Remote rehabilitation monitoring and management system and methods for patients with coronary artery disease undergoing reconstruction

Abstract

The application discloses a remote rehabilitation monitoring and management system and method for coronary heart disease reconstruction patients. The system comprises a user end and a service end. The user end is provided with a first monitoring module for acquiring first monitoring data of the patient and second monitoring data when an initial exercise plan is executed, a second monitoring module for collecting graphic data when the patient executes each exercise group of the initial exercise plan, and a first communication module for transmitting the first and second monitoring data to the service end. The service end comprises a second communication module for receiving the data, a first processing module for generating the initial exercise plan containing multiple exercise groups according to the first monitoring data, a second processing module for processing preset indexes and judging whether the initial rules are met in combination with the second monitoring data and the graphic data, a comparison module for calculating the first and second evaluation indexes, and a judgment module for generating evaluation information and a second exercise plan according to the evaluation indexes. The system can realize remote monitoring and individual management of the rehabilitation of the patient and improve the rehabilitation effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical remote monitoring and rehabilitation training, in particular to a remote rehabilitation monitoring and management system and method for coronary heart disease reconstruction patients. BACKGROUND

[0002] With the increasing incidence of cardiovascular diseases, coronary heart disease has become one of the major diseases threatening human health. It is crucial for patients after coronary heart disease revascularization to perform scientific and reasonable exercise rehabilitation during the rehabilitation process to improve their cardiac function and quality of life. However, the traditional rehabilitation management mode for coronary heart disease patients mainly relies on patients regularly going to the hospital for face-to-face rehabilitation guidance and monitoring, which has many limitations. On the one hand, patients' back-and-forth trips to the hospital increase time and economic costs, and it is difficult to ensure the continuity and timeliness of rehabilitation training; on the other hand, medical staff cannot obtain real-time physiological data and exercise execution of patients during the rehabilitation process outside the hospital, and it is difficult to dynamically adjust the rehabilitation plan according to individual differences of patients, resulting in uneven rehabilitation effects, and even safety risks may be caused by improper exercise.

[0003] In recent years, telemedicine technology has been gradually applied to the field of rehabilitation, but existing remote rehabilitation monitoring and management systems for coronary heart disease reconstruction patients still have deficiencies. Some systems can only achieve simple physiological data monitoring, lacking precise evaluation and effective management of patients' exercise execution process; some systems have exercise plan development functions, but cannot dynamically optimize rehabilitation programs according to real-time feedback data of patients, making it difficult to meet the individualized and precise rehabilitation needs of coronary heart disease patients.

[0004] Therefore, there is an urgent need for a system and method that can achieve real-time remote monitoring, precise evaluation, and individualized rehabilitation management to improve the rehabilitation effect and safety of coronary heart disease reconstruction patients. SUMMARY

[0005] To solve the above problems, the present application provides a remote rehabilitation monitoring and management system and method for coronary heart disease reconstruction patients.

[0006] In a first aspect of the present application, a remote rehabilitation monitoring and management system for coronary heart disease reconstruction patients is provided, comprising a user end and a service end.

[0007] The user end at least comprises:

[0008] A first monitoring module configured to obtain first monitoring data of the patient, and configured to obtain second monitoring data when the patient performs an initial exercise plan;

[0009] A second monitoring module configured to collect graphical data when the patient performs each exercise group in the initial exercise plan;

[0010] a first communication module configured to communicate the first monitoring data, the second monitoring data and the graphic data from the first monitoring module;

[0011] The server comprises at least:

[0012] a second communication module configured to be communicatively connected to the first communication module to receive the first monitoring data, the second monitoring data and the graphic data;

[0013] a first processing module configured to receive the first monitoring data and generate an initial exercise plan, wherein each exercise group in the initial exercise plan comprises actions, exercise duration and interval duration which are set according to the first monitoring data;

[0014] a second processing module configured to receive the second monitoring data and the graphic data, process preset indexes of each exercise group in the initial exercise plan performed by the patient according to the second monitoring data and the graphic data, and determine whether the preset indexes meet initial rules;

[0015] a comparison module configured to obtain a first evaluation index according to a difference between the preset indexes of the patient performing the initial exercise plan and the initial rules, and obtain a second evaluation index according to a difference between the second monitoring data under the current preset indexes and the monitoring data;

[0016] a determination module configured to generate evaluation information to the user terminal according to the first evaluation index, and configured to generate a second exercise plan to the user terminal according to the second evaluation index, wherein the second exercise plan comprises a plurality of exercise groups different from the initial exercise plan, or any one of actions, exercise duration, interval duration and action sequence in the same plurality of exercise groups is set to be different.

[0017] As a preferred mode, the first monitoring module is set as a wearable monitoring device configured to monitor at least heart rate and cardiopulmonary resonance index of the patient.

[0018] As a preferred mode, the second monitoring module is set as an exercise monitoring component and / or a visual monitoring module arranged with the wearable monitoring device, wherein the visual monitoring module is configured to monitor graphic data of the patient performing the initial exercise plan and the second exercise plan at a preset monitoring distance, and the exercise monitoring component is configured to monitor speed and acceleration parameters of the patient performing the initial exercise plan.

[0019] As a preferred mode, an alarm module is further included, which is arranged on the wearable monitoring device and / or the visual monitoring module, configured to generate guidance information about the first evaluation index corresponding to the initial exercise plan when the first evaluation index is received, and further configured to generate alarm information when the second evaluation index is received.

[0020] As a preferred mode, a training module is further included, which is configured to perform the following steps:

[0021] A first sample set and a second sample set are obtained, wherein the first sample set is a control group sample set using a conventional rehabilitation training method, and the second sample set is an experimental group sample set using the system of the first aspect;

[0022] A difference between the first monitoring data between the first sample set and the second sample set is obtained;

[0023] Samples with the first monitoring data difference within a preset threshold value are taken as a sample control set,

[0024] Combinations of each exercise group in the sample control set and / or actions, exercise durations, interval durations, action sequences between each exercise group in the second sample set are set to obtain a first standard exercise plan;

[0025] Feedback is obtained after training samples in the second sample set according to the first standard exercise plan.

[0026] As a preferred mode, the training module is further configured to perform the following steps:

[0027] After the first standard exercise plan is generated, samples with the first monitoring data within a preset threshold value in the first sample set and the second sample set are obtained, second monitoring data is obtained according to a periodic rehabilitation plan of the first sample set, and second monitoring data is obtained according to the first standard exercise plan of the second sample set;

[0028] An average difference between the second monitoring data between the first sample set and the second sample set is obtained;

[0029] The average difference is fed back to the first standard exercise plan, and the combinations of each exercise group in the standard exercise plan and / or the actions, exercise durations, interval durations, action sequences between each exercise group in the standard exercise plan are adjusted;

[0030] The average difference between the second monitoring data between the first sample set and the second sample set is performed to be within a preset threshold value, and a second standard exercise plan is generated;

[0031] Repeat the exercise until the patient's first monitoring data reaches the target monitoring data level, and then use the current second standard exercise plan as the initial exercise plan for the sample control set of the first monitoring data.

[0032] As a preferred approach, the preset indicators include metabolic equivalent change rate, movement intensity level, and target rehabilitation time error, and the initial rules include metabolic equivalent change rate not exceeding a preset threshold, movement intensity level matching the patient's current cardiopulmonary function level, and target rehabilitation time within a preset range.

[0033] When generating an action group, the action group sequence is generated, which includes the following steps:

[0034] Establish a correlation matrix between the action group and physiological indicators. Each element in the matrix represents the quantitative impact value of a specific action type on the coefficient of variation of heart rate, cardiopulmonary resonance index, and blood oxygen saturation. Simultaneously establish a mapping relationship between the action group and the graphic parameters of the standard action. The graphic parameters include the range of major joint angles and the degree of consistency of the movement trajectory.

[0035] Based on the patient's current risk level threshold, a set of safe actions with an impact value lower than the risk threshold is selected from the correlation matrix. The real-time joint angles and motion trajectories of the patient when performing the action are sampled based on the graphical data. The deviation value from the standard action is calculated. When the deviation value exceeds the preset safety threshold, the action type is excluded from the set of safe actions.

[0036] The sequence of each action in the action group is constrained, the metabolic equivalent change rate between adjacent actions is set to not exceed a preset threshold, the intensity level of the action in a single action group is set to match the patient's current cardiopulmonary function level, and the allowable deviation range of joint angles of each action in the action group is dynamically adjusted based on historical graphic data. When the trajectory deviation value of N consecutive actions exceeds the threshold, the intensity level of the action group is reduced.

[0037] After generating multiple action sequence sets, the first evaluation index for each action sequence set is calculated. The first evaluation index = Σ(preset index deviation × weight coefficient). The first exercise plan is selected when the first evaluation index is within a preset range and the patient performs the action sequence set with the lowest risk probability.

[0038] As a preferred embodiment, between executing the first exercise plan and the second exercise plan, the following steps are further included:

[0039] The monitoring cycle is divided into three phases: high-frequency monitoring cycle for the acute phase, medium-frequency monitoring cycle for the stable phase, and low-frequency monitoring cycle for the maintenance phase. Each monitoring cycle is within a preset monitoring time interval.

[0040] At the end of each monitoring period, the following second evaluation index, including the following dynamic adjustment coefficients, is calculated:

[0041] Action completion coefficient α = Number of actual completed action sets / Number of planned action sets;

[0042] The physiological tolerance coefficient β = Σ(real-time monitoring value / safety threshold) normalized value;

[0043] Recovery rate coefficient γ = Δcardiopulmonary function index / time;

[0044] Obtain a predefined adjustment strategy library for matching coefficient combinations [α,β,γ]. Compare the values ​​in the adjustment strategy library with the values ​​in the preset library to adjust the settings of the movements, exercise duration, interval duration, and movement sequence of the exercise groups or between exercise groups in the second exercise plan.

[0045] A second aspect of the present invention provides a method for remote rehabilitation monitoring and management of patients with coronary artery disease undergoing reconstruction, comprising the following steps:

[0046] Step S1: Acquire the patient's first monitoring data through a wearable monitoring device, and generate an initial exercise plan containing multiple exercise groups based on the first monitoring data. The movements, exercise duration, and interval duration of each exercise group are dynamically set according to the patient's heart rate variability coefficient and cardiopulmonary resonance index.

[0047] Step S2: When the patient performs the initial exercise plan, the second monitoring data and the exercise group graphic data are collected simultaneously. The exercise execution features are extracted through computer vision processing. The deviation value between the preset index and the initial rule is calculated to generate the first evaluation index. At the same time, the difference between the second monitoring data and the expected monitoring data is compared.

[0048] Step S3: Establish an action group sequence optimization model and execute:

[0049] Construct a correlation matrix between action types and physiological indicators, and filter out a set of safe actions with impact values ​​below the risk threshold;

[0050] The metabolic equivalent change rate between adjacent actions is constrained to not exceed a preset threshold, and the intensity of a single action group matches the current cardiopulmonary function level.

[0051] Generate candidate sequences that meet the target rehabilitation duration error, and select the one with the lowest risk probability as the second exercise plan;

[0052] Step S4: Divide the monitoring period into phases based on the recovery cycle, including:

[0053] High-frequency monitoring cycles are used during the acute phase;

[0054] The stabilization period uses a medium-frequency monitoring cycle;

[0055] The maintenance period uses a low-frequency monitoring cycle;

[0056] Step S5: At the end of each monitoring period, calculate the second evaluation index, which includes the following dynamic adjustment coefficients:

[0057] Action completion coefficient α = Number of actual completed action sets / Number of planned action sets;

[0058] The physiological tolerance coefficient β = Σ(real-time monitoring value / safety threshold) normalized value;

[0059] Recovery rate coefficient γ = Δcardiopulmonary function index / time dimension;

[0060] Step S6: Match the predefined adjustment strategy library according to the combination of [α,β,γ] coefficients. When α<0.8 and β>1.2, adjust the exercise intensity by downgrading. When α>1.2 and γ>the baseline, increase the metabolic equivalent by upgrading. Simultaneously update the forced rest interval between exercise groups to (total metabolic equivalent of the new exercise group / baseline metabolic rate) × compensation coefficient.

[0061] Step S7: Feed the adjusted exercise parameters back to the optimized exercise plan. Generate a second exercise plan based on the second evaluation index, which is adapted to the current rehabilitation stage and includes adjusted movement groups, exercise duration, and movement sequence. Execute the plan cyclically until the patient's physiological indicators reach the target level.

[0062] Compared with the prior art, the present invention has the following advantages:

[0063] (1) This invention acquires the patient's heart rate variability coefficient, cardiopulmonary resonance index, and other first monitoring data through the first monitoring module. The first processing module on the server generates an initial exercise plan based on these data. The movements, exercise duration, and intervals of each exercise group are dynamically set according to the patient's physiological indicators. This personalized rehabilitation plan formulation method fully considers the individual differences of patients and can provide the most suitable rehabilitation training program for coronary heart disease reconstruction patients with different physical conditions, thereby improving the pertinence and effectiveness of rehabilitation.

[0064] (2) This invention collects graphical data of the patient executing the exercise plan through the second monitoring module. The second processing module on the server combines the second monitoring data to process the preset indicators of each exercise group executed by the patient and determine whether they meet the initial rules. The comparison module further obtains the first evaluation indicator and the second evaluation indicator based on the difference between the preset indicators and the initial rules, as well as the difference between the second monitoring data under the current preset indicators and the expected monitoring data. The judgment module generates evaluation information and the second exercise plan based on these evaluation indicators, realizing accurate evaluation of the patient's exercise execution and dynamically adjusting the rehabilitation plan according to the evaluation results, making rehabilitation training more scientific and reasonable, and ensuring that the patient gradually improves the rehabilitation effect under the premise of safety. Among them, the first monitoring module uses wearable monitoring devices to monitor at least the patient's heart rate and cardiopulmonary resonance index, and obtain the patient's physiological state in real time; the second monitoring module monitors the patient's speed, acceleration parameters and graphical data when executing the exercise plan through the motion monitoring component and / or visual monitoring module, and comprehensively grasps the patient's exercise execution. The alarm module generates guidance information and alarm information based on the evaluation indicators, and issues an alarm in a timely manner when the patient's exercise is abnormal, providing comprehensive safety protection for the patient's rehabilitation training.

[0065] (3) The training module of this invention acquires a first sample set and a second sample set, compares the difference in monitoring data between the two, and iteratively optimizes the initial exercise plan and the second exercise plan. It continuously adjusts parameters such as the combination of exercise groups, movements, and exercise duration until the difference in monitoring data reaches a preset threshold, thereby generating a better rehabilitation plan. This intelligent optimization mechanism can continuously adapt to changes in the patient's physical condition and continuously improve the scientificity and effectiveness of the rehabilitation plan. At the same time, when generating the movement group sequence, this invention establishes a correlation matrix between the movement group and physiological indicators, filters a safe movement set, and constrains the metabolic equivalent change rate and movement intensity level in the movement sequence to ensure that the movement group sequence not only conforms to the patient's physical condition but also achieves a good rehabilitation effect. After confirming that the movement execution and the target rehabilitation duration are within the preset range through the first monitoring data, the movement group sequence with the lowest risk probability is selected as the exercise plan, further improving the safety and effectiveness of rehabilitation training.

[0066] (4) The method provided by this invention divides the rehabilitation phase into an acute phase, a stable phase, and a maintenance phase, adopts monitoring cycles of different frequencies, and calculates the action completion coefficient, physiological tolerance coefficient, and recovery rate coefficient at the end of each monitoring cycle. Based on the coefficient combination, a predefined adjustment strategy library is matched to dynamically adjust the exercise plan, making rehabilitation management more in line with the actual needs of patients at different rehabilitation stages, and realizing refined and scientific management of the rehabilitation process of patients with coronary artery disease reconstruction. Attached Figure Description

[0067] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0068] Figure 1 This is a structural block diagram of the system provided in the embodiments of the present invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] This disclosure provides a remote rehabilitation monitoring and management system for patients with coronary artery disease undergoing reconstruction, such as... Figure 1 As shown, the system includes a user terminal and a server terminal. The system aims to collect relevant patient data through the user terminal and transmit it to the server terminal for analysis and processing, thereby developing personalized rehabilitation plans for patients and making dynamic adjustments. In this embodiment, a progressive Baduanjin exercise is used as an example. However, the progressive Baduanjin exercise is not the only implementation method for achieving the objectives of this invention, and those skilled in the art can substitute it with other methods depending on the specific circumstances.

[0071] Specifically, the user terminal in this embodiment of the disclosure includes:

[0072] The first monitoring module is configured to acquire the patient's first monitoring data and simultaneously acquire second monitoring data when the patient performs the initial exercise plan. In this embodiment, the first monitoring module uses a progressive Baduanjin exercise program as the exercise plan. It acquires the patient's basic physical data before performing Baduanjin exercises, such as heart rate and blood pressure, as the first monitoring data. During the patient's execution of the initial exercise plan based on Baduanjin, it continuously acquires data such as heart rate and exercise intensity as the second monitoring data. The first monitoring data reflects the patient's physical condition before exercise, providing a basis for developing the initial exercise plan; the second monitoring data is used to assess the patient's physical response during the execution of the exercise plan, so as to adjust the exercise plan subsequently.

[0073] The second monitoring module is configured to collect graphical data of the patient as they perform each exercise group in the initial exercise plan. In this embodiment, the second monitoring module uses devices such as a camera to collect graphical data of the patient's posture and range of motion during each movement of the progressive Baduanjin exercise. The graphical data can intuitively reflect the standard of the patient's movements, helping to determine whether the patient is correctly executing the exercise plan and thus assessing the exercise effect.

[0074] The first communication module is configured to transmit first and second monitoring data from the first monitoring module to the server. In this embodiment, the first communication module transmits relevant monitoring data before, during, and after the Baduanjin exercise to the server via wireless communication. Through the first communication module, the server can promptly obtain the patient's monitoring data, providing a basis for subsequent data analysis and processing.

[0075] Specifically, the server in this embodiment of the disclosure includes:

[0076] The second communication module is configured to communicate with the first communication module and receive the first monitoring data and the second monitoring data. In this embodiment, the second communication module receives monitoring data related to the Baduanjin exercise from the user terminal. The second communication module acts as a bridge for data interaction between the server and the user terminal, ensuring that the server can obtain accurate patient data.

[0077] The first processing module is configured to receive the first monitoring data and generate an initial exercise plan. The initial exercise plan includes multiple exercise groups, and the movements, durations, and intervals in each exercise group are set based on the first monitoring data. In this embodiment, the first processing module determines the duration, number of repetitions, and intervals between each Baduanjin movement based on the first monitoring data before the patient begins the Baduanjin exercise, thus generating an initial Baduanjin exercise plan. The first processing module develops a personalized initial exercise plan based on the patient's individual physical condition, ensuring the rationality and safety of the exercise plan.

[0078] The second processing module is configured to receive the second monitoring data and the graphical data, process preset indicators for each exercise group of the initial exercise plan performed by the patient based on the graphical data, and determine whether the preset indicators meet the initial rules. In this embodiment, the second processing module analyzes whether the completion quality and exercise intensity of each Baduanjin movement performed by the patient meets the preset standards based on the second monitoring data and graphical data. Through the analysis of the second monitoring data and graphical data, the second processing module evaluates the patient's performance in executing the exercise plan, providing a basis for subsequent evaluation and adjustment.

[0079] The comparison module is configured to obtain a first evaluation index based on the difference between preset indicators and initial rules when executing the initial exercise plan, and a second evaluation index based on the difference between the second monitoring data and expected monitoring data under the current preset indicators. In this embodiment, the comparison module compares the differences between preset indicators such as the patient's movement standard and exercise intensity when performing Baduanjin exercises and the initial rules to obtain the first evaluation index; it compares the differences between the patient's actual heart rate, blood pressure, and other second monitoring data during exercise and the expected data to obtain the second evaluation index. Through quantified evaluation indicators, the comparison module intuitively reflects the effect of the patient's execution of the exercise plan and their physical response, providing a decision-making basis for the judgment module.

[0080] The judgment module is configured to generate evaluation information to the user terminal based on the first evaluation index, and simultaneously configure to generate a second exercise plan to the user terminal based on the second evaluation index. The second exercise plan includes multiple exercise groups different from the initial exercise plan, or multiple identical exercise groups with different settings for any of the following: movements, exercise duration, interval duration, or movement sequence. In this embodiment, the judgment module sends evaluation information about the patient's Baduanjin movement standardization and completion status to the user terminal based on the first evaluation index; it adjusts the Baduanjin exercise plan based on the second evaluation index, such as changing the movement sequence or adjusting the exercise duration, generating a second exercise plan and sending it to the user terminal. The judgment module provides feedback information to the patient based on the evaluation index and generates a second exercise plan more suitable for the patient's physical condition, realizing dynamic adjustment of the rehabilitation plan.

[0081] Specifically, in the embodiments of this disclosure, the following is true:

[0082] The first monitoring module is specifically configured as a wearable monitoring device, designed to monitor at least the patient's heart rate and cardiopulmonary resonance index. In this embodiment, the wearable monitoring device monitors the patient's heart rate and cardiopulmonary resonance index in real time during the patient's progressive Baduanjin exercise. The wearable monitoring device allows the patient to conveniently monitor key physiological indicators in real time during exercise, providing the system with accurate monitoring data.

[0083] The second monitoring module is specifically configured as a motion monitoring component and / or a visual monitoring module that are integrated with the wearable monitoring device. The visual monitoring module is configured to monitor the graphical data of the patient executing the initial exercise plan and the second exercise plan at a preset monitoring distance. The motion monitoring component is configured to monitor the speed and acceleration parameters of the patient when executing the initial exercise plan. In this embodiment, the motion monitoring component monitors parameters such as the patient's arm raising speed and turning acceleration when performing the Baduanjin movements; the visual monitoring module captures the entire process of the patient's Baduanjin movements at an appropriate distance, acquiring graphical data. The motion monitoring component and the visual monitoring module collect the patient's motion data from different angles, comprehensively reflecting the patient's movement status.

[0084] The remote rehabilitation monitoring and management system for patients undergoing coronary artery disease reconstruction also includes an alarm module. This alarm module is mounted on the wearable monitoring device and / or the visual monitoring module. It is configured to generate guidance information about the initial exercise plan corresponding to the first evaluation indicator when receiving the first evaluation indicator, and is also configured to issue alarm information when receiving the second evaluation indicator. In this embodiment, when a patient performs Baduanjin exercises, if the first evaluation indicator indicates that the movement is not standard, the alarm module displays movement correction guidance information on the wearable monitoring device; if the second evaluation indicator indicates that the patient's exercise intensity is too high and there may be a danger, the alarm module issues an alarm. The alarm module provides timely exercise guidance and danger warnings to the patient, ensuring the patient's exercise safety.

[0085] The remote rehabilitation monitoring and management system for patients with coronary artery disease reconstruction also includes a training module, which is configured to perform the following steps:

[0086] Obtain a first sample set and a second sample set, wherein the first sample set is a control group sample set using conventional rehabilitation training methods, and the second sample set is an experimental group sample set using the system described in this embodiment; it should be noted that each sample in the first sample set adopts the rehabilitation and recovery methods specified in the clinical guidelines and performs a periodic training plan according to the rehabilitation and recovery methods specified in the clinical guidelines, while the samples in the second sample set perform rehabilitation training using Baduanjin and according to the progressive combination of movement groups as in this embodiment.

[0087] Obtain the difference between the first monitoring data and the second sample set;

[0088] Samples whose differences in the first monitoring data are within a preset threshold are used as the sample control set.

[0089] The first standard exercise plan is obtained by setting the combination of each exercise group in the second sample set in the sample control set and / or the actions, exercise duration, interval duration and action sequence between each exercise group;

[0090] Feedback is then provided after training the samples in the second sample set according to the first standard exercise plan.

[0091] The training module is also configured to perform the following steps:

[0092] After generating the first standard exercise plan, samples of the first monitoring data in the first sample set and the second sample set that are within the preset threshold range are obtained. The second monitoring data is obtained according to the periodic rehabilitation plan of the first sample set, and the second monitoring data is obtained according to the first standard exercise plan of the second sample set.

[0093] Obtain the average difference between the second monitoring data of the first sample set and the second sample set;

[0094] The average difference is fed back to the first standard exercise plan to adjust the combination of each exercise group in the standard exercise plan and / or the settings of the movements, exercise duration, interval duration, and movement sequence between each exercise group;

[0095] The second standard exercise plan is generated when the average difference between the second monitoring data of the first sample set and the second sample set is within a preset threshold.

[0096] Repeat the exercise until the patient's first monitoring data reaches the target monitoring data level, and then use the current second standard exercise plan as the initial exercise plan for the sample control set of the first monitoring data.

[0097] The preset indicators include metabolic equivalent change rate, movement intensity level, and target rehabilitation time error. The initial rules include metabolic equivalent change rate not exceeding a preset threshold, movement intensity level matching the patient's current cardiopulmonary function level, and target rehabilitation time within a preset range.

[0098] When generating an action group, the action group sequence is generated, which includes the following steps:

[0099] Establish a correlation matrix between the action group and physiological indicators. Each element in the matrix represents the quantitative impact value of a specific action type on the coefficient of variation of heart rate, cardiopulmonary resonance index, and blood oxygen saturation. Simultaneously establish a mapping relationship between the action group and the graphical parameters of the standard action. The graphical parameters include at least the range of major joint angles and the degree of consistency of the movement trajectory.

[0100] Based on the patient's current risk level threshold, a set of safe actions with an impact value lower than the risk threshold is selected from the correlation matrix. The real-time joint angles and motion trajectories of the patient when performing the action are sampled based on the graphical data. The deviation value from the standard action is calculated. When the deviation value exceeds the preset safety threshold, the action type is excluded from the set of safe actions.

[0101] The sequence of each action in the action group is constrained, the metabolic equivalent change rate between adjacent actions is set to not exceed a preset threshold, the intensity level of the action in a single action group is set to match the patient's current cardiopulmonary function level, and the allowable deviation range of joint angles of each action in the action group is dynamically adjusted based on historical graphic data. When the trajectory deviation value of N consecutive actions exceeds the threshold, the intensity level of the action group is reduced.

[0102] After generating multiple movement sequence sets, a first evaluation index is calculated for each movement sequence set. The first evaluation index = Σ(preset index deviation × weight coefficient). The movement sequence set with the lowest risk probability for the patient to execute the movement sequence set within the preset range is selected as the first exercise plan. In this embodiment, multiple Baduanjin movement sequence sets are compared, and the sequence with the lowest risk to the patient is selected as the first exercise plan. The movement sequence generation step ensures the scientific nature and safety of the Baduanjin exercise plan and improves the rehabilitation effect. Furthermore, in this embodiment, the acquisition of the main joint angle range and the consistency of the movement trajectory are obtained by calculating the acceleration and velocity parameters in the graphic data. The processing and calculation of graphic data are common techniques in the field and will not be elaborated here. In the calculation of the first evaluation index, the preset index deviation represents the weighted calculation of the deviation of each parameter of metabolic equivalent change rate, movement intensity level, and target rehabilitation time error. That is, the first evaluation index = (metabolic equivalent change rate deviation × weight) + (movement intensity level deviation × weight) + (target rehabilitation time error × weight). It should be noted that the first evaluation index in this embodiment only shows the above three parameters, which does not mean that the evaluation index only includes these three parameters. As an implementation method, it also includes the main joint angle range, movement trajectory matching degree, and movement speed.

[0103] Subsequently, this embodiment of the invention further includes a step of adjusting the exercise plan. Between executing the first exercise plan and the second exercise plan, the following steps are also included:

[0104] The monitoring cycle is divided into three phases: a high-frequency monitoring cycle for the acute phase, a medium-frequency monitoring cycle for the stable phase, and a low-frequency monitoring cycle for the maintenance phase. Each monitoring cycle is within a preset monitoring time interval. In this embodiment, during the acute phase when the patient is performing Baduanjin rehabilitation training, monitoring is conducted multiple times a day; during the stable phase, monitoring is conducted once every two days; and during the maintenance phase, monitoring is conducted once a week.

[0105] At the end of each monitoring period, calculate the dynamic adjustment coefficient of the second evaluation indicator:

[0106] The action completion coefficient α = the number of actual action sets completed / the number of planned action sets; in this embodiment, the ratio of the number of Baduanjin action sets actually completed by the patient to the number of planned sets is calculated.

[0107] The physiological tolerance coefficient β is a normalized value of Σ(real-time monitoring value / safety threshold). In this embodiment, the real-time monitoring values ​​of the patient's heart rate, blood pressure, etc. during exercise are compared with the safety threshold and normalized.

[0108] The recovery rate coefficient γ = Δcardiopulmonary function index / time; in this embodiment, the ratio of the change in the patient's cardiopulmonary function index to time over a monitoring period is calculated.

[0109] A predefined adjustment strategy library is obtained for matching coefficient combinations [α, β, γ]. The values ​​in the adjustment strategy library are compared with the values ​​in the preset library to adjust the settings of movements, exercise duration, interval duration, and movement sequence in the second exercise plan, or between exercise groups. In this embodiment, if α < 0.8 and β > 1.2, it indicates that the patient's movement completion is low and physical tolerance is poor, so the intensity of the Baduanjin exercise is reduced; if α > 1.2 and γ > the baseline, it indicates that the patient's movement completion is good and recovery rate is fast, so the exercise intensity is increased. Through phased monitoring and dynamic adjustment of coefficients, the Baduanjin exercise plan can be adjusted in a timely manner according to the patient's recovery status, realizing personalized rehabilitation management.

[0110] This disclosure also provides a method for remote rehabilitation monitoring and management of patients with coronary artery disease undergoing reconstruction, including the following steps:

[0111] Step S1: Obtain the patient's first monitoring data through a wearable monitoring device, and generate an initial exercise plan containing multiple exercise groups based on the first monitoring data. The movements, exercise duration, and interval duration of each exercise group are dynamically set according to the patient's heart rate variability coefficient and cardiopulmonary resonance index. In this embodiment, the patient's heart rate variability coefficient, cardiopulmonary resonance index, and other data before performing Baduanjin exercise are obtained through a wearable monitoring device to formulate a personalized Baduanjin initial exercise plan.

[0112] Step S2: When the patient performs the initial exercise plan, the second monitoring data and exercise group graphic data are collected simultaneously. The exercise execution features are extracted through computer vision processing, and the deviation value between the preset index and the initial rule is calculated to generate the first evaluation index. At the same time, the difference between the second monitoring data and the expected monitoring data is compared to generate the second evaluation index. In this embodiment, when the patient performs the Baduanjin initial exercise plan, body data and graphic data during the exercise are collected, the standard of the movement and the body response are analyzed, and evaluation indexes are generated.

[0113] Step S3: Establish an action group sequence optimization model and execute:

[0114] A correlation matrix between movement types and physiological indicators is constructed, and a set of safe movements with influence values ​​below the risk threshold is selected. In this embodiment, the influence of Baduanjin movements on physiological indicators is analyzed to select safe movements.

[0115] The metabolic equivalent change rate between adjacent movements is constrained to not exceed a preset threshold, and the intensity of a single movement group matches the current cardiopulmonary function level; in this embodiment, the sequence and intensity of the Eight Pieces of Brocade movements are reasonably arranged.

[0116] Candidate sequences that meet the target rehabilitation time error are generated, and the one with the lowest risk probability is selected as the first exercise plan; in this embodiment, the most suitable Baduanjin movement group sequence for the patient is selected as the first exercise plan.

[0117] Step S4: Divide the monitoring period into phases, including:

[0118] High-frequency monitoring cycles are used during the acute phase;

[0119] The stabilization period uses a medium-frequency monitoring cycle;

[0120] The maintenance period uses a low-frequency monitoring cycle;

[0121] Step S5: At the end of each monitoring period, calculate the dynamic adjustment coefficient of the second evaluation indicator:

[0122] Action completion coefficient α = Number of actual completed action sets / Number of planned action sets;

[0123] The physiological tolerance coefficient β = Σ(real-time monitoring value / safety threshold) normalized value;

[0124] Recovery rate coefficient γ = Δcardiopulmonary function index / time dimension;

[0125] Step S6: Match the predefined adjustment strategy library according to the coefficient combination of [α,β,γ]. When α<0.8 and β>1.2, adjust the exercise intensity by downgrading. When α>1.2 and γ>the baseline, increase the metabolic equivalent by upgrading. Simultaneously update the forced rest interval between exercise groups to (total metabolic equivalent of the new exercise group / baseline metabolic rate) × compensation coefficient. In this embodiment, the intensity and rest interval of the Baduanjin exercise plan are adjusted according to the coefficient combination.

[0126] Step S7: The adjusted exercise parameters are fed back to the optimized exercise plan, generating a second exercise plan that includes the adjusted action sets, exercise duration, and action sequence. This plan is executed cyclically until the patient's physiological indicators reach the target level. In this embodiment, the Baduanjin exercise plan is continuously optimized until the patient's physical indicators reach the rehabilitation goal. This method, through a series of steps, achieves comprehensive and personalized management of Baduanjin rehabilitation training for patients undergoing coronary artery disease reconstruction, thereby improving rehabilitation outcomes.

[0127] This embodiment of the disclosure acquires first monitoring data such as the patient's heart rate variability coefficient and cardiopulmonary resonance index through a first monitoring module. The first processing module on the server generates an initial exercise plan based on this data. The movements, duration, and intervals of each exercise group are dynamically set according to the patient's physiological indicators. This personalized rehabilitation plan development method fully considers individual patient differences and can provide the most suitable rehabilitation training program for coronary artery disease reconstruction patients with different physical conditions, improving the pertinence and effectiveness of rehabilitation.

[0128] This embodiment of the disclosure uses a second monitoring module to collect graphical data of the patient executing an exercise plan. A second processing module on the server side combines this second monitoring data to process preset indicators for each exercise group and determine whether they conform to initial rules. A comparison module further obtains a first evaluation indicator and a second evaluation indicator based on the difference between the preset indicators and the initial rules, and the difference between the second monitoring data under the current preset indicators and the expected monitoring data. A judgment module generates evaluation information and a second exercise plan based on these evaluation indicators, achieving accurate assessment of the patient's exercise execution and dynamically adjusting the rehabilitation plan based on the assessment results. This makes rehabilitation training more scientific and reasonable, ensuring that the patient gradually improves rehabilitation effects while maintaining safety. Specifically, the first monitoring module uses wearable monitoring devices to monitor at least the patient's heart rate and cardiopulmonary resonance index, acquiring the patient's physiological state in real time. The second monitoring module monitors the patient's speed, acceleration parameters, and graphical data during exercise execution using motion monitoring components and / or a visual monitoring module, comprehensively understanding the patient's exercise execution. An alarm module generates guidance and alarm information based on the evaluation indicators, issuing timely alerts when abnormalities occur during the patient's exercise, providing comprehensive safety assurance for the patient's rehabilitation training.

[0129] The training module of this embodiment acquires a first sample set and a second sample set, compares the difference in monitoring data between the two, and iteratively optimizes the initial exercise plan and the second exercise plan. It continuously adjusts parameters such as the combination of exercise groups, movements, and exercise duration until the difference in monitoring data reaches a preset threshold, generating a better rehabilitation plan. This intelligent optimization mechanism can continuously adapt to changes in the patient's physical condition, continuously improving the scientific nature and effectiveness of the rehabilitation plan. Simultaneously, when generating the movement group sequence, this embodiment establishes a correlation matrix between the movement group and physiological indicators, filters a safe set of movements, and constrains the metabolic equivalent change rate and movement intensity level in the movement sequence to ensure that the movement group sequence not only matches the patient's physical condition but also achieves good rehabilitation results. After confirming through the first monitoring data that the movement execution and target rehabilitation duration are within a preset range, the movement group sequence with the lowest risk probability is selected as the exercise plan, further improving the safety and effectiveness of rehabilitation training.

[0130] The method provided in this disclosure divides the rehabilitation phase into an acute phase, a stable phase, and a maintenance phase, employs monitoring cycles of different frequencies, and calculates the movement completion coefficient, physiological tolerance coefficient, and recovery rate coefficient at the end of each monitoring cycle. Based on the coefficient combinations, a predefined adjustment strategy library is matched to dynamically adjust the exercise plan, making rehabilitation management more aligned with the actual needs of patients at different rehabilitation stages. This achieves refined and scientific management of the rehabilitation process for patients undergoing coronary artery disease reconstruction.

[0131] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0132] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to achieve the described functions, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the described devices, apparatuses, and units can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, function, and operation of possible implementations of apparatus, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than those disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based device that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for remote rehabilitation monitoring and management for a patient with coronary heart disease reconstruction, characterized in that, The method comprises the following steps: Step S1: acquiring first monitoring data of a patient through a wearable monitoring device, generating an initial exercise plan comprising a plurality of exercise groups based on the first monitoring data, the action, exercise duration and interval duration of each exercise group being dynamically set according to the heart rate variability and cardiopulmonary resonance index of the patient; Step S2: synchronously collecting second monitoring data and exercise group graphic data when the patient performs the initial exercise plan, extracting exercise execution features through computer vision processing, calculating a deviation value of a preset index and an initial rule to generate a first evaluation index, and generating guidance information about the initial exercise plan according to the first evaluation index, while comparing the difference between the second monitoring data and the expected monitoring data; Step S3: establishing an action group sequence optimization model, and performing: constructing an association matrix of action types to physiological indexes, and screening a safe action set with an influence value below a risk threshold; wherein each element in the matrix represents the quantitative influence value of a specific action type on the heart rate variability, cardiopulmonary resonance index and blood oxygen saturation; constraining the metabolic equivalent change rate between adjacent actions to be no more than a preset threshold, and matching the intensity of a single action group to the current cardiopulmonary function level; generating a candidate sequence that satisfies the target rehabilitation duration error, and selecting the one with the lowest risk probability as the first exercise plan; Step S4: dividing a phased monitoring period according to a recovery period, comprising: using a high-frequency monitoring period in the acute stage; using a medium-frequency monitoring period in the stable stage; using a low-frequency monitoring period in the maintenance stage; Step S5: at the end of each monitoring period, calculating a second evaluation index comprising the following dynamic adjustment coefficients: action completion coefficient α = actual completed action group number / planned action group number; physiological tolerance coefficient β = normalized value of Σ (real-time monitoring value / safety threshold); recovery rate coefficient γ = Δ cardiopulmonary function index / time; Step S6: matching a pre-defined adjustment strategy library according to the [α, β, γ] coefficient combination, adjusting the exercise intensity when α < 0.8 and β > 1.2, increasing the metabolic equivalent when α > 1.2 and γ > baseline, and synchronously updating the forced rest interval between exercise groups to (new total metabolic equivalent of action groups / baseline metabolic rate) × compensation coefficient; Step S7: feeding back the adjusted exercise parameters to the first exercise plan, generating a second exercise plan with the action group, exercise duration and action sequence adapted to the current rehabilitation stage and adjusted according to the second evaluation index, and performing the cycle until the physiological index of the patient reaches the target level.

2. A remote rehabilitation monitoring and management system for a patient with coronary revascularization for performing the remote rehabilitation monitoring and management method for a patient with coronary revascularization according to claim 1, characterized in that, The system comprises a user end and a service end; The user end at least comprises: a first monitoring module configured to acquire first monitoring data of a patient, and configured to acquire second monitoring data when the patient performs an initial exercise plan; a second monitoring module configured to collect graphic data when the patient performs each exercise group in the initial exercise plan; a first communication module configured to communicate and transmit the first monitoring data and the second monitoring data from the first monitoring module, and the graphic data from the second monitoring module; The service end at least comprises: a second communication module configured to be communicatively connected to the first communication module, and receive the first monitoring data, the second monitoring data and the graphic data; The first processing module is configured to receive the first monitoring data, and generate an initial exercise plan, wherein each exercise group in the initial exercise plan comprises actions, exercise time length and interval time length which are set according to the first monitoring data. The second processing module is configured to receive the second monitoring data and the graphic data, and process preset indexes of each exercise group in the initial exercise plan performed by the patient according to the second monitoring data and the graphic data, and determine whether the preset indexes meet initial rules. The comparison module is configured to obtain a first evaluation index according to a difference between the preset indexes of the patient performing the initial exercise plan and the initial rules, and obtain a second evaluation index according to a difference between the second monitoring data under the current preset indexes and the monitoring data. The determination module is configured to generate evaluation information to the user terminal according to the first evaluation index, and generate a second exercise plan to the user terminal according to the second evaluation index, wherein the second exercise plan comprises a plurality of exercise groups different from the initial exercise plan, or any one of actions, exercise time length, interval time length and action sequence in the same plurality of exercise groups is set to be different.

3. The remote rehabilitation monitoring and management system for a patient with a coronary revascularization procedure of claim 2, wherein, The first monitoring module is set as a wearable monitoring device configured to monitor at least heart rate and cardiopulmonary resonance index of the patient.

4. The remote rehabilitation monitoring and management system for a patient with a coronary revascularization procedure of claim 3, wherein, The second monitoring module is set as a motion monitoring component and / or a visual monitoring module arranged with the wearable monitoring device, wherein the visual monitoring module is configured to monitor graphic data of the patient performing the initial exercise plan and the second exercise plan at a preset monitoring distance, and the motion monitoring component is configured to monitor speed and acceleration parameters of the patient performing the initial exercise plan.

5. The remote rehabilitation monitoring and management system for a patient undergoing revascularization for coronary artery disease of claim 4, wherein, The system further comprises an alarm module arranged on the wearable monitoring device and / or the visual monitoring module, and configured to generate guidance information about the first evaluation index corresponding to the initial exercise plan when the first evaluation index is received, and further configured to generate alarm information when the second evaluation index is received.

6. The remote rehabilitation monitoring and management system for a patient undergoing revascularization for coronary heart disease according to any one of claims 2-5, wherein, The system further comprises a training module configured to perform the following steps: obtain a first sample set and a second sample set, wherein the first sample set is a control group sample set adopting a conventional rehabilitation training method, and the second sample set is an experimental group sample set adopting the system according to any one of claims 2-5; obtain a difference between the first monitoring data between the first sample set and the second sample set; set samples with the first monitoring data difference within a preset threshold as a sample control set, set combinations of each exercise group and / or actions, exercise time length, interval time length and action sequence between each exercise group in the sample control set to obtain a first standard exercise plan; feedback after training samples in the second sample set according to the first standard exercise plan.

7. The remote rehabilitation monitoring and management system for a patient undergoing revascularization for coronary heart disease of claim 6, wherein, The training module is further configured to perform the following steps: After the first standard exercise plan is generated, samples in the first monitoring data of the first sample set and the second sample set within a preset threshold range are obtained, the second monitoring data is obtained according to the periodic rehabilitation plan of the first sample set, and the second monitoring data is obtained according to the first standard exercise plan of the second sample set; An average difference value between the second monitoring data of the first sample set and the second sample set is obtained; The average difference value is fed back to the first standard exercise plan, and the combination of each exercise group in the standard exercise plan and / or the settings of the action, exercise duration, interval duration, action sequence between each exercise group are adjusted; The average difference value between the first monitoring data of the patient and the second monitoring data is executed to be within a preset threshold value, and a second standard exercise plan is generated; The first monitoring data of the patient is repeatedly executed to reach the target monitoring data level, and the current second standard exercise plan is taken as the initial exercise plan of the sample contrast set of the first monitoring data.

8. The remote rehabilitation monitoring and management system for coronary heart disease reconstruction patients according to claim 7, wherein The preset indicators include metabolic equivalent change rate, action intensity level, and target rehabilitation duration error, and the initial rules include that the metabolic equivalent change rate does not exceed a preset threshold value, the action intensity level matches the current cardiopulmonary function level of the patient, and the target rehabilitation duration is within a preset range; When the action group is generated, an action group sequence is generated, specifically including the following steps: An association matrix of the action group and the physiological indicators is established, each element in the matrix represents the quantitative influence value of a specific action type on the heart rate variability coefficient, cardiopulmonary resonance index, and blood oxygen saturation, and a mapping relationship between the action group and the standard action graphical parameters is simultaneously established, the graphical parameters including the main joint angle range and the motion trajectory fitting degree; According to the risk level threshold value of the patient at the current stage, a safe action set with an action type influence value lower than the risk threshold value is screened in the association matrix, and based on the graphical data, the real-time joint angle and motion trajectory of the patient when performing the action are sampled, the deviation value from the standard action is calculated, and when the deviation value exceeds a preset safety threshold value, the action type is excluded from the safe action set; The sequence of each action in the action group is constrained, the metabolic equivalent change rate between adjacent actions is set to not exceed a preset threshold value, the action intensity level within a single action group is set to match the current cardiopulmonary function level of the patient, and the joint angle allowable deviation range of each action within the action group is dynamically adjusted based on historical graphical data, and when the trajectory deviation value of consecutive N actions exceeds the threshold value, the intensity level of the action group is reduced; After a plurality of action group sequences are generated, a first evaluation index of each action group sequence is calculated, the first evaluation index = Σ (preset indicator deviation × weight coefficient), and the minimum risk probability of the patient performing the action group sequence within a preset range is selected as the first exercise plan.

9. The remote rehabilitation monitoring and management system for a patient undergoing revascularization for coronary heart disease of claim 8, wherein, Between the execution of the first exercise plan and the second exercise plan, the following steps are further included: The rehabilitation stage monitoring period is divided into a high-frequency monitoring period for the acute stage, a medium-frequency monitoring period for the stable stage, and a low-frequency monitoring period for the maintenance stage, wherein each monitoring period is located in a preset monitoring time interval; At the end of each monitoring period, the following second evaluation index including the following dynamic adjustment coefficient is calculated: Action completion coefficient α = actual completed action group number / planned action group number; Physiological tolerance coefficient β = normalized value of Σ (real-time monitoring value / safety threshold); Recovery rate coefficient γ = Δ cardiopulmonary function index / time; The combination [α, β, γ] of the coefficients is matched with a predefined adjustment strategy library, the values in the adjustment strategy library are compared with the values in the preset library, and the settings of the action, the exercise duration, the interval duration, and the action sequence between the exercise groups in the second exercise plan are adjusted.

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