, storing and analyzing medical data, managing users and logically controlling the medical data and comprises a data collecting and storing module, a data processing and analyzing module, a real-time monitoring and feedback module, a user management module, a service logic control module, an interface integration module and a privacy and safety module; the foreground terminal displays the data processed by the data; the monitoring equipment is used for monitoring patient data in real time and sending the data to the background server; the rehabilitation equipment is used for remote control and rehabilitation treatment of patients. The 5G technology-based off-hospital rehabilitation guidance and monitoring evaluation system provides more efficient and convenient medical services for patients through the combination of the background server, the foreground terminal, the monitoring equipment and the rehabilitation equipment, and provides more personalized and accurate rehabilitation guidance for the patients through powerful and efficient data transmission and data processing and analysis capabilities.
The data collection and storage module in the background server is responsible for communication with the monitoring equipment, receiving the uploaded data, and storing, backing up and recovering the data; the rehabilitation data of the patient are acquired in real time by using various sensors, cameras and other medical monitoring equipment to carry out communication connection by using a 5G technology; or the interface integrated module is connected with other medical management systems in an interface way to acquire patient information in the other medical management systems; when patient data is collected, the patient data is stored at the same time, wherein a storage database adopts a distributed storage technology, patient rehabilitation information is divided into a plurality of nodes to be stored, a real-time data synchronization and backup function is realized, and the data can not be lost and can be quickly recovered under the condition that a certain storage node fails; meanwhile, an expandable storage architecture is adopted, so that the system can seamlessly expand the storage capacity along with the increase of the number of patients and the increase of rehabilitation data, and the use of the system is not affected; meanwhile, when the data collection and storage module collects data, an edge computing technology is adopted, so that the data is subjected to preliminary processing and storage on edge equipment of a data source, the data transmission time is shortened, the response speed of the system is improved, and meanwhile, the data quantity of patients needing to be transmitted to a background server is reduced by the edge computing technology, and the processing pressure of the background server is further reduced. The storage architecture design adopted by the extrahospital rehabilitation guidance and monitoring and evaluation system can enable the system to flexibly expand along with the increase of patient data, so that future requirements are met.
The data processing and analyzing module in the background server is responsible for preprocessing data, extracting features from the original data, analyzing the data by using statistics and a machine learning algorithm, and generating a rehabilitation progress report from an analysis result. Firstly, data cleaning is carried out on monitored patient data, the wrong data format and abnormal value in the data are corrected, for repeated recording, whether the data are recorded in error or the data are recorded repeatedly or not is determined, corresponding operations such as re-recording or deleting are adopted, different measurement standards of the data are converted into a standard measurement, and the data are scaled to be within the range of one standard, such as a [0,1] interval. And extracting characteristic values, and identifying the most important characteristics for predicting the patient data through methods such as statistical test, principal component analysis, correlation analysis and the like. Then, analyzing data, namely, using different machine learning algorithms and deep learning algorithms to carry out rehabilitation data of the patient, wherein the rehabilitation data comprise various physiological data of the patient monitored by using monitoring equipment, shooting rehabilitation motions made by the patient by using a camera, carrying out deep prediction analysis on the data to obtain a patient health prediction result, evaluating the motion accuracy, stability and motion range of the patient, and ensuring that the patient correctly carries out rehabilitation motions; analyzing various physiological data of the patient, and predicting the rehabilitation state of the patient and physiological changes in the rehabilitation process; setting feasible short-term and long-term targets for the patient according to the rehabilitation history and the current state of the patient; according to the reaction and progress of the patient, automatically adjusting a rehabilitation plan to ensure that the patient obtains the most suitable rehabilitation guidance;
Predicting the rehabilitation state of the patient by using a Support Vector Machine (SVM), constructing an optimization problem according to various physiological data of the patient monitored by monitoring equipment, wherein the Support Vector Machine (SVM) model is used for finding out a hyperplane with maximized class interval, the hyperplane can be used for distinguishing different rehabilitation states, the rehabilitation state is divided into several classes including improvement, no change and deterioration, and the optimization problem of the Support Vector Machine (SVM) model construction is as follows:
Is limited by/>
Wherein,The method is a hyperplane normal vector which is searched by a support vector machine model and is used for determining the importance of different physiological data characteristics of a patient, such as heart rate and blood pressure, in prediction modularity; Is the norm of the normal vector, half of the square of which is the objective function to be minimized, a smaller normal vector norm in physiological data analysis means that smoother predictions of the patient's state of recovery are possible; Is a bias item of a hyperplane, and is beneficial to adjusting a judging threshold value of the SVM model so as to better classify the rehabilitation state; Is the first The feature vector of each patient training sample consists of physiological data of the patient, including heart rate, blood pressure, blood sugar and movement range of the patient; Is the first Labels of individual patient training samples representing the rehabilitation status of the patient; Is a relaxation variable, meaning that some patient data points can tolerate such deviations even if they do not meet preset classification criteria; Is a regularization parameter which allows the Support Vector Machine (SVM) model to tolerate misclassification to some extent, helping to avoid overfitting; Is the total number of training samples, representing the size of the patient data set used to train the model; when new physiological data of the patient is input into the support vector machine SVM model, the specific rehabilitation state of the patient is classified by judging the positions of the data points and the hyperplane. And creating an intuitive chart by using a graphic tool to help explain the data analysis result, generating a clear report or summary and a patient personalized rehabilitation plan according to the data visualization and analysis result, allowing medical staff and patients to check, and finally, performing parameter adjustment on the model and algorithm by the staff according to the model performance and the user feedback to ensure the accuracy and applicability of the SVM model.
In a real-time monitoring and feedback module in a background server, medical staff can customize an abnormal movement early warning threshold value x for a patient according to medical knowledge and specific illness and rehabilitation progress of the patient, the threshold value is dynamic, the abnormal movement early warning threshold value x can be adjusted along with the change of the rehabilitation condition of the patient, analysis and prediction are carried out on movement data of the patient, such as heart rate, movement intensity, movement mode and the like, in a data processing and analyzing module, whether potential abnormality exists or not is predicted, such as heart rate abnormality, movement quantity abnormality or falling risk and the like, the real-time monitoring and feedback module monitors the data and compares the data with a preset threshold value x to judge whether the data exceeds the threshold value x, after the data exceeds the threshold value x, the real-time monitoring and feedback module judges that the patient is in abnormal movement, the real-time monitoring and feedback module can autonomously initiate early warning signals to a background terminal through the background server, the early warning signals can be sound, vibration or visual prompt and the like, the adoption of the method is carried out through the real-time monitoring and feedback module by judging the current environment of the patient, and aims at immediately causing attention of the patient or surrounding people, the early warning can be continuously carried out until the patient movement data value returns to the early warning value. During the continuous pre-warning, the real-time monitoring and feedback module may provide immediate intervention advice, such as prompting the patient to slow down movement, stop current activity, or take certain safety measures. The off-hospital rehabilitation guidance and monitoring evaluation system provides more accurate monitoring and early warning by dynamically adjusting the early warning threshold according to the actual rehabilitation situation of the patient instead of the fixed early warning threshold in the traditional medical system; meanwhile, the system is beneficial to ensuring that patients can obtain early warning information in time under different situations by adopting various early warning modes such as sound, vibration, visual prompt and the like.
The user management module in the background server is responsible for creating, modifying and deleting user accounts, and the user management module realizes authority control based on the roles of the users, and defines different authority sets for users with different roles, such as management personnel, doctors, nursing personnel, patients and the like.
The service logic control module in the background server is responsible for processing various requests of the foreground terminal, completing interaction between the system and the user, combining the high-bandwidth advantage of the 5G technology, and can support high-definition video call, so that medical staff can conduct remote online real-time guidance.
The interface integration module in the background server is responsible for data exchange and function integration with other medical systems and provides API interfaces accessed by other medical systems, and meanwhile, the interface integration module adopts an event-driven architecture design, so that an off-site rehabilitation guidance and monitoring evaluation system based on a 5G technology can respond to key events in the rehabilitation process more quickly and push the information to related medical systems and personnel in time. The traditional medical system updates information in a polling or timing checking mode, the event-driven architecture provides a very sensitive event response mechanism, information updating delay is reduced, and meanwhile, the interface integration module realizes smoother and wider medical information integration, can improve the quality and efficiency of rehabilitation service and promote the optimal configuration of medical resources.
The privacy and security module in the background server ensures that the system uses an end-to-end encryption communication mode when the system performs data communication, and ensures the security of data in the transmission process; meanwhile, when the data processing and analyzing module analyzes the patient data, anonymization or de-identification technology is adopted to protect personal identity information of the patient, so that the data is ensured to be processed and analyzed under the condition of no personal identity information; fine-granularity access control is realized, and the user can only access the data in the authority range; while the privacy and security module records all accesses and operations to the data, including reads, modifications, and deletions, for auditing, if necessary. The extra-hospital rehabilitation guidance and monitoring evaluation system combines the high-speed transmission capability of the 5G technology by using end-to-end encryption, so that the performance of the system is not sacrificed while the data safety is ensured; meanwhile, the system not only pays attention to the security of data transmission, but also covers the security management of multiple layers such as data processing, access control, audit trail and the like. Providing a more comprehensive security policy.
The foreground terminal displays the data in the background server and realizes dialogue connection between the foreground terminals through the background server; the foreground terminal is divided into different terminals according to different role authorities set by a user management module in the background server for each user, wherein the different terminals comprise a doctor terminal, a nursing staff terminal and a patient terminal; the user can operate on the terminal, and the background server responds to the operation instruction on the terminal, for example, the doctor terminal and the patient terminal can carry out video call under the logic control of the service logic control module, so that real-time online rehabilitation guidance is realized. The terminal performs visual display on the data, the rehabilitation training program and the rehabilitation file processed and analyzed by the background server, displays the data in a line diagram and table mode, and reduces waiting time of a user and improves user experience through lazy loading, data caching and front-end rendering optimization. The off-hospital rehabilitation guidance and monitoring evaluation system enhances personalized service and data safety by providing more subdivided role authority setting, and meanwhile doctors and patients can communicate in real time through videos realized by a 5G technology, thereby being beneficial to providing real-time medical advice and rehabilitation guidance and enhancing interactivity and effectiveness of remote medical services.
Meanwhile, in order to improve the enthusiasm and rehabilitation efficiency of the rehabilitation training of the patient, game elements are added in the rehabilitation training, and the achievement is unlocked by completing a specific rehabilitation task to obtain rewards, so that the patient is stimulated to adhere to the training; meanwhile, a rehabilitation training game is developed, a patient can be connected with a VR device through a patient terminal, the VR device constructs the rehabilitation training game into a virtual game environment, then the patient can perform rehabilitation training in the virtual game environment through the VR device and a motion capturing technology, and meanwhile, an out-of-hospital rehabilitation guidance and detection evaluation system monitors physiological parameters and physiological responses of the patient such as heart rate, blood pressure and muscle activity in real time through a real-time monitoring device and a 5G technology, so that the virtual game scene and difficulty are adjusted for the patient, the patient is ensured to train in a proper intensity range all the time, and the condition that the training is excessive or insufficient is avoided; the patient can design a personalized virtual scene according to the interests and rehabilitation needs of the patient, for example, the patient who likes the ocean can perform rehabilitation training in the seabed world, the patient who likes the sky can perform rehabilitation training in the virtual sky, and the patient who likes the mountain climbing can be arranged in the virtual mountain to exercise and the like; meanwhile, the patient can share the rehabilitation game progress and the rehabilitation game experience in the rehabilitation game to the patient community through the patient terminal, or share the rehabilitation experience in the patient community. Rehabilitation training in the prior art is focused on traditional physical therapy and training methods, and the external rehabilitation guidance and monitoring evaluation system excites patients through game elements and patient communities, so that the participation degree and rehabilitation effect of the patients are improved, and the combination of VR equipment and motion capture technology provides rehabilitation experience of stronger immersion and interaction of the patients.
The rehabilitation device processes and analyzes the data monitored by the monitoring device, then judges whether the patient needs the rehabilitation device to carry out auxiliary rehabilitation, if yes, and if yes, the background server operates the rehabilitation device to carry out rehabilitation training on the patient according to a set rehabilitation device use mechanism; or medical staff gives an operation command for operating the rehabilitation equipment at the foreground terminal, and the rehabilitation equipment is remotely controlled by the background server to stimulate and recover the patient by using the 5G technology. The rehabilitation state of the patient is monitored remotely by using the 5G technology, and the rehabilitation equipment can be controlled, so that the rehabilitation service is more flexible and convenient, and the rehabilitation device is particularly suitable for the outside-hospital environment. Meanwhile, the extra-hospital rehabilitation guidance and monitoring evaluation system adjusts the rehabilitation plan according to the real-time monitoring data instead of adopting a general rehabilitation scheme, so that individuation and adaptability of rehabilitation training are improved.
The foregoing examples have shown only the preferred embodiments of the invention, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that modifications, improvements and substitutions can be made by those skilled in the art without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (8)
1. An extra-hospital rehabilitation guidance and monitoring evaluation system based on 5G technology, comprising: the system comprises a background server, a foreground terminal, monitoring equipment, rehabilitation equipment and VR equipment;
The background server is used for processing, storing, analyzing, managing and logically controlling medical data; the foreground terminal displays the data processed by the background server; the monitoring equipment is used for monitoring patient data in real time and sending the data to the background server; the rehabilitation equipment is used for remote control and rehabilitation treatment of a patient; the VR equipment is used for assisting a patient in rehabilitation training;
The background server comprises a data collection and storage module, a data processing and analysis module, a real-time monitoring and feedback module, a user management module, a service logic control module, an interface integration module and a privacy and security module;
the data collection and storage module is responsible for communicating with the monitoring equipment, receiving the uploaded data, and storing, backing up and recovering the data;
The data processing and analyzing module is responsible for preprocessing data, extracting features from the original data, analyzing the data by using statistics and a machine learning algorithm, and generating a rehabilitation progress report from an analysis result;
the specific operation steps of the data processing and analyzing module are as follows:
s1, data are cleaned, the data format and abnormal value of errors in the data are corrected, whether the data are input errors or the data are repeated is determined for repeated recording, the operation of re-inputting or deleting is adopted, and different measurement standards of the data are converted into a standard measurement;
s2, extracting characteristic values, and identifying the most important characteristics for predicting patient data through statistical tests, principal component analysis and correlation analysis;
S3, analyzing data, namely, using different machine learning algorithms and deep learning algorithms to recover data of a patient, wherein the data comprise various physiological data of the patient monitored by using monitoring equipment, shooting recovery motions of the patient by using a camera, and performing deep prediction analysis on the data to obtain a patient health prediction result, and evaluating the motion accuracy, stability and motion range of the patient to ensure that the patient correctly executes recovery motions; analyzing various physiological data of the patient, and predicting the rehabilitation state of the patient and physiological changes in the rehabilitation process; setting feasible short-term and long-term targets for the patient according to the rehabilitation history and the current state of the patient; according to the reaction and progress of the patient, automatically adjusting a rehabilitation plan to ensure that the patient obtains the most suitable rehabilitation guidance;
Predicting the rehabilitation state of the patient by using a Support Vector Machine (SVM), constructing an optimization problem according to various physiological data of the patient monitored by monitoring equipment, and finding out a hyperplane with maximized class intervals, wherein the hyperplane can best distinguish different rehabilitation states, and the rehabilitation states are divided into several classes including improvement, no change and deterioration; the optimization problem of the support vector machine SVM model construction is as follows:
Is limited by
Wherein,The method is a hyperplane normal vector which is searched by a support vector machine model and is used for determining the importance of different physiological data characteristics of a patient, such as heart rate, blood pressure and blood sugar, in a prediction model; Is the norm of the normal vector, half of the square of which is the objective function to be minimized, a smaller normal vector norm in physiological data analysis means that smoother predictions can be made of the patient's state of recovery; Is a bias item of a hyperplane, and is beneficial to adjusting a judging threshold value of the SVM model so as to better classify the rehabilitation state; Is the first The feature vector of each patient training sample consists of physiological data of the patient, including heart rate, blood pressure and movement range of the patient; Is the first Labels of individual patient training samples representing the rehabilitation status of the patient; is a relaxation variable, meaning that some patient data points, even if not meeting preset classification criteria, can be tolerated by the model; Is a regularization parameter which allows the Support Vector Machine (SVM) model to tolerate misclassification to some extent, helping to avoid overfitting; Is the total number of training samples, representing the size of the patient data set used to train the model; when new physiological data of a patient is input into the support vector machine SVM model, the specific rehabilitation state of the patient is classified by judging the positions of the data points and the hyperplane;
S4, creating an intuitive chart by using a graphic tool to help explain the result of data analysis, and then generating a clear report or summary and a personalized rehabilitation plan of a patient according to the data visualization and the analysis result for medical staff and the patient to check;
s5, feeding back, and carrying out parameter adjustment on the model and the algorithm according to the model performance and the user feedback;
The real-time monitoring and feedback module is responsible for monitoring the body data of the patient after the pretreatment of the data processing and analyzing module in real time, and triggering an alarm when an abnormal situation exists;
the user management module is responsible for creating, modifying and deleting user accounts and managing access rights of different users;
The service logic control module is responsible for processing various requests of the foreground terminal and completing interaction between the system and the user;
The interface integration module is responsible for data exchange and function integration with other medical systems and provides an API interface accessed by the other medical systems;
The privacy and security module is responsible for ensuring the security of data transmission and storage, limiting access to sensitive data, protecting the system from network attacks and limiting unauthorized data access.
2. The 5G technology-based extra-hospital rehabilitation guidance and monitoring and assessment system according to claim 1, wherein the monitoring device comprises a wearable device and a camera, and the wearable device comprises a plurality of types, such as a smart watch and a health monitoring belt, and is used for collecting physiological parameters of a patient, such as heart rate, blood pressure and blood sugar, in real time; the camera includes a plurality of cameras for collecting user rehabilitation exercise video data from a plurality of angles.
3. The 5G technology-based extra-hospital rehabilitation guidance and monitoring evaluation system according to claim 1 is characterized in that the real-time monitoring and feedback module customizes an abnormal movement early warning threshold value x for a patient according to medical knowledge of medical staff and specific illness state and rehabilitation progress of the patient, the data processing and analyzing module analyzes and predicts movement data of the patient, including heart rate, abnormal movement quantity and falling, the real-time monitoring and feedback module obtains prediction data and monitors the data, judges whether the data exceeds the threshold value x, and judges that the patient is performing abnormal movement after the data exceeds the threshold value x, the real-time monitoring and feedback module automatically initiates continuous early warning to a front platform terminal through a background server until a patient movement data monitoring value returns to a normal value.
4. The 5G technology-based extra-hospital rehabilitation guidance and monitoring and assessment system according to claim 1, wherein the privacy and security module ensures the end-to-end encryption communication mode of the system when the system performs data communication, and ensures the security of the data in the transmission process; meanwhile, when the data processing and analyzing module analyzes the patient data, anonymization or de-identification technology is adopted to protect personal identity information of the patient, so that the data is ensured to be processed and analyzed under the condition of no personal identity information; fine-granularity access control is realized, and the user can only access the data in the authority range; while the privacy and security module records all accesses and operations to the data, including reads, modifications, and deletions, for auditing, if necessary.
5. The 5G technology-based extra-hospital rehabilitation guidance and monitoring evaluation system is characterized in that the foreground terminal is divided into a doctor terminal, a nursing staff terminal and a patient terminal according to user authority set by user management in a background server, communication requests are sent between medical staff and patients through the respective foreground terminals, a service logic control module of the background server analyzes the communication requests, and the medical staff and the patient foreground terminals are connected through the 5G technology, so that real-time communication between doctors and patients is realized, and the medical staff guides the patients to perform rehabilitation training on line; the foreground terminal performs visual display on the data, the rehabilitation training program and the rehabilitation file processed and analyzed by the background server, displays the data in a line diagram and a table mode, and optimizes through lazy loading, data caching and front-end rendering, so that waiting time of a user is reduced, and user experience is improved.
6. The 5G technology-based extra-hospital rehabilitation guidance and monitoring and assessment system according to claim 5, wherein the patient terminal is added with a gambling element and designs a special medical rehabilitation mini-game, the game is connected with the patient rehabilitation task, and the patient unlocks the achievement and obtains rewards by completing the rehabilitation task; combining the VR equipment with a motion capture technology, and enabling a patient to perform rehabilitation training in a virtual game environment by connecting the VR equipment with a patient terminal; the off-hospital rehabilitation guidance and monitoring and evaluation system tracks the movement and physiological response of the patient in real time through monitoring equipment, and adjusts the scene and difficulty in the virtual game environment; the patient can share the progress and experience of the rehabilitation game in the rehabilitation game to the patient community through the patient terminal.
7. The 5G technology-based extra-hospital rehabilitation guidance and monitoring and assessment system according to claim 1, wherein after the background server processes and analyzes the data monitored by the monitoring device, the rehabilitation device judges whether the patient needs to be stimulated and recovered by the rehabilitation device or if the medical staff issues an operation command at the foreground terminal, and the rehabilitation device is remotely controlled to stimulate and recover the patient through the 5G technology.
8. The 5G technology-based extra-hospital rehabilitation guidance and monitoring and assessment system according to claim 1, wherein the 5G technology-based extra-hospital rehabilitation guidance and monitoring and assessment system integrates the patient's medical data, rehabilitation records and training plans, and the doctor can monitor the patient's rehabilitation progress at any time and adjust the rehabilitation plans as required; meanwhile, the system can automatically adjust the rehabilitation training difficulty and frequency according to the rehabilitation progress of the patient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410444485.7A CN118039193B (en) | 2024-04-15 | 2024-04-15 | 5G technology-based extra-hospital rehabilitation guidance and monitoring evaluation system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410444485.7A CN118039193B (en) | 2024-04-15 | 2024-04-15 | 5G technology-based extra-hospital rehabilitation guidance and monitoring evaluation system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118039193A true CN118039193A (en) | 2024-05-14 |
| CN118039193B CN118039193B (en) | 2024-06-21 |
Family
ID=90993672
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410444485.7A Active CN118039193B (en) | 2024-04-15 | 2024-04-15 | 5G technology-based extra-hospital rehabilitation guidance and monitoring evaluation system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118039193B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102567638A (en) * | 2011-12-29 | 2012-07-11 | 无锡微感科技有限公司 | Interactive upper limb rehabilitation system based on micro-sensor |
| CN106127646A (en) * | 2016-07-15 | 2016-11-16 | 佛山科学技术学院 | The monitoring system of a kind of recovery period data and monitoring method |
| CN110353695A (en) * | 2019-07-19 | 2019-10-22 | 湖南工程学院 | A kind of wearable athletic rehabilitation guidance and monitor system and its method |
| CN110931103A (en) * | 2019-11-01 | 2020-03-27 | 深圳市迈步机器人科技有限公司 | Control method and system of rehabilitation equipment |
| CN111105860A (en) * | 2019-12-18 | 2020-05-05 | 青岛科技大学 | Intelligent prediction, analysis and optimization system for accurate motion big data for chronic disease rehabilitation |
| CN112735610A (en) * | 2021-02-05 | 2021-04-30 | 唐斌 | Post-hospital follow-up system and follow-up method |
| CN117558404A (en) * | 2023-11-30 | 2024-02-13 | 中国医学科学院北京协和医院 | Outside hospital medical care integrated interactive motion system |
| CN117594245A (en) * | 2024-01-18 | 2024-02-23 | 凝动万生医疗科技(武汉)有限公司 | Orthopedic patient rehabilitation process tracking method and system |
| CN117831794A (en) * | 2024-03-06 | 2024-04-05 | 南通大学 | Exercise process function monitoring method for diabetes rehabilitation |
-
2024
- 2024-04-15 CN CN202410444485.7A patent/CN118039193B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102567638A (en) * | 2011-12-29 | 2012-07-11 | 无锡微感科技有限公司 | Interactive upper limb rehabilitation system based on micro-sensor |
| CN106127646A (en) * | 2016-07-15 | 2016-11-16 | 佛山科学技术学院 | The monitoring system of a kind of recovery period data and monitoring method |
| CN110353695A (en) * | 2019-07-19 | 2019-10-22 | 湖南工程学院 | A kind of wearable athletic rehabilitation guidance and monitor system and its method |
| CN110931103A (en) * | 2019-11-01 | 2020-03-27 | 深圳市迈步机器人科技有限公司 | Control method and system of rehabilitation equipment |
| CN111105860A (en) * | 2019-12-18 | 2020-05-05 | 青岛科技大学 | Intelligent prediction, analysis and optimization system for accurate motion big data for chronic disease rehabilitation |
| CN112735610A (en) * | 2021-02-05 | 2021-04-30 | 唐斌 | Post-hospital follow-up system and follow-up method |
| CN117558404A (en) * | 2023-11-30 | 2024-02-13 | 中国医学科学院北京协和医院 | Outside hospital medical care integrated interactive motion system |
| CN117594245A (en) * | 2024-01-18 | 2024-02-23 | 凝动万生医疗科技(武汉)有限公司 | Orthopedic patient rehabilitation process tracking method and system |
| CN117831794A (en) * | 2024-03-06 | 2024-04-05 | 南通大学 | Exercise process function monitoring method for diabetes rehabilitation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118039193B (en) | 2024-06-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Feuz et al. | Automated detection of activity transitions for prompting | |
| DE60034965T2 (en) | DYNAMIC FIRE-WIDE MONITORING AND ADJUSTMENT FOR REMOTE COMMUNICATION WITH A MEDICAL DEVICE | |
| Zhang et al. | HONEY: a multimodality fall detection and telecare system | |
| CN110047575B (en) | Feedback type sleep-aiding system based on remote decision | |
| CN100559382C (en) | Emergent medical rescuing wireless mobile information systems and method for interchanging data | |
| CN117238452B (en) | Regional medical image cloud and inspection result mutual recognition sharing platform | |
| CN111227789A (en) | Human health monitoring method and device | |
| CN106663140A (en) | Device, system and method for detecting a health condition of a subject | |
| CN110755091A (en) | Personal mental health monitoring system and method | |
| CN118039193B (en) | 5G technology-based extra-hospital rehabilitation guidance and monitoring evaluation system | |
| Jersak et al. | A systematic review on mobile health care | |
| Khovrichev et al. | Intelligent approach for heterogeneous data integration: Information processes analysis engine in clinical remote monitoring systems | |
| KR102233725B1 (en) | Smart home platform for multi-health care service with 5g network and its way to working | |
| DE202023101305U1 (en) | An intelligent health and fitness data management system using artificial intelligence with IoT devices | |
| AU2016200158A1 (en) | System and method for autonomous chronic disease management | |
| CN109979570A (en) | First aid hand-held diagnostic device and diagnostic method with X-ray check function | |
| CN112689123A (en) | Intelligent infectious disease early warning system and method | |
| CN114141390A (en) | Remote ICU data network system | |
| CN113724853A (en) | Intelligent medical system based on deep learning | |
| CN111091901A (en) | Old people first aid decision-making method, device, equipment and storage medium | |
| Sun et al. | An overview of aiomt applications | |
| Shetty et al. | Design and Development of Mobile Phone Based Healthcare System for Emergency Situation | |
| CN115590502B (en) | Deep learning-based senile safety monitoring system and method | |
| Kumar et al. | Proactive health monitoring and screening using IoT and Artificial Intelligence | |
| CN116313021B (en) | Medical treatment management system and method for hospital |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |