Remote collaboration and adaptive control system of ICU-AW rehabilitation robot based on 5G network

Abstract

The present invention provides a remote collaborative and adaptive control system for an ICU-AW rehabilitation robot based on a 5G network, which belongs to the field of visual detection technology. The system comprises: an operation simulation state evaluation module, an ICU environment monitoring module, an initial plan simulation execution module, and an initial plan judgment and adjustment module. The patient state monitoring component collects the patient's initial muscle oxygen state data, and the rehabilitation robot simulates and verifies the muscle oxygen state evaluation value based on the data, and uploads it to the rehabilitation control platform; at the same time, the ICU environment monitoring component obtains the environmental state data to evaluate the environmental feature influence coefficient and then uploads it. The rehabilitation control platform matches the initial plan according to the muscle oxygen simulation state evaluation value, remotely collaborates with the rehabilitation robot to simulate the execution, obtains execution data, and comprehensively evaluates the execution quality index of the environmental feature influence coefficient. The execution quality index is compared with the expected index to determine whether adaptive adjustment is made, thereby realizing remote collaborative and adaptive control of the ICU-AW rehabilitation robot.

Description

Technical Field

[0001] The present invention relates to the field of visual detection technology, and in particular to a remote collaborative and adaptive control system of an ICU-AW rehabilitation robot based on a 5G network. Background Art

[0002] Critically ill patients who remain bedridden / require mechanical ventilation for more than 5-7 days are highly susceptible to intensive care unit-acquired weakness (ICU-AW), with an incidence rate as high as 85% and an in-hospital mortality rate as high as 27%. Current clinical interventions are ineffective, and long-term prognosis is poor. The United States spends $16 billion annually on this issue. ICU-AW patients experience multi-system weakness simultaneously, including weakness in the brain, heart and lungs, and the trunk and limbs, leading to cognitive and communication impairments, activity intolerance, and impaired executive function. Therefore, rehabilitation requires comprehensive coverage of all these issues, without any single one missing.

[0003] Existing rehabilitation assessment and intervention methods are unable to meet clinical needs, primarily due to three issues: inadequacy, scarcity, and inadequacy. Inadequacy refers to the lack of an assessment system, making early diagnosis difficult. Scarcity refers to the unique conditions of ICU-AW patients, such as sedation and intubation, which necessitate limited rehabilitation methods tailored to these specific conditions. Inadequacy refers to the poor coordination of patient rehabilitation prescriptions, the patchwork of complex content, and the heavy reliance on manual labor, resulting in low efficiency. Summary of the Invention

[0004] The present invention provides a remote collaborative and adaptive control system for the ICU-AW rehabilitation robot based on a 5G network, which solves the problems in the existing technology of the lack of an evaluation system, the particularity of the environment, and the low efficiency of reliance on manpower.

[0005] In order to solve the above-mentioned purpose of the invention, the technical solution provided by the present invention is as follows:

[0006] A remote collaborative and adaptive control system for an ICU-AW rehabilitation robot based on a 5G network includes: a muscle oxygen simulation state evaluation module, which is used for the patient state monitoring component to perform initial state monitoring on ICU-AW patients, collect the patient's initial muscle oxygen state data, and the rehabilitation robot simulates the patient's muscle oxygen state according to the initial muscle oxygen state data, determines the muscle oxygen simulation state evaluation value of the rehabilitation robot, and verifies it with a predefined muscle oxygen simulation state evaluation threshold to determine whether the muscle oxygen simulation state of the rehabilitation robot is optimized, and uploads the muscle oxygen simulation state evaluation value of the rehabilitation robot to the rehabilitation control platform through the 5G network; an ICU environment monitoring module, which is used for the ICU environment monitoring component to monitor the ICU environment to which the rehabilitation robot belongs, obtain the ICU environment state data to which the rehabilitation robot belongs, and evaluate the rehabilitation robot's The environmental characteristic influence coefficient of the ICU is uploaded to the rehabilitation control platform through the 5G network; the initial plan simulation execution module is used for the rehabilitation control platform to match the initial rehabilitation control plan according to the muscle oxygen simulation state evaluation value of the rehabilitation robot, so that the rehabilitation control platform can simulate the execution of the initial rehabilitation control plan through the remote collaborative rehabilitation robot and obtain the execution data of the initial rehabilitation control plan; the initial plan judgment and adjustment module is used for the rehabilitation control platform to evaluate the execution quality index of the initial rehabilitation control plan according to the execution data of the initial rehabilitation control plan, the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs, and compare it with the predefined execution quality expected index to determine whether to make adaptive adjustments to the initial rehabilitation control plan, and finally complete the remote collaboration and adaptive control of the ICU-AW rehabilitation robot.

[0007] Optionally, the muscle oxygen simulation state evaluation value of the rehabilitation robot is determined, and the specific determination process is: the rehabilitation robot simulates the muscle oxygen state of the patient according to the initial muscle oxygen state data, and obtains the muscle oxygen simulation state data of the rehabilitation robot, specifically including the output power of the rehabilitation robot at each muscle oxygen simulation time point, the number of set action repetitions of the rehabilitation robot within the muscle oxygen simulation cycle, the response delay time of the rehabilitation robot within the muscle oxygen simulation cycle, and the sensor linearity error mean of the rehabilitation robot within the muscle oxygen simulation cycle; the output power of the rehabilitation robot at each muscle oxygen simulation time point is averaged to obtain the output power of the rehabilitation robot within the muscle oxygen simulation cycle. The average output power value is obtained; the electromagnetic radiation intensity value of the environment to which the rehabilitation robot belongs at each muscle oxygen simulation time point is obtained; the output power adaptation average value and the set action repetition adaptation number are extracted from the rehabilitation control information library; the output power average value of the rehabilitation robot during the muscle oxygen simulation cycle, the set action repetition number of the rehabilitation robot during the muscle oxygen simulation cycle, the response delay time of the rehabilitation robot during the muscle oxygen simulation cycle, the average value of the sensor linearity error of the rehabilitation robot during the muscle oxygen simulation cycle, and the electromagnetic radiation intensity value of the environment to which the rehabilitation robot belongs at each muscle oxygen simulation time point are comprehensively processed to obtain the muscle oxygen simulation state evaluation value of the rehabilitation robot.

[0008] Optionally, the determination of whether to optimize the muscle oxygen simulation state of the rehabilitation robot is specifically to verify the muscle oxygen simulation state evaluation value of the rehabilitation robot with a predefined muscle oxygen simulation state evaluation threshold to obtain a verification result, and determine whether to optimize the muscle oxygen simulation state of the rehabilitation robot based on the verification result; the verification result is a first verification result or a second verification result; the first verification result is specifically that the muscle oxygen simulation state evaluation value of the rehabilitation robot is greater than or equal to the muscle oxygen simulation state evaluation threshold; the second verification result is specifically that the muscle oxygen simulation state evaluation value of the rehabilitation robot is less than the muscle oxygen simulation state evaluation threshold; if the verification result shows the first verification result, there is no need to optimize the muscle oxygen simulation state of the rehabilitation robot; if the verification result shows the second verification result, it is necessary to optimize the muscle oxygen simulation state of the rehabilitation robot.

[0009] Optionally, the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs is evaluated, and the specific evaluation process is as follows: the environmental status data of the ICU to which the rehabilitation robot belongs specifically includes the real-time environmental temperature of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time environmental humidity of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time electromagnetic interference intensity of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time noise decibel value of the ICU environment to which the rehabilitation robot belongs during the evaluation period, and the real-time carbon dioxide concentration of the ICU environment to which the rehabilitation robot belongs during the evaluation period; extracting the environmental temperature adaptation value, the environmental humidity adaptation value, and the carbon dioxide concentration adaptation value from the rehabilitation control information library; comprehensively analyzing the real-time environmental temperature of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time environmental humidity of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time electromagnetic interference intensity of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time noise decibel value of the ICU environment to which the rehabilitation robot belongs during the evaluation period, and the real-time carbon dioxide concentration of the ICU environment to which the rehabilitation robot belongs during the evaluation period to obtain the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs.

[0010] Optionally, the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs is specifically analyzed by the following method:

[0011]

[0012] Where HJ is the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs, t is the time variable, t∈[t0, t1], t0 is the start time of the evaluation period, t1 is the end time of the evaluation period, β(t) is the real-time ambient temperature of the ICU environment to which the rehabilitation robot belongs at time t of the evaluation period, β′ is the ambient temperature adaptation value, γ(t) is the real-time ambient humidity of the ICU environment to which the rehabilitation robot belongs at time t of the evaluation period, γ′ is the ambient humidity adaptation value, δ(t) is the real-time electromagnetic interference intensity of the ICU environment to which the rehabilitation robot belongs at time t of the evaluation period, θ(t ) is the real-time noise decibel value of the ICU environment to which the rehabilitation robot belongs at the evaluation period t, τ(t) is the real-time carbon dioxide concentration of the ICU environment to which the rehabilitation robot belongs at the evaluation period t, τ′ is the carbon dioxide concentration adaptation value, YX is the muscle oxygen simulation state evaluation value of the rehabilitation robot, b1 is the environmental characteristic parameter corresponding to the real-time electromagnetic interference intensity predefined in the rehabilitation control information library, b2 is the environmental characteristic parameter corresponding to the real-time noise decibel value predefined in the rehabilitation control information library, and k1 is the environmental characteristic parameter corresponding to the muscle oxygen simulation state evaluation value predefined in the rehabilitation control information library.

[0013] Optionally, the matching obtains an initial rehabilitation control plan, and the specific matching process is: matching the muscle oxygen simulation state evaluation value of the rehabilitation robot with the initial rehabilitation control plan corresponding to each predefined muscle oxygen simulation state evaluation value interval. The specific matching process is: extracting a mapping set between the muscle oxygen simulation state evaluation value of the rehabilitation robot and the initial rehabilitation control plan from the rehabilitation control information library, determining the interval to which the muscle oxygen simulation state evaluation value of the rehabilitation robot belongs, and obtaining the initial rehabilitation control plan corresponding to the interval, thereby matching to obtain the initial rehabilitation control plan.

[0014] Optionally, the execution data of the initial rehabilitation control plan specifically includes the number of rehabilitation training completions of the initial rehabilitation control plan within the execution cycle, the feedback response duration of the initial rehabilitation control plan within the execution cycle, the number of rehabilitation robot failures within the execution cycle of the initial rehabilitation control plan, and the final simulated muscle oxygen saturation of the rehabilitation robot within the execution cycle of the initial rehabilitation control plan; obtaining the initial simulated muscle oxygen saturation of the rehabilitation robot within the muscle oxygen simulation cycle; obtaining the preset number of rehabilitation training of the initial rehabilitation control plan within the execution cycle from the rehabilitation control information library; and performing ratio processing on the number of rehabilitation training completions of the initial rehabilitation control plan within the execution cycle and the preset number of rehabilitation training of the initial rehabilitation control plan within the execution cycle to obtain the rehabilitation training simulation completion rate of the initial rehabilitation control plan within the execution cycle.

[0015] Optionally, the execution quality index of the initial rehabilitation control plan is specifically analyzed as follows: matching the number of action repetitions of the rehabilitation robot within the muscle oxygen simulation cycle with the number of fault definitions corresponding to each predefined interval of action repetitions, thereby obtaining the fault definition number of the rehabilitation robot; matching the response delay duration of the rehabilitation robot within the muscle oxygen simulation cycle with the feedback response definition duration corresponding to each predefined interval of response delay duration, thereby obtaining the feedback response definition duration of the initial rehabilitation control plan; comprehensively analyzing the rehabilitation training simulation completion rate of the initial rehabilitation control plan within the execution cycle, the feedback response duration of the initial rehabilitation control plan within the execution cycle, the feedback response definition duration of the initial rehabilitation control plan, the number of rehabilitation robot failures within the execution cycle of the initial rehabilitation control plan, the number of fault definitions of the rehabilitation robot, the final simulated muscle oxygen saturation of the rehabilitation robot within the execution cycle of the initial rehabilitation control plan, the initial simulated muscle oxygen saturation of the rehabilitation robot within the muscle oxygen simulation cycle, and the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs, thereby obtaining the execution quality index of the initial rehabilitation control plan.

[0016] Optionally, the determination of whether to perform adaptive adjustment on the initial rehabilitation control plan is as follows: if the execution quality index of the initial rehabilitation control plan is equal to the default execution quality index of the initial rehabilitation control plan, a predefined optimization adjustment plan is executed on the initial rehabilitation control plan; if the execution quality index of the initial rehabilitation control plan is not equal to the default execution quality index of the initial rehabilitation control plan, the execution quality index of the initial rehabilitation control plan is compared with a predefined execution quality reference index; if the execution quality index of the initial rehabilitation control plan is greater than the execution quality reference index, there is no need to perform adaptive adjustment on the initial rehabilitation control plan; if the execution quality index of the initial rehabilitation control plan is less than or equal to the execution quality reference index, then the initial rehabilitation control plan needs to be adaptively adjusted.

[0017] Optionally, the adaptive adjustment of the initial rehabilitation control plan is carried out, and the specific adjustment process is: performing difference processing on the execution quality index of the initial rehabilitation control plan and the execution quality reference index to obtain the execution quality deviation value of the initial rehabilitation control plan, and matching it with the adaptive adjustment plan corresponding to each predefined execution quality deviation value interval, so as to obtain the adaptive adjustment plan of the initial rehabilitation control plan, and finally performing adaptive adjustment on the initial rehabilitation control plan.

[0018] The technical solution provided by the present invention has at least the following beneficial effects compared with the prior art:

[0019] In the above scheme, the patient's initial muscle oxygen status data is collected through the patient status monitoring component, and the rehabilitation robot simulates and verifies the muscle oxygen status evaluation value based on this, and uploads it to the rehabilitation control platform; at the same time, the ICU environmental monitoring component obtains the environmental status data, evaluates the environmental characteristic impact coefficient, and then uploads it. The rehabilitation control platform matches the initial plan according to the muscle oxygen simulation status evaluation value, remotely collaborates with the rehabilitation robot to simulate the execution, obtains the execution data, and comprehensively evaluates the execution quality index of the environmental characteristic impact coefficient. It is compared with the expected index to determine whether adaptive adjustment is made, thereby realizing remote collaboration and adaptive control of the ICU-AW rehabilitation robot.

[0020] By collecting the patient's initial muscle oxygen status data, the rehabilitation robot simulates the patient's muscle oxygen status based on the initial muscle oxygen status data, determines the rehabilitation robot's muscle oxygen simulation status evaluation value, and helps the rehabilitation robot accurately simulate the patient's muscle oxygenation status. Based on the individual differences of the patient, the rehabilitation robot can simulate the best rehabilitation plan for the patient as accurately as possible, thereby improving the adaptability and effectiveness of the rehabilitation robot.

[0021] By obtaining the environmental status data of the ICU where the rehabilitation robot belongs and evaluating the environmental characteristic influence coefficient of the ICU where the rehabilitation robot belongs, the environmental factors that may affect the performance of the rehabilitation robot can be identified in advance, and corresponding safeguards can be taken in time, so that the rehabilitation robot can work in a suitable environment and its performance remains stable, which helps to reduce robot failures caused by environmental factors and ensure the smooth implementation of subsequent rehabilitation simulation training.

[0022] By evaluating the execution quality indicators of the initial rehabilitation control plan and comparing them with the predefined expected execution quality indicators, it is determined whether the initial rehabilitation control plan should be adaptively adjusted. This allows timely discovery of possible problems in the rehabilitation simulation plan, allowing for different degrees of adaptive adjustment of the plan, thereby improving the simulation effect of the rehabilitation robot, making the initial rehabilitation control plan more suitable for the patient's condition, and improving the feasibility of the plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic diagram of system modules involved in an embodiment of the present invention;

[0025] Figure 2 This is the sensor linearity curve.

[0026] Reference numerals: 1, reference straight line; 2, sensor linearity curve. DETAILED DESCRIPTION

[0027] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Unless otherwise defined, the technical or scientific terms used in the present invention shall have the usual meaning understood by persons of ordinary skill in the field to which the present invention belongs. The words "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one", "an" or "the" do not indicate a quantity limitation, but rather indicate the presence of at least one. Words such as "include" or "comprise" mean that the elements or objects preceding the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0029] It should be noted that the terms "up", "down", "left", "right", "front" and "back" used in the present invention are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0030] In response to the problems of the lack of existing evaluation systems, the particularity of the environment, and the low efficiency of reliance on manpower, the present invention provides an ICU-AW rehabilitation robot remote collaboration and adaptive control system based on a 5G network, which can effectively improve the evaluation system, reduce the negative impact of special environments, and improve control efficiency.

[0031] like Figure 1 As shown, an embodiment of the present invention provides an ICU-AW rehabilitation robot remote collaboration and adaptive control system based on a 5G network, including: a muscle oxygen simulation state evaluation module, which is used for the patient state monitoring component to perform initial state monitoring on ICU-AW patients, collect the patient's initial muscle oxygen state data, and the rehabilitation robot simulates the patient's muscle oxygen state according to the initial muscle oxygen state data, determines the muscle oxygen simulation state evaluation value of the rehabilitation robot, and verifies it with the predefined muscle oxygen simulation state evaluation threshold to determine whether to optimize the muscle oxygen simulation state of the rehabilitation robot, and upload the muscle oxygen simulation state evaluation value of the rehabilitation robot to the rehabilitation control platform through the 5G network.

[0032] The patient status monitoring component is an equipment system used to comprehensively monitor the physical status of ICU-AW (ICU-acquired weakness) patients. Its main function is to accurately collect the patient's initial muscle oxygen status data. It may also monitor other physiological parameters of the patient, such as heart rate, blood pressure, respiratory rate, body temperature, etc., to provide basic data support for subsequent rehabilitation simulation plans. It can be a wearable muscle oxygen content real-time monitoring device and use wireless communication technology to send data to the control system of the rehabilitation robot.

[0033] The ICU-AW rehabilitation robot is an intelligent device specifically designed to assist ICU-AW patients with rehabilitation treatment. Combining technologies from multiple fields, including mechanical engineering, electronics, computer science, and rehabilitation medicine, it can provide precise, personalized rehabilitation training in the ICU environment for patients experiencing debilitating conditions such as muscle weakness and limited joint mobility caused by factors such as long-term bed rest, illness, or medication. The ICU-AW rehabilitation robot's mechanical structure is designed to simulate the body's natural movements and is equipped with sensors to monitor the patient's muscle oxygen status. The robot can strictly follow the simulation program set by the rehabilitation control platform, including training time, frequency, and exercise pattern combinations, to provide customized rehabilitation services for patients.

[0034] The rehabilitation control platform is an integrated intelligent system that plays a core control and coordination role in the entire rehabilitation process. It is like the "brain center" of rehabilitation treatment, integrating data from various aspects, including patient status data, rehabilitation robot data, and ICU environment data, etc., and then analyzing and making decisions based on this data, remotely and collaboratively controlling the work of the rehabilitation robot, and being able to adaptively adjust the rehabilitation plan to achieve the best rehabilitation effect.

[0035] The patient's initial muscle oxygen status data may specifically include the patient's muscle oxygen saturation at each acquisition time point, the patient's oxygen uptake rate at each acquisition time point, and the patient's muscle oxygen metabolic rate at each acquisition time point.

[0036] The collection time points are specifically several collection time points that divide the collection period into time points, where the division method can be 30 seconds. The collection period is determined by the control management personnel based on a comprehensive analysis of factors such as the patient's status, equipment monitoring status, and specific collection requirements.

[0037] The rehabilitation robot simulates the patient's muscle oxygen status according to the initial muscle oxygen status data. Specifically, the rehabilitation robot adopts a data-driven machine learning model, such as a neural network model, and uses the patient's historical muscle oxygen status data to form a training set of the crop model. The neural network model is trained using the training set, and the weights and biases of the network are adjusted through the back propagation algorithm, so that the model can learn the complex relationship between the input parameters and the muscle oxygen status. The patient's initial muscle oxygen status data is used as the input of the neural network model, and the data is cleaned to remove possible noise and outliers, for example, due to poor sensor contact or external interference. Extremely high or low muscle oxygen saturation data points that occur in an instant are identified and corrected or eliminated. Data normalization is performed to convert muscle oxygen data in different ranges into a standard interval to facilitate subsequent model processing. For example, the muscle oxygen saturation value range is converted from 0-100% to a numerical range of 0-1 to make the data comparable and consistent. The model calculates the simulated muscle oxygen state through a forward propagation algorithm based on the learned weights and biases. For example, in a neural network, the input data undergoes linear and nonlinear transformations between layers, and ultimately the simulated muscle oxygen saturation and oxygen uptake rate parameters are obtained at the output layer.

[0038] Among them, the rehabilitation robot simulates the patient's muscle oxygen state according to the initial muscle oxygen state data, and obtains the muscle oxygen simulation state data of the rehabilitation robot, specifically including the output power of the rehabilitation robot at each muscle oxygen simulation time point, the number of set action repetitions of the rehabilitation robot in the muscle oxygen simulation cycle, the response delay time of the rehabilitation robot in the muscle oxygen simulation cycle, and the average value of the sensor linearity error of the rehabilitation robot in the muscle oxygen simulation cycle.

[0039] The muscle oxygen simulation cycle is specifically a period of time used by the rehabilitation robot to simulate the patient's status based on the patient data. Each muscle oxygen simulation time point is specifically a number of muscle oxygen simulation time points that divide the muscle oxygen simulation cycle into time points, where the division method can be 30 seconds. The determination of the muscle oxygen simulation cycle is obtained by the control management personnel based on a comprehensive analysis of factors such as the simulation status, simulation environment, and actual simulation needs of the rehabilitation robot.

[0040] The muscle oxygen simulation state data of the rehabilitation robot is specifically extracted from the muscle oxygen simulation report of the rehabilitation robot.

[0041] In a specific embodiment, the mean linearity error of the sensor can be obtained by applying a series of input physical quantities of different magnitudes to the sensor during a muscle oxygen simulation period, such as applying muscle oxygen saturation of different magnitudes to the muscle oxygen sensor, while recording the output signal of the sensor, such as the current signal, and plotting these input-output data into a curve to construct a sensor linearity curve diagram, such as Figure 2As shown, the horizontal axis is muscle oxygen saturation, the unit is percentage, the vertical axis is the current signal, the unit is ampere. Ideally, the sensor linearity curve should be a straight line, so a reference straight line 1 is located in the sensor linearity curve diagram. In actual testing, the difference between the sensor linearity curve 2 and the reference straight line 1 can be calculated and averaged to obtain the average sensor linearity error of the rehabilitation robot during the muscle oxygen simulation cycle.

[0042] The output power of the rehabilitation robot at each muscle oxygen simulation time point was averaged to obtain the average output power of the rehabilitation robot during the muscle oxygen simulation cycle.

[0043] The electromagnetic radiation intensity value of the environment of the rehabilitation robot at each muscle oxygen simulation time point is obtained, wherein the electromagnetic radiation intensity value can be obtained by detecting with an electromagnetic radiation detector.

[0044] It should be explained that the environment to which the rehabilitation robot in this embodiment belongs may be the same as or different from the ICU environment to which the rehabilitation robot described below belongs.

[0045] The output power adaptation average value and the set action repetition adaptation times are extracted from the rehabilitation control information library.

[0046] The specific determination process of determining the muscle oxygen simulation state evaluation value of the rehabilitation robot is as follows:

[0047] The average output power of the rehabilitation robot during the muscle oxygen simulation cycle, the number of set action repetitions of the rehabilitation robot during the muscle oxygen simulation cycle, the response delay time of the rehabilitation robot during the muscle oxygen simulation cycle, the average linearity error of the sensor of the rehabilitation robot during the muscle oxygen simulation cycle, and the electromagnetic radiation intensity value of the environment to which the rehabilitation robot belongs at each muscle oxygen simulation time point are comprehensively processed to obtain the muscle oxygen simulation state evaluation value of the rehabilitation robot. The specific method is as follows:

[0048]

[0049] Where YX is the muscle oxygen simulation state evaluation value of the rehabilitation robot, GL is the average output power of the rehabilitation robot during the muscle oxygen simulation period, and GL ′ is the average value of output power adaptation, DZ is the number of set action repetitions of the rehabilitation robot in the muscle oxygen simulation cycle, and DZ ′ is the number of repeated adaptations for the set action, WY is the response delay time of the rehabilitation robot during the muscle oxygen simulation cycle, WC is the mean linearity error of the sensor of the rehabilitation robot during the muscle oxygen simulation cycle, DF nis the electromagnetic radiation intensity value of the environment to which the rehabilitation robot belongs at the nth muscle oxygen simulation time point, n is the number of each muscle oxygen simulation time point, n = 1, 2, 3, ..., N, N is the total number of muscle oxygen simulation time points, max represents the maximum value, a2 is the muscle oxygen simulation influence factor corresponding to the response delay time predefined in the rehabilitation control information library, a3 is the muscle oxygen simulation influence factor corresponding to the mean value of the sensor linearity error predefined in the rehabilitation control information library, a4 is the muscle oxygen simulation influence factor corresponding to the maximum electromagnetic radiation intensity value predefined in the rehabilitation control information library, and e is a natural constant.

[0050] In this embodiment, the muscle oxygen simulation state evaluation value of the rehabilitation robot is used to measure the accuracy and effectiveness of the rehabilitation robot's simulation of the patient's muscle oxygenation state during the simulation of the patient's limb movement. The higher the muscle oxygen simulation state evaluation value, the more accurate the rehabilitation robot's grasp of the patient's muscle oxygen state, and the greater the help for subsequent operations.

[0051] It should be explained that the output power average value refers to the average value of the power output by the rehabilitation robot during the muscle oxygen simulation cycle. For a rehabilitation robot, the output power reflects the amount of energy output per unit time during the simulation of the patient's exercise; the output power adaptation average value refers to the reference average value corresponding to the pre-set output power; the set number of action repetitions refers to the number of times the rehabilitation robot repeats the preset simulation action during the muscle oxygen simulation cycle. For example, in a muscle oxygen simulation cycle of an upper limb rehabilitation simulation training, the preset action of the rehabilitation robot is to simulate the flexion and extension movement of the arm, from straightening the arm to bending the arm so that the palm is close to the shoulder, and then returning to the straight state as a complete action. If in this simulation cycle, the robot is set to complete such arm flexion and extension movements 30 times, then these 30 times are It is the set number of action repetitions; the set action repetition adaptation number refers to the reference value corresponding to the predefined set number of action repetitions; the response delay time refers to the time interval from the time the rehabilitation robot receives the simulation instruction issued by the rehabilitation control platform to the time the rehabilitation robot actually performs the simulated action during the muscle oxygen simulation cycle; the sensor linearity error mean refers to the average value of the deviation between the actual output signal and the ideal linear output signal of the muscle oxygen sensor carried by the rehabilitation robot when measuring muscle oxygen-related physical quantities during the muscle oxygen simulation cycle; the electromagnetic radiation intensity value refers to the intensity of the electromagnetic radiation existing in the spatial environment of the rehabilitation robot during the muscle oxygen simulation cycle. This electromagnetic radiation includes the electromagnetic radiation generated by the operation of the rehabilitation robot's own electronic equipment, as well as the electromagnetic radiation emitted by other surrounding medical equipment, electrical equipment, etc.

[0052] The muscle oxygen simulation influence factor corresponding to the response delay time, the muscle oxygen simulation influence factor corresponding to the mean value of the sensor linearity error, and the muscle oxygen simulation influence factor corresponding to the maximum electromagnetic radiation intensity value are all obtained in advance in the rehabilitation control information library, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. For example, the response delay time and the muscle oxygen simulation influence factor corresponding to the preset response delay time in the rehabilitation control information library form a mapping set, and the real-time response delay time is brought into the mapping set to obtain the muscle oxygen simulation influence factor corresponding to the response delay time; the muscle oxygen simulation influence factor corresponding to the mean value of the sensor linearity error and the preset mean value of the sensor linearity error in the rehabilitation control information library are A mapping set is formed by assuming the influencing factors, and the real-time mean value of the sensor linearity error is substituted into the mapping set to obtain the muscle oxygen simulation influencing factor corresponding to the mean value of the sensor linearity error; a mapping set is formed by the muscle oxygen simulation influencing factors corresponding to the maximum electromagnetic radiation intensity value and the preset maximum electromagnetic radiation intensity value in the rehabilitation control information library, and the real-time maximum electromagnetic radiation intensity value is substituted into the mapping set to obtain the muscle oxygen simulation influencing factor corresponding to the maximum electromagnetic radiation intensity value. In this embodiment, the value ranges of the muscle oxygen simulation influencing factor corresponding to the response delay time, the muscle oxygen simulation influencing factor corresponding to the mean value of the sensor linearity error, and the muscle oxygen simulation influencing factor corresponding to the maximum electromagnetic radiation intensity value are all (0, 1).

[0053] It should be explained that during the process of the rehabilitation robot simulating the patient's muscle oxygen status, if the output power of the rehabilitation robot during this process is too high and deviates from the adapted output power, it may cause the rehabilitation robot to repeatedly execute the set action, thereby greatly increasing the number of set action executions, causing it to also deviate from the adapted execution number, thereby greatly reducing the muscle oxygen simulation state of the rehabilitation robot. Conversely, if the output power is low and less than the adapted output power, there may be a situation where the output power is insufficient to support the execution of the set action, resulting in the set action execution number being far lower than the adapted execution number, which will also reduce the muscle oxygen simulation state of the rehabilitation robot; if the mean value of the sensor linearity error is large, the number of action repetitions set based on these inaccurate data may not be suitable for simulating the actual situation of the patient. For example, if the muscle oxygen sensor error is large, the oxygen metabolism capacity of the patient's muscles may be misjudged, thereby setting too high or too low a number of action repetitions, affecting the simulation effect of the rehabilitation robot and reducing the simulation state; too high an electromagnetic radiation intensity value may increase the response delay time of the rehabilitation robot, and electromagnetic interference may affect the robot's communication system and control system, slowing down data transmission and processing speeds, thereby increasing the response delay time, thereby reducing the muscle oxygen simulation state of the rehabilitation robot.

[0054] Among them, the determination of whether to optimize the muscle oxygen simulation state of the rehabilitation robot is specifically to verify the muscle oxygen simulation state evaluation value of the rehabilitation robot with a predefined muscle oxygen simulation state evaluation threshold to obtain a verification result, and determine whether to optimize the muscle oxygen simulation state of the rehabilitation robot based on the verification result.

[0055] The verification result is the first verification result or the second verification result.

[0056] The first verification result is specifically that the muscle oxygen simulation state evaluation value of the rehabilitation robot is greater than or equal to a muscle oxygen simulation state evaluation threshold predefined in the rehabilitation control information library.

[0057] Specifically, the second test result is that the muscle oxygen simulation state evaluation value of the rehabilitation robot is less than the muscle oxygen simulation state evaluation threshold.

[0058] If the verification result shows the first verification result, there is no need to optimize the muscle oxygen simulation state of the rehabilitation robot. If the verification result shows the second verification result, the muscle oxygen simulation state of the rehabilitation robot needs to be optimized.

[0059] The muscle oxygen simulation state of the rehabilitation robot is optimized, specifically using professional calibration equipment, such as a high-precision oxygen content standard gas source, simulated tissue samples with known muscle oxygen saturation, etc., to calibrate the muscle oxygen sensor carried by the rehabilitation robot to improve the initial accuracy of muscle oxygen data collection; electromagnetic shielding materials, such as nickel-plated copper mesh, electromagnetic shielding foil, etc., are installed on key parts such as the electronic equipment compartment and sensor housing of the rehabilitation robot to block the intrusion of external electromagnetic radiation and reduce the negative impact of electromagnetic radiation; a redundant communication link design is adopted, and when the main link fails, the rehabilitation robot network system can automatically switch to the backup link within milliseconds to avoid muscle oxygen simulation interruption due to network interruption; a data verification and retransmission mechanism is introduced to perform integrity verification on each packet of simulated muscle oxygen data at the receiving end. Once an error or lost data packet is found, a retransmission request is immediately sent to the sending end to ensure that the data received by the control platform is accurate.

[0060] The ICU environmental monitoring module is used by the ICU environmental monitoring component to monitor the ICU environment to which the rehabilitation robot belongs, obtain the environmental status data of the ICU to which the rehabilitation robot belongs, evaluate the environmental characteristic impact coefficient of the ICU to which the rehabilitation robot belongs, and upload it to the rehabilitation control platform through the 5G network.

[0061] The ICU environmental monitoring component is a device system specifically used to monitor the environmental conditions of the intensive care unit (ICU). Its main function is to obtain various physical and chemical parameters in the ICU environment in real time, providing data support for evaluating the impact of the environment on rehabilitation robots and initial rehabilitation control plans. The ICU environmental monitoring component receives various analog signals from various sensors, converts them into digital signals, and then sends the data to the rehabilitation control platform via wireless communication.

[0062] The various sensors may specifically be temperature sensors, humidity sensors, electromagnetic interference detection sensors, noise sensors, gas sensors, and the like.

[0063] The specific evaluation process for evaluating the environmental characteristic impact coefficient of the ICU to which the rehabilitation robot belongs is as follows:

[0064] The ICU environmental status data of the rehabilitation robot specifically includes the real-time ambient temperature of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time ambient humidity of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time electromagnetic interference intensity of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time noise decibel value of the ICU environment to which the rehabilitation robot belongs during the evaluation period, and the real-time carbon dioxide concentration of the ICU environment to which the rehabilitation robot belongs during the evaluation period.

[0065] The evaluation cycle specifically refers to the period of time that the ICU environmental monitoring component monitors the ICU environment to which the rehabilitation robot belongs. The evaluation cycle is determined by the control management personnel based on a comprehensive analysis of factors such as the environmental status, the monitoring status of the components, and the actual monitoring needs.

[0066] The ICU environmental status data can be specifically extracted from the monitoring report of the ICU environmental monitoring component.

[0067] The ambient temperature adaptation value, the ambient humidity adaptation value and the carbon dioxide concentration adaptation value are extracted from the rehabilitation control information library.

[0068] The real-time ambient temperature, real-time ambient humidity, real-time electromagnetic interference intensity, real-time noise decibel value, and real-time carbon dioxide concentration of the ICU environment of the rehabilitation robot during the evaluation period are comprehensively analyzed to obtain the environmental characteristic influence coefficient of the ICU environment of the rehabilitation robot. The specific method is as follows:

[0069]

[0070] Where HJ is the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs, t is the time variable, t∈[t0, t1], t0 is the start time of the evaluation period, t1 is the end time of the evaluation period, β(t) is the real-time ambient temperature of the ICU environment to which the rehabilitation robot belongs at time t of the evaluation period, β′ is the ambient temperature adaptation value, γ(t) is the real-time ambient humidity of the ICU environment to which the rehabilitation robot belongs at time t of the evaluation period, γ′ is the ambient humidity adaptation value, δ(t) is the real-time electromagnetic interference intensity of the ICU environment to which the rehabilitation robot belongs at time t of the evaluation period, θ(t) is the real-time noise decibel value of the ICU environment to which the rehabilitation robot belongs at time t of the evaluation period, τ(t) is the real-time carbon dioxide concentration of the ICU environment to which the rehabilitation robot belongs at time t of the evaluation period, τ′ is the carbon dioxide concentration adaptation value, YX is the muscle oxygen simulation state evaluation value of the rehabilitation robot, and b ′ is the environmental characteristic parameter corresponding to the real-time electromagnetic interference intensity predefined in the rehabilitation control information library, b2 is the environmental characteristic parameter corresponding to the real-time noise decibel value predefined in the rehabilitation control information library, and k1 is the environmental characteristic parameter corresponding to the muscle oxygen simulation state evaluation value predefined in the rehabilitation control information library.

[0071] In this embodiment, the environmental characteristic impact coefficient of the ICU to which the rehabilitation robot belongs is used to measure the degree of influence of various factors in the ICU environment, such as temperature, humidity, electromagnetic interference, air quality, noise, etc., on the performance of the rehabilitation robot and the process of the rehabilitation robot simulating the execution of the initial rehabilitation control plan.

[0072] It needs to be explained that the real-time ambient temperature refers to the real-time temperature value of the space around the location of the rehabilitation robot during the evaluation period for evaluating the ICU environment; the ambient temperature adaptation value refers to the reference value corresponding to the preset ambient temperature; the real-time ambient humidity refers to the real-time humidity value of the space around the location of the rehabilitation robot during the evaluation period for evaluating the ICU environment; the ambient humidity adaptation value refers to the reference value corresponding to the preset ambient humidity; the real-time electromagnetic interference intensity refers to the real-time electromagnetic interference intensity value of the space around the location of the rehabilitation robot during the evaluation period for evaluating the ICU environment; the real-time noise decibel value refers to the real-time noise decibel value of the space around the location of the rehabilitation robot during the evaluation period for evaluating the ICU environment; the real-time carbon dioxide concentration refers to the real-time carbon dioxide concentration value of the space around the location of the rehabilitation robot during the evaluation period for evaluating the ICU environment; the carbon dioxide concentration adaptation value refers to the reference value corresponding to the preset carbon dioxide concentration.

[0073] The environmental characteristic parameters corresponding to the real-time electromagnetic interference intensity, the environmental characteristic parameters corresponding to the real-time noise decibel value, and the environmental characteristic parameters corresponding to the muscle oxygen simulation state assessment value are all extracted from the rehabilitation control information library, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. For example, the real-time electromagnetic interference intensity and the environmental characteristic parameters corresponding to the real-time electromagnetic interference intensity preset in the rehabilitation control information library form a mapping set, and the real-time electromagnetic interference intensity is brought into the mapping set to obtain the environmental characteristic parameters corresponding to the real-time electromagnetic interference intensity; the real-time noise decibel value and the real-time noise decibel value preset in the rehabilitation control information library The corresponding environmental characteristic parameters form a mapping set, and the real-time noise decibel value is brought into the mapping set to obtain the environmental characteristic parameters corresponding to the real-time noise decibel value; the muscle oxygen simulation state evaluation value and the environmental characteristic parameters corresponding to the muscle oxygen simulation state evaluation value preset in the rehabilitation control information library form a mapping set, and the real-time muscle oxygen simulation state evaluation value is brought into the mapping set to obtain the environmental characteristic parameters corresponding to the muscle oxygen simulation state evaluation value. In this embodiment, the value ranges of the environmental characteristic parameters corresponding to the real-time electromagnetic interference intensity, the environmental characteristic parameters corresponding to the real-time noise decibel value, and the environmental characteristic parameters corresponding to the muscle oxygen simulation state evaluation value are all (0, 1).

[0074] In this embodiment, temperature affects the performance of electronic equipment, and electronic equipment is one of the main sources of electromagnetic interference. If the ambient temperature is high and deviates from the adapted ambient temperature, the performance of the electronic components of various medical instruments and rehabilitation robots in the ICU may change, causing the electromagnetic emission frequency of these electronic equipment to drift, increasing the complexity of the electromagnetic interference intensity. At the same time, high temperature may also reduce the anti-interference ability of electronic equipment, making it more susceptible to external electromagnetic interference. Similarly, higher humidity may also cause the electromagnetic radiation frequency of electronic equipment to change, or make the equipment more susceptible to external electromagnetic interference, thereby affecting the distribution of electromagnetic interference intensity, thereby increasing the environmental characteristic influence coefficient, and bringing a greater negative impact on the execution performance of the rehabilitation robot; if there is strong electromagnetic interference, it may cause abnormal electronic equipment working state, thereby generating additional noise, resulting in an increase in the noise decibel value, and also increasing the environmental characteristics impact coefficient; in addition, the carbon dioxide concentration in the ICU environment is too high or too low, which will have a negative impact on the execution performance of the rehabilitation robot. High concentrations of carbon dioxide may corrode the electronic components inside the rehabilitation robot. In a high concentration of carbon dioxide environment, if the ambient humidity is also high, carbon dioxide will react with water vapor in the air to produce carbonic acid. If the electronic components are in a high carbon dioxide concentration environment for a long time, the aging rate of the electronic components will accelerate. If the carbon dioxide concentration is low, in order to maintain a normal carbon dioxide concentration, the ICU ventilation system may overwork, which may cause changes in the airflow speed and pressure around the ventilation equipment, thereby affecting the stability of the rehabilitation robot, thereby having a negative impact on the execution quality of the rehabilitation robot.

[0075] In this embodiment, the larger the muscle oxygen simulation state evaluation value of the rehabilitation robot, the more accurate the rehabilitation robot's simulation of the patient's muscle oxygen state and the more it can meet actual needs. A high muscle oxygen simulation state evaluation value indicates that the rehabilitation robot's system operation is more stable and accurate, which means that the robot generates less electromagnetic signal interference during data acquisition, transmission and processing. The stable operating state also reduces the rehabilitation robot's own sensitivity to external electromagnetic interference because it can better resist interference and maintain the accuracy of muscle oxygen simulation. The rehabilitation robot works in a better muscle oxygen simulation state, and its own electronic components can also operate in a more suitable temperature environment, reducing the impact of ambient temperature fluctuations on the robot. When the muscle oxygen simulation state evaluation value is large, the rehabilitation robot can simulate the state with a more reasonable movement pattern and rhythm, which can avoid additional mechanical noise caused by uneven movement or frequent adjustments to movements.

[0076] The initial plan simulation execution module is used for the rehabilitation control platform to match the initial rehabilitation control plan according to the muscle oxygen simulation state evaluation value of the rehabilitation robot. The rehabilitation control platform simulates the execution of the initial rehabilitation control plan through the remote collaborative rehabilitation robot and obtains the execution data of the initial rehabilitation control plan.

[0077] The matching obtains the initial rehabilitation control plan, and the specific matching process is as follows:

[0078] The muscle oxygen simulation state evaluation value of the rehabilitation robot is matched with the rehabilitation control initial plan corresponding to each muscle oxygen simulation state evaluation value interval predefined in the rehabilitation control information library. The specific matching process is: extracting the mapping set between the muscle oxygen simulation state evaluation value of the rehabilitation robot and the rehabilitation control initial plan from the rehabilitation control information library, determining the interval to which the muscle oxygen simulation state evaluation value of the rehabilitation robot belongs, and obtaining the rehabilitation control initial plan corresponding to the interval, thereby matching to obtain the rehabilitation control initial plan.

[0079] The initial rehabilitation control plan specifically adjusts the assistance or resistance based on the muscle oxygen saturation simulated by the rehabilitation robot. When the muscle oxygen simulation state assessment value shows that the muscle oxygen saturation is at a high level, such as 75%-85%, it indicates that the patient's muscles are well oxygenated and the muscles have a strong current tolerance. The initial rehabilitation control plan can set the rehabilitation robot to moderately increase the assistance when performing limb simulation assisted movements. Conversely, if the muscle oxygen saturation is low, such as 60%-70%, it means that the muscles may be in a relatively hypoxic state. At this time, the assistance or resistance of the robot's simulated movement should be reduced to avoid exceeding the patient's acceptance level; if the oxygen uptake rate is within the normal range, such as 20%-30%, it indicates that the patient's muscles are using oxygen efficiently, and the rehabilitation robot can proceed according to the standard rehabilitation simulation training intensity; if the muscle oxygen metabolic rate shows that the muscle consumes oxygen at a moderate rate, such as 2-3 ml / 100 g of muscle per minute, the duration of a single simulation training of the rehabilitation robot can be set to 30-40 minutes; if during the muscle oxygen simulation cycle, the various simulated muscle oxygen indicators of the rehabilitation robot fluctuate smoothly and return to normal, it is determined that the simulation training interval can be set to 1-2 days.

[0080] The initial plan judgment and adjustment module is used by the rehabilitation control platform to evaluate the execution quality indicators of the initial rehabilitation control plan based on the execution data of the initial rehabilitation control plan and the environmental characteristics influence coefficient of the ICU to which the rehabilitation robot belongs. The module compares the results with the predefined expected execution quality indicators to determine whether to make adaptive adjustments to the initial rehabilitation control plan and ultimately complete the remote collaboration and adaptive control of the ICU-AW rehabilitation robot.

[0081] Among them, the execution data of the initial rehabilitation control plan specifically includes the number of rehabilitation training completed during the execution cycle of the initial rehabilitation control plan, the feedback response time of the initial rehabilitation control plan during the execution cycle, the number of rehabilitation robot failures during the execution cycle of the initial rehabilitation control plan, and the final simulated muscle oxygen saturation of the rehabilitation robot during the execution cycle of the initial rehabilitation control plan.

[0082] The execution cycle is specifically a period of time for the rehabilitation robot to simulate the execution of the initial rehabilitation control plan. The execution cycle is specifically a period of time from the time the rehabilitation robot starts to execute the first operation of the initial rehabilitation control plan to the time the rehabilitation robot completes all operations.

[0083] The execution data of the initial rehabilitation control plan can be specifically extracted from the control report of the rehabilitation control platform.

[0084] The initial simulated muscle oxygen saturation of the rehabilitation robot during the muscle oxygen simulation cycle is obtained, wherein the initial simulated muscle oxygen saturation can be specifically extracted from the simulation report of the rehabilitation robot.

[0085] The set number of action repetitions of the rehabilitation robot within the muscle oxygen simulation cycle is matched with the fault definition times corresponding to each action repetition number interval predefined in the rehabilitation control information library. The specific matching process is: extracting a mapping set between the set number of action repetitions of the rehabilitation robot within the muscle oxygen simulation cycle and the fault definition times from the rehabilitation control information library, determining the specific interval of the set number of action repetitions of the rehabilitation robot within the muscle oxygen simulation cycle, and allocating the fault definition times corresponding to the interval to the rehabilitation robot corresponding to the set number of action repetitions, thereby matching and obtaining the fault definition times of the rehabilitation robot.

[0086] The response delay duration of the rehabilitation robot within the muscle oxygen simulation cycle is matched with the feedback response defined duration corresponding to each response delay duration interval predefined in the rehabilitation control information library. The specific matching process is: extracting a mapping set between the response delay duration of the rehabilitation robot within the muscle oxygen simulation cycle and the feedback response defined duration from the rehabilitation control information library, determining the specific interval of the response delay duration of the rehabilitation robot within the muscle oxygen simulation cycle, and obtaining the feedback response defined duration of the interval, thereby matching the feedback response defined duration of the initial rehabilitation control plan.

[0087] The preset number of rehabilitation trainings of the initial rehabilitation control plan within the execution cycle is obtained from the rehabilitation control information library.

[0088] The number of rehabilitation training completions of the initial rehabilitation control plan within the execution cycle is ratioed to the number of rehabilitation training presets within the execution cycle to obtain the rehabilitation training simulation completion rate of the initial rehabilitation control plan within the execution cycle.

[0089] The number of completed rehabilitation trainings specifically refers to the actual number of successful completions of a whole set of rehabilitation training movements or processes within the execution cycle in accordance with the requirements of the initial rehabilitation control plan. For example, the initial rehabilitation control plan stipulates that rehabilitation simulation training of upper limb strength and joint range of motion should be conducted once a day. If the rehabilitation robot completes 5 days of simulation training within a week, then the number of completed rehabilitation trainings within this week is 5 times.

[0090] The specific analysis process of the execution quality index of the initial rehabilitation control plan is as follows:

[0091] The rehabilitation training simulation completion rate of the rehabilitation control initial plan within the execution cycle, the feedback response time of the rehabilitation control initial plan within the execution cycle, the feedback response definition time of the rehabilitation control initial plan, the number of rehabilitation robot failures within the execution cycle of the rehabilitation control initial plan, the number of rehabilitation robot failure definition times, the final simulated muscle oxygen saturation of the rehabilitation robot within the execution cycle of the rehabilitation control initial plan, the initial simulated muscle oxygen saturation of the rehabilitation robot within the muscle oxygen simulation cycle, and the environmental characteristics influence coefficient of the ICU to which the rehabilitation robot belongs are comprehensively analyzed to obtain the execution quality index of the rehabilitation control initial plan. The specific method is as follows:

[0092]

[0093] Where ZX is the execution quality index of the initial rehabilitation control plan, ZX ′ It is the default indicator of the execution quality of the initial rehabilitation control plan predefined in the rehabilitation control information library. SmO is the final simulated muscle oxygen saturation of the rehabilitation robot during the execution cycle of the initial rehabilitation control plan. SmO ′ is the initial simulated muscle oxygen saturation of the rehabilitation robot during the muscle oxygen simulation cycle, CL is the rehabilitation training simulation completion rate of the initial rehabilitation control plan during the execution cycle, FK is the feedback response time of the initial rehabilitation control plan during the execution cycle, and FK ′ is the feedback response time of the initial rehabilitation control plan, GZ is the number of rehabilitation robot failures during the execution cycle of the initial rehabilitation control plan, GZ ′ is the number of fault limits of the rehabilitation robot, HJ is the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs, d1 is the execution quality influence factor corresponding to the rehabilitation training simulation completion rate predefined in the rehabilitation control information library, d2 is the execution quality influence factor corresponding to the environmental characteristic influence coefficient predefined in the rehabilitation control information library, and e is a natural constant.

[0094] It should be explained that the default execution quality index of the initial rehabilitation control plan refers to the minimum permissible value corresponding to the pre-set execution quality index of the initial rehabilitation control plan. The minimum permissible value of the execution quality index is set to ensure that the rehabilitation simulation training meets certain basic requirements, thereby ensuring that the rehabilitation training better meets the needs of patients. When the actual execution quality index is equal to the minimum permissible value, this indicates that there are major problems in the simulation execution process of the initial rehabilitation control plan, and timely adjustments are needed to make the initial rehabilitation control plan more in line with the patient's actual situation and improve the use effect of the rehabilitation robot.

[0095] The environmental characteristic impact coefficient of the ICU to which the rehabilitation robot belongs is obtained by comprehensively analyzing the real-time ambient temperature of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time ambient humidity of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time electromagnetic interference intensity of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time noise decibel value of the ICU environment to which the rehabilitation robot belongs during the evaluation period, and the real-time carbon dioxide concentration of the ICU environment to which the rehabilitation robot belongs during the evaluation period.

[0096] In this embodiment, the execution quality index of the initial rehabilitation control plan is a quantitative or evaluable standard used to measure whether the initial rehabilitation control plan achieves the expected goals and whether the execution effect is good during the actual implementation process. The larger the execution quality index of the initial rehabilitation control plan, the higher the degree of compliance with the patient during the implementation process and the greater the effect on the patient.

[0097] It should be explained that the final simulated muscle oxygen saturation of the rehabilitation robot refers to the final value of the simulated muscle oxygen saturation of the rehabilitation robot at the end of a complete cycle of rehabilitation simulation training according to the initial rehabilitation control plan; the initial simulated muscle oxygen saturation refers to the simulated muscle oxygen saturation of the rehabilitation robot before simulating the execution of the initial rehabilitation control plan; the rehabilitation training simulation completion rate refers to the ratio of the actual simulated rehabilitation training volume completed by the rehabilitation robot during the execution cycle of the initial rehabilitation control plan to the planned simulated rehabilitation training volume set in the initial plan; the feedback response time refers to the situation that requires feedback during the rehabilitation training process, such as abnormal working status of the rehabilitation robot, unsatisfactory training effect, etc. At the beginning, the rehabilitation control center makes effective feedback based on these situations, such as the time interval for adjusting the rehabilitation robot parameters and changing the training plan; the feedback response definition time refers to the maximum value corresponding to the pre-set feedback response time; the number of rehabilitation robot failures refers to the cumulative number of times the rehabilitation robot fails to work normally during the rehabilitation simulation training according to the initial rehabilitation control plan. These failures may include mechanical component damage, electronic component failure, software system crash, sensor failure and other types. Each such situation is counted as a failure; the number of rehabilitation robot failures refers to the maximum value corresponding to the pre-set number of rehabilitation robot failures.

[0098] The execution quality influencing factors corresponding to the rehabilitation training simulation completion rate and the execution quality influencing factors corresponding to the environmental characteristic influence coefficient are both extracted from the rehabilitation control information library, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. For example, the rehabilitation training simulation completion rate and the execution quality influencing factors corresponding to the rehabilitation training simulation completion rate preset in the rehabilitation control information library form a mapping set, and the real-time rehabilitation training simulation completion rate is brought into the mapping set to obtain the execution quality influencing factors corresponding to the rehabilitation training simulation completion rate; the environmental characteristic influence coefficient and the execution quality influencing factors corresponding to the environmental characteristic influence coefficient preset in the rehabilitation control information library form a mapping set, and the real-time environmental characteristic influence coefficient is brought into the mapping set to obtain the execution quality influencing factors corresponding to the environmental characteristic influence coefficient. In this embodiment, the value ranges of the execution quality influencing factors corresponding to the rehabilitation training simulation completion rate and the execution quality influencing factors corresponding to the environmental characteristic influence coefficient are both (0, 1).

[0099] In this embodiment, the feedback response time has a direct impact on the rehabilitation training simulation completion rate. If the feedback response time is short, when problems occur during the rehabilitation training process, such as deviations in the rehabilitation robot's running simulation parameters, they can be quickly adjusted and resolved, and the rehabilitation robot can continue to complete the simulation training, thereby improving the rehabilitation training simulation completion rate and improving the execution quality of the initial rehabilitation control plan. On the contrary, if the feedback response time is too long, the rehabilitation training simulation completion rate may be reduced because the problem cannot be solved in time, and the rehabilitation robot may be forced to interrupt the simulation training, resulting in a decrease in the rehabilitation training simulation completion rate; similarly, the number of rehabilitation robot failures will also directly affect the rehabilitation training simulation completion rate. Every time the rehabilitation robot fails, the rehabilitation simulation training will be interrupted, thereby reducing the completion rate of the rehabilitation training simulation and greatly reducing the execution quality indicators of the initial rehabilitation control plan. In addition, when the muscle oxygen saturation decreases instead of increases after the execution of the initial rehabilitation control plan, it means that the current initial rehabilitation control plan may cause the simulation state of the rehabilitation robot to be in a state of excessive oxygen consumption. Setting the execution quality to the minimum value allowed by the quality can timely adjust the intensity of the rehabilitation model training to prevent large deviations from the initial rehabilitation control plan. This situation also indicates that the current initial rehabilitation control plan may not be suitable for the patient's physical condition and the parameters of the initial rehabilitation control plan need to be adjusted.

[0100] In this embodiment, the smaller the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs, the less interference the ICU environment has on the rehabilitation robot. For example, lower electromagnetic interference can ensure the stable operation of the motor control system of the rehabilitation robot, so that the rehabilitation robot can work accurately according to the set assistance or resistance parameters during simulation training. The rehabilitation robot has good adaptability to the ICU environment, so that it can continuously and stably simulate the execution of the initial rehabilitation control plan, effectively improving the execution quality of the initial rehabilitation control plan; at the same time, the smaller the interference of environmental factors on the rehabilitation robot, the more accurate the rehabilitation robot will be when performing motion coordination simulation training, and the sensors and control system of the rehabilitation robot can accurately perceive its own simulated movements, thereby improving the applicability of the initial rehabilitation control plan.

[0101] The specific determination process of whether to perform adaptive adjustment on the initial rehabilitation control plan is as follows:

[0102] If the execution quality index of the initial rehabilitation control plan is equal to the default execution quality index of the initial rehabilitation control plan, a predefined optimization adjustment plan is executed on the initial rehabilitation control plan;

[0103] If the execution quality index of the initial rehabilitation control plan is not equal to the default execution quality index of the initial rehabilitation control plan, the execution quality index of the initial rehabilitation control plan will be compared with the execution quality reference index predefined in the rehabilitation control information library. If the execution quality index of the initial rehabilitation control plan is greater than the execution quality reference index, there is no need to adaptively adjust the initial rehabilitation control plan. If the execution quality index of the initial rehabilitation control plan is less than or equal to the execution quality reference index, it is necessary to adaptively adjust the initial rehabilitation control plan.

[0104] It should be explained that the default execution quality indicator of the initial rehabilitation control plan is represented by the minimum allowable value corresponding to the execution quality indicator of the initial rehabilitation control plan, and is smaller than the execution quality reference indicator.

[0105] In this embodiment, when the final simulated muscle oxygen saturation of the rehabilitation robot within the execution cycle of the initial rehabilitation control plan is greater than the initial simulated muscle oxygen saturation of the rehabilitation robot within the muscle oxygen simulation cycle, the execution quality index of the initial rehabilitation control plan is greater than the default execution quality index of the initial rehabilitation control plan; when the final simulated muscle oxygen saturation of the rehabilitation robot within the execution cycle of the initial rehabilitation control plan is less than or equal to the initial simulated muscle oxygen saturation of the rehabilitation robot within the muscle oxygen simulation cycle, the execution quality index of the initial rehabilitation control plan is equal to the default execution quality index of the initial rehabilitation control plan, that is, there is no situation where the execution quality index of the initial rehabilitation control plan is less than the default execution quality index of the initial rehabilitation control plan.

[0106] The adaptive adjustment of the initial rehabilitation control plan is carried out in the following specific process:

[0107] The execution quality index of the initial rehabilitation control plan is differenced with the execution quality reference index to obtain the execution quality deviation value of the initial rehabilitation control plan, and matched with the adaptive adjustment plan corresponding to each execution quality deviation value interval predefined in the rehabilitation control information library. The specific matching process is: extracting the mapping set between the execution quality deviation value of the initial rehabilitation control plan and the adaptive adjustment plan from the rehabilitation control information library, determining the specific interval of the execution quality deviation value of the initial rehabilitation control plan, and allocating the adaptive adjustment plan corresponding to the interval to the initial rehabilitation control plan corresponding to the execution quality deviation value, so as to match the adaptive adjustment plan of the initial rehabilitation control plan, and finally perform adaptive adjustment on the initial rehabilitation control plan.

[0108] The optimization and adjustment plan can specifically be that when the execution quality index is equal to the execution quality default index, it may mean that the current simulation training intensity is too high or too low and needs to be optimized. For the case where the simulation training intensity is too high, the simulation assistance or resistance of the rehabilitation robot can be reduced, and the number of repetitions of each set of simulation training can be reduced. If the training intensity is too low, the simulation assistance or resistance can be increased to increase the difficulty and intensity of the simulation training; it may be necessary to adjust the duration of a single simulation training or the frequency of simulation training. If the rehabilitation robot has a low training completion rate during the simulation training process, it may be necessary to shorten the single simulation training time. Conversely, if the simulation training time is too short and the initial rehabilitation control plan is not effective, the single simulation training time can be appropriately extended, and the simulation training intensity can be adjusted at the same time; the various parameters of the rehabilitation robot are calibrated to ensure its accuracy and stability, including the calibration of sensors, such as muscle oxygen sensors, force sensors, etc., so that it can more accurately perceive the physiological data and motion data during the simulation process, providing a more reliable basis for rehabilitation simulation training.

[0109] The adaptive adjustment plan can specifically be that when the execution quality index is less than or equal to the execution quality reference index, different degrees of intensity adjustment are made according to the size of the deviation value. If the deviation value is small and is in the mild deviation range, the simulated assistance or resistance of the rehabilitation robot can be appropriately fine-tuned, such as increasing or decreasing by 5%-10%, and the simulation training time can be adjusted, such as increasing or decreasing the single simulation training time by 5 minutes. If the deviation value is in the moderate deviation range, it may be necessary to adjust the simulation training intensity to a large extent, such as changing the simulated assistance or resistance by 15%-30%, and at the same time adjust the training action mode, and add some targeted simulation training actions to make up for the lack of effect of the initial rehabilitation control plan. For the severe deviation range, it may be necessary to suspend the current training simulation plan, re-evaluate the patient's physical condition and rehabilitation needs, formulate a new initial rehabilitation control plan, and gradually resume simulation training from a lower training simulation intensity; and taking into account the influence coefficient of ICU environmental characteristics, if environmental factors have a greater impact on rehabilitation training, such as electromagnetic interference causing an increase in the number of failures of the rehabilitation robot, affecting the performance of the rehabilitation robot, the adaptive adjustment plan may include taking corresponding environmental improvement measures, for example, adding an electromagnetic shielding device to reduce electromagnetic interference.

[0110] It should be explained that the optimization adjustment plan and the adaptive adjustment plan can specifically be adjusted by monitoring the muscle oxygen simulation status data of the rehabilitation robot, the movement execution accuracy data of the rehabilitation robot, and the ICU environmental data during the adjustment process to adjust the initial rehabilitation control plan. The data can all be extracted from the rehabilitation control platform.

[0111] There are a few points to note:

[0112] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0113] (2) For the sake of clarity, the thickness of layers or regions in the drawings used to describe the embodiments of the present invention are exaggerated or reduced, that is, these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or intervening elements may be present.

[0114] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to form new embodiments.

[0115] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. ICU-AW rehabilitation robot remote collaboration and adaptive control system based on 5G network, characterized by: include: The muscle oxygen simulation state assessment module is used by the patient state monitoring component to perform initial state monitoring on ICU-AW patients and collect the patient's initial muscle oxygen state data. The rehabilitation robot simulates the patient's muscle oxygen state based on the initial muscle oxygen state data, determines the rehabilitation robot's muscle oxygen simulation state assessment value, and verifies it with the predefined muscle oxygen simulation state assessment threshold to determine whether the rehabilitation robot's muscle oxygen simulation state should be optimized. The muscle oxygen simulation state assessment value of the rehabilitation robot is then uploaded to the rehabilitation control platform via the 5G network. The ICU environment monitoring module is used by the ICU environment monitoring component to monitor the ICU environment of the rehabilitation robot, obtain the environmental status data of the ICU to which the rehabilitation robot belongs, evaluate the environmental characteristic impact coefficient of the ICU to which the rehabilitation robot belongs, and upload it to the rehabilitation control platform through the 5G network; The initial plan simulation execution module is used for the rehabilitation control platform to match the rehabilitation control initial plan according to the muscle oxygen simulation state evaluation value of the rehabilitation robot. The rehabilitation control platform simulates and executes the initial rehabilitation control plan through the remote collaborative rehabilitation robot and obtains the execution data of the initial rehabilitation control plan; The initial plan judgment and adjustment module is used by the rehabilitation control platform to evaluate the execution quality indicators of the initial rehabilitation control plan based on the execution data of the initial rehabilitation control plan and the environmental characteristics of the ICU to which the rehabilitation robot belongs. The module compares the results with the predefined expected execution quality indicators to determine whether to make adaptive adjustments to the initial rehabilitation control plan, ultimately completing the remote collaboration and adaptive control of the ICU-AW rehabilitation robot. The specific determination process of determining the muscle oxygen simulation state evaluation value of the rehabilitation robot is as follows: The rehabilitation robot simulates the patient's muscle oxygenation state based on the initial muscle oxygenation state data to obtain muscle oxygenation simulation state data of the rehabilitation robot, specifically including the output power of the rehabilitation robot at each muscle oxygenation simulation time point, the number of set action repetitions of the rehabilitation robot within the muscle oxygenation simulation cycle, the response delay duration of the rehabilitation robot within the muscle oxygenation simulation cycle, and the average linearity error of the sensor of the rehabilitation robot within the muscle oxygenation simulation cycle; The output power of the rehabilitation robot at each muscle oxygen simulation time point is averaged to obtain the average output power of the rehabilitation robot during the muscle oxygen simulation period; Obtain the electromagnetic radiation intensity value of the environment of the rehabilitation robot at each muscle oxygen simulation time point; Extracting the output power adaptation average value and the set action repetition adaptation times from the rehabilitation control information library; The average output power of the rehabilitation robot during the muscle oxygen simulation cycle, the number of set action repetitions of the rehabilitation robot during the muscle oxygen simulation cycle, the response delay time of the rehabilitation robot during the muscle oxygen simulation cycle, the average linearity error of the sensor of the rehabilitation robot during the muscle oxygen simulation cycle, and the electromagnetic radiation intensity value of the environment to which the rehabilitation robot belongs at each muscle oxygen simulation time point are comprehensively processed to obtain the muscle oxygen simulation state evaluation value of the rehabilitation robot; The specific evaluation process for evaluating the environmental characteristic impact coefficient of the ICU to which the rehabilitation robot belongs is as follows: The ICU environment status data of the rehabilitation robot specifically includes the real-time ambient temperature of the ICU environment of the rehabilitation robot during the evaluation period, the real-time ambient humidity of the ICU environment of the rehabilitation robot during the evaluation period, the real-time electromagnetic interference intensity of the ICU environment of the rehabilitation robot during the evaluation period, the real-time noise decibel value of the ICU environment of the rehabilitation robot during the evaluation period, and the real-time carbon dioxide concentration of the ICU environment of the rehabilitation robot during the evaluation period; Extracting the ambient temperature adaptation value, the ambient humidity adaptation value and the carbon dioxide concentration adaptation value from the rehabilitation control information database; The real-time ambient temperature, real-time ambient humidity, real-time electromagnetic interference intensity, real-time noise decibel value and real-time carbon dioxide concentration of the ICU environment to which the rehabilitation robot belongs during the evaluation period are comprehensively analyzed to obtain the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs.

2. The 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system according to claim 1 is characterized in that: The determining whether to optimize the muscle oxygen simulation state of the rehabilitation robot is specifically to verify the muscle oxygen simulation state evaluation value of the rehabilitation robot with a predefined muscle oxygen simulation state evaluation threshold to obtain a verification result, and determining whether to optimize the muscle oxygen simulation state of the rehabilitation robot based on the verification result; The verification result is the first verification result or the second verification result; The first verification result is specifically that the muscle oxygen simulation state evaluation value of the rehabilitation robot is greater than or equal to the muscle oxygen simulation state evaluation threshold; The second verification result is specifically that the muscle oxygen simulation state evaluation value of the rehabilitation robot is less than the muscle oxygen simulation state evaluation threshold; If the verification result shows the first verification result, there is no need to optimize the muscle oxygen simulation state of the rehabilitation robot. If the verification result shows the second verification result, the muscle oxygen simulation state of the rehabilitation robot needs to be optimized.

3. The 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system according to claim 1 is characterized in that: The environmental characteristics influence coefficient of the ICU to which the rehabilitation robot belongs is analyzed in detail as follows: ； Where, is the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs, t is the time variable, , The starting time of the evaluation period. The end time of the evaluation period. is the real-time ambient temperature of the ICU environment where the rehabilitation robot belongs at the evaluation period t, is the ambient temperature adaptation value, is the real-time humidity of the ICU environment where the rehabilitation robot belongs at the evaluation period t, is the ambient humidity adaptation value, is the real-time electromagnetic interference intensity of the ICU environment where the rehabilitation robot belongs at the evaluation period t, is the real-time noise decibel value of the ICU environment where the rehabilitation robot belongs at the evaluation period t, is the real-time carbon dioxide concentration of the ICU environment where the rehabilitation robot belongs at the evaluation period t, is the adapted value of carbon dioxide concentration, is the muscle oxygen simulation state evaluation value of the rehabilitation robot, It is the environmental characteristic parameter corresponding to the real-time electromagnetic interference intensity predefined in the rehabilitation control information library. It is the environmental characteristic parameter corresponding to the real-time noise decibel value predefined in the rehabilitation control information library. Environmental characteristic parameters corresponding to the muscle oxygen simulation state evaluation values ​​predefined in the rehabilitation control information library.

4. The 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system according to claim 1 is characterized in that: The matching obtains the initial rehabilitation control plan, and the specific matching process is as follows: The muscle oxygen simulation state evaluation value of the rehabilitation robot is matched with the initial rehabilitation control plan corresponding to each predefined muscle oxygen simulation state evaluation value interval. The specific matching process is: extracting the mapping set between the muscle oxygen simulation state evaluation value of the rehabilitation robot and the initial rehabilitation control plan from the rehabilitation control information library, determining the interval to which the muscle oxygen simulation state evaluation value of the rehabilitation robot belongs, and obtaining the initial rehabilitation control plan corresponding to the interval, thereby matching to obtain the initial rehabilitation control plan.

5. The ICU-AW rehabilitation robot remote collaboration and adaptive control system based on 5G network according to claim 1 is characterized in that: The execution data of the initial rehabilitation control plan specifically includes the number of rehabilitation training completed within the execution cycle of the initial rehabilitation control plan, the feedback response duration within the execution cycle of the initial rehabilitation control plan, the number of rehabilitation robot failures within the execution cycle of the initial rehabilitation control plan, and the final simulated muscle oxygen saturation of the rehabilitation robot within the execution cycle of the initial rehabilitation control plan; Obtain the initial simulated muscle oxygen saturation of the rehabilitation robot during the muscle oxygen simulation cycle; Obtaining the preset number of rehabilitation trainings of the rehabilitation control initial plan within the execution cycle from the rehabilitation control information database; The number of rehabilitation training completions of the initial rehabilitation control plan within the execution cycle is ratioed to the number of rehabilitation training presets within the execution cycle to obtain the rehabilitation training simulation completion rate of the initial rehabilitation control plan within the execution cycle.

6. The 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system according to claim 5 is characterized in that: The specific analysis process of the execution quality indicators of the initial rehabilitation control plan is as follows: Matching the number of action repetitions of the rehabilitation robot within the muscle oxygen simulation cycle with the fault limit number corresponding to each predefined action repetition number interval, thereby obtaining the fault limit number of the rehabilitation robot; Matching the response delay duration of the rehabilitation robot within the muscle oxygen simulation cycle with the feedback response limit duration corresponding to each predefined response delay duration interval, thereby obtaining the feedback response limit duration of the initial rehabilitation control plan; A comprehensive analysis was conducted on the rehabilitation training simulation completion rate of the initial rehabilitation control plan within the execution cycle, the feedback response time of the initial rehabilitation control plan within the execution cycle, the feedback response definition time of the initial rehabilitation control plan, the number of rehabilitation robot failures within the execution cycle of the initial rehabilitation control plan, the number of rehabilitation robot failure definitions, the final simulated muscle oxygen saturation of the rehabilitation robot within the execution cycle of the initial rehabilitation control plan, the initial simulated muscle oxygen saturation of the rehabilitation robot within the muscle oxygen simulation cycle, and the influence coefficient of the environmental characteristics of the ICU to which the rehabilitation robot belongs to obtain the execution quality indicators of the initial rehabilitation control plan.

7. The 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system according to claim 1 is characterized in that: The specific process of determining whether to perform adaptive adjustment on the initial rehabilitation control plan is as follows: If the execution quality index of the initial rehabilitation control plan is equal to the default execution quality index of the initial rehabilitation control plan, a predefined optimization adjustment plan is executed on the initial rehabilitation control plan; If the execution quality index of the initial rehabilitation control plan is not equal to the default execution quality index of the initial rehabilitation control plan, the execution quality index of the initial rehabilitation control plan will be compared with the predefined execution quality reference index. If the execution quality index of the initial rehabilitation control plan is greater than the execution quality reference index, there is no need to adaptively adjust the initial rehabilitation control plan. If the execution quality index of the initial rehabilitation control plan is less than or equal to the execution quality reference index, it is necessary to adaptively adjust the initial rehabilitation control plan.

8. The 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system according to claim 7 is characterized in that: The adaptive adjustment of the initial rehabilitation control plan is carried out as follows: The execution quality index of the initial rehabilitation control plan is differenced with the execution quality reference index to obtain the execution quality deviation value of the initial rehabilitation control plan, which is matched with the adaptive adjustment plan corresponding to each predefined execution quality deviation value interval. In this way, the adaptive adjustment plan of the initial rehabilitation control plan is obtained, and finally the initial rehabilitation control plan is adaptively adjusted.

Images