Robotic-based assisted rehabilitation method and system

Abstract

The application discloses a robot-based assisted rehabilitation method and system, relates to the technical field of assisted rehabilitation, and if the scene abnormality degree exceeds expectation, the length of a rehabilitation observation period is constrained according to the scene abnormality degree, rehabilitation state data of a patient is collected to identify rehabilitation requirements of the patient, and corresponding rehabilitation schemes are matched for the patient according to the rehabilitation requirements; if the number of abnormal sign indexes existing in the patient exceeds expectation after the rehabilitation robot executes the corresponding rehabilitation scheme, a multi-level alarm mechanism is triggered and multi-level alarm instructions are sent to the outside; the environment factors in a rehabilitation area are automatically adjusted by the rehabilitation robot, if the rehabilitation target of the patient has not been achieved, the rehabilitation scheme is optimized according to the re-determined rehabilitation requirements, or the rehabilitation robot is intervened according to detection data. The rehabilitation state of the patient is monitored and evaluated in real time, which is used as feedback to adjust the control strategy of the rehabilitation scheme and the rehabilitation robot.

Description

Technical Field

[0001] The present invention relates to the field of assisted rehabilitation technology, and in particular to a robot-based assisted rehabilitation method and system. Background Art

[0002] Robot-assisted rehabilitation is an advanced rehabilitation treatment method that integrates modern robotics technology and rehabilitation medicine theory. By utilizing sophisticated robotic equipment, combined with professional rehabilitation medicine knowledge and personalized treatment plans, this method can provide patients with precise, efficient, and customized rehabilitation training services. Compared with traditional rehabilitation treatments, robot-assisted rehabilitation can more accurately simulate human movement, thereby more effectively promoting patients' muscle recovery, joint flexibility improvement, and neurological function reconstruction. In addition, this method can significantly reduce the workload of rehabilitation therapists and improve the efficiency and quality of overall rehabilitation treatment. For patients, robot-assisted rehabilitation not only provides a more scientific and intelligent rehabilitation experience, but also accelerates the rehabilitation process, improves quality of life, and creates favorable conditions for their return to society and independent living.

[0003] In the Chinese invention patent application publication number CN114496159A, a robot-assisted rehabilitation method, system, device and medium are disclosed. The method includes: collecting the user's EEG signal and starting timing; preprocessing the user's EEG signal to obtain a preprocessed EEG signal; performing feature extraction processing on the preprocessed EEG signal to obtain feature data; determining whether a training error-related potential occurs based on the feature data and the trained model; if so, sending a signal requiring assistance to the robot to control the robot to operate according to the signal requiring assistance; if not, determining whether the recorded time reaches a set time threshold; if the time threshold is reached, sending a signal not requiring assistance to the robot to control the robot to remain motionless; if the time threshold is not reached, continuing to collect the user's EEG signal; the method proposed by the present invention can realize automatic assisted rehabilitation.

[0004] When a patient is in a state of rehabilitation, considering the need to improve the patient's rehabilitation efficiency and save the caregiver's time, a rehabilitation robot is usually introduced in the patient's rehabilitation area to assist in rehabilitation. For example, the rehabilitation robot collects and monitors the patient's various physical signs and behavioral data in real time. When the patient's physical signs and behavioral data are abnormal, an alarm is promptly issued to the outside and the intervention of the guardian is introduced to ensure the patient's rehabilitation effect.

[0005] In existing rehabilitation robot-assisted rehabilitation methods, medical staff usually develop personalized rehabilitation plans for patients in advance, which are then assisted in execution by rehabilitation robots. The patient's rehabilitation data is used as feedback to optimize and correct the rehabilitation plan until the patient reaches the expected rehabilitation goal. However, when there are certain interference conditions in the rehabilitation area, such as noise, light conditions, temperature and humidity or other environmental factors in the rehabilitation area, it is difficult for the rehabilitation robot to adjust the current rehabilitation process and rehabilitation environment in real time based on the patient's physical signs and behavioral status data. Therefore, when controlling the rehabilitation robot to assist the patient in rehabilitation, the patient's rehabilitation process will be affected to a certain extent.

[0006] To this end, the present invention provides a robot-based assisted rehabilitation method and system. Summary of the Invention

[0007] (1) Technical problems solved

[0008] To address the shortcomings of existing technologies, the present invention provides a robot-based assisted rehabilitation method and system. This system matches patients with appropriate rehabilitation plans based on their rehabilitation needs. After the rehabilitation robot executes the plan, if the number of abnormal physical signs exceeds expectations, a multi-level alarm mechanism is triggered and a multi-level alarm command is issued externally. The rehabilitation robot automatically adjusts environmental factors within the rehabilitation area. If the patient's rehabilitation goals are still not achieved, the rehabilitation plan is optimized based on the redefined rehabilitation needs, or the rehabilitation robot is intervened based on detection data. The patient's rehabilitation status is monitored and evaluated in real time, and this feedback is used to adjust the rehabilitation plan and the control strategy of the rehabilitation robot. This system thus resolves the technical problems described in the background art.

[0009] (2) Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0011] A robot-based assisted rehabilitation method includes collecting rehabilitation scene data within a rehabilitation area, generating a scene abnormality degree from the rehabilitation scene data, and issuing a health assessment instruction to an external party if the scene abnormality degree exceeds expectations;

[0012] The length of the rehabilitation observation period is constrained based on the degree of scene abnormality. After collecting the patient's rehabilitation status data, the patient's rehabilitation needs are identified and the corresponding rehabilitation plan is matched to the patient based on the rehabilitation needs.

[0013] After the rehabilitation robot executes the corresponding rehabilitation plan, if the number of abnormal physical signs of the patient exceeds the expected number, the abnormal physical sign emergency treatment knowledge graph will provide an emergency treatment plan based on the abnormal physical sign indicators, triggering a multi-level alarm mechanism and issuing a multi-level alarm instruction to the outside world;

[0014] After analyzing and obtaining the key factors that affect the patient's recovery, the rehabilitation robot automatically adjusts the environmental factors in the rehabilitation area. If the patient's rehabilitation goals are still not achieved, a three-level alarm command is issued to the outside world;

[0015] The rehabilitation plan is optimized based on the re-determined rehabilitation needs, or the rehabilitation robot is comprehensively tested and corresponding test data is obtained, and intervention is performed on the rehabilitation robot based on the test data.

[0016] Furthermore, a sensor network is arranged in the rehabilitation area, and environmental parameters are collected using the sensor network. The acquired environmental parameters are summarized to generate a rehabilitation scene data set; the rehabilitation scene data in the rehabilitation scene data set is used as input, and the trained scene condition evaluation model is used to output the scene abnormality degree in the rehabilitation area.

[0017] Furthermore, after receiving the health assessment instruction, the length of the rehabilitation observation period cT is constrained according to the abnormality degree Co of several recent consecutive scenes, and the length of the rehabilitation observation period is adjusted according to the constraint conditions. The constraint method is as follows:

[0018]

[0019] Weight coefficient, 0≤α≤1, 0≤β≤1; n is the number of time nodes, Co i is the abnormality of the scene at the i-th time node, Co a is its average value.

[0020] Furthermore, the patient's physical signs, mental health data, and behavioral data in the current rehabilitation scenario are continuously monitored and collected, and summarized as the patient's rehabilitation status data; using the patient's rehabilitation status data as input, the trained rehabilitation needs identification model is used to identify rehabilitation needs, and the corresponding rehabilitation plan is matched to the patient based on the rehabilitation needs.

[0021] Furthermore, the rehabilitation robot executes the corresponding rehabilitation plan in the patient rehabilitation area and collects the patient's vital sign data after a preset rehabilitation observation period; using the patient's vital sign data as input, the trained abnormal indicator recognition model is used to identify the patient's abnormal vital sign indicators;

[0022] If the number of abnormal vital signs of the patient exceeds expectations, a first-level alarm command will be issued to the outside.

[0023] Furthermore, after receiving the first-level alarm command, the emergency treatment of abnormal physical signs of rehabilitation patients is used as the target word, and a knowledge graph of emergency treatment of abnormal physical signs is pre-built; the abnormal physical sign indicators of patients during the rehabilitation observation period are collected, and the rehabilitation robot sends the emergency treatment plan to the cloud.

[0024] Furthermore, after executing the emergency response plan, if the number of consecutive first-level alarm instructions received exceeds the expected number, a risk value R is generated based on the status data of the first-level alarm instructions received. t ;

[0025] If the risk value R is obtained t If the risk threshold is exceeded, a secondary alarm command will be issued to the outside world, and the patient's recovery degree data, vital signs data and environmental condition data will be recorded.

[0026] Furthermore, under dimensionless conditions, the risk value R is generated based on the status data of the first-level alarm instruction received. t The way is as follows:

[0027]

[0028] Where: T i is the time interval between the i-th and i+1-th alarms, N i is the number of abnormal indicators at the time of the i-th alarm, γ is the time attenuation coefficient, α and β are weight coefficients, and γ is the attenuation coefficient of the cumulative impact.

[0029] Furthermore, after receiving the second-level alarm instruction, principal component analysis is used to analyze the environmental condition data in the rehabilitation area to obtain the key factors affecting the patient's recovery, and corresponding qualified intervals are set for each key factor according to the degree of influence of the key factor;

[0030] During the rehabilitation observation period, various vital signs data of the patient are collected as feedback data. The feedback data is used as input, and the trained patient rehabilitation evaluation model is used to output the rehabilitation value. If the obtained rehabilitation value is lower than the rehabilitation threshold, a three-level alarm instruction is issued to the outside.

[0031] Furthermore, if the third-level alarm instruction is not received, the patient's rehabilitation status data is re-collected, the patient's rehabilitation needs are identified again, and the rehabilitation plan is optimized based on the re-determined rehabilitation needs, and the rehabilitation robot executes the optimized rehabilitation plan.

[0032] Furthermore, if a third-level alarm instruction is received, the rehabilitation robot is comprehensively inspected and corresponding inspection data is obtained, which are summarized to generate a rehabilitation robot inspection data set; a control coefficient is generated from the inspection data of the rehabilitation robot inspection data set. If the control coefficient does not exceed the preset response threshold, the rehabilitation robot stops the current task, and the robot operation is guided or directly controlled through remote control.

[0033] The robot-based assisted rehabilitation system includes a scene analysis unit that collects rehabilitation scene data within the rehabilitation area, generates a scene abnormality degree based on the rehabilitation scene data, and issues a health assessment instruction to the outside if the scene abnormality degree exceeds expectations;

[0034] The rehabilitation plan generation unit constrains the length of the rehabilitation observation period based on the degree of scene abnormality, collects the patient's rehabilitation status data, identifies the patient's rehabilitation needs, and matches the patient with a corresponding rehabilitation plan based on the rehabilitation needs;

[0035] Multi-level alarm unit: After the rehabilitation robot executes the corresponding rehabilitation plan, if the number of abnormal physical signs of the patient exceeds the expected number, the abnormal physical sign emergency treatment knowledge map will provide an emergency treatment plan based on the abnormal physical sign indicators, triggering the multi-level alarm mechanism and issuing a multi-level alarm command to the outside world;

[0036] The adjustment unit analyzes and obtains the key factors affecting the patient's recovery. The rehabilitation robot then automatically adjusts the environmental factors in the rehabilitation area. If the patient's rehabilitation goal is still not achieved, a three-level alarm command is issued to the outside world.

[0037] The feedback processing unit optimizes the rehabilitation plan according to the re-determined rehabilitation needs, or performs a comprehensive inspection of the rehabilitation robot and obtains corresponding inspection data, and intervenes in the rehabilitation robot according to the inspection data.

[0038] (3) Beneficial effects

[0039] The present invention provides a robot-based assisted rehabilitation method and system, which has the following beneficial effects:

[0040] 1. Judge and evaluate the degree of abnormality in the rehabilitation area based on the scene abnormality. When the current rehabilitation area is not conducive to patient recovery, adjust the environment in the rehabilitation area to avoid negative impact on the patient's rehabilitation process.

[0041] 2. Adjust the pre-set rehabilitation observation period, and readjust the patient's rehabilitation plan or the control strategy of the rehabilitation robot after the rehabilitation observation period, using the patient's environmental conditions as feedback to ensure the patient's rehabilitation effect.

[0042] 3. Based on data analysis and identification, the abnormality of the patient's current indicators can be judged, and rehabilitation needs analysis and rehabilitation plan formulation can be completed. By formulating targeted rehabilitation plans and assisting with rehabilitation robots, the patient's rehabilitation efficiency and effectiveness can be improved.

[0043] 4. Identify abnormal physical signs and indicators to monitor and alarm the patient's rehabilitation effect, and intervene in time when the patient's rehabilitation effect is not good; use the patient's abnormal physical signs and indicators as feedback, and provide emergency treatment plans based on the abnormal physical signs emergency treatment knowledge map as a reference and executed or guided by the rehabilitation robot to ensure the patient's rehabilitation effect.

[0044] 5. By building a multi-level alarm mechanism and issuing alarms multiple times, the patient's recovery status can be monitored and evaluated in real time when the patient is in the recovery state, and this can be used as feedback to adjust the rehabilitation plan and the control strategy of the rehabilitation robot to ensure the patient's rehabilitation effect.

[0045] 6. Use rehabilitation robots to issue control commands, or use trained automatic control models to control various equipment in the rehabilitation area to reduce the interference of environmental conditions on patient rehabilitation. By evaluating the patient's rehabilitation effect, the reliability of the current rehabilitation process can be verified, and a three-level alarm command can be issued when the rehabilitation effect does not exceed expectations, enriching the current multi-level alarm mechanism.

[0046] 7. Re-detect and identify the patient's various status data. After the rehabilitation robot executes the new rehabilitation plan, it switches to the current rehabilitation stage and adjusts the patient's rehabilitation stage. By constructing a control coefficient, the response effect of the rehabilitation robot is evaluated and the rehabilitation robot executing the rehabilitation plan is adjusted. When the current rehabilitation effect is not good, positive feedback can be formed in time to ensure the patient's rehabilitation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic flow chart of the robot-based assisted rehabilitation method of the present invention;

[0048] Figure 2 This is a schematic structural diagram of the robot-based assisted rehabilitation system of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] See also Figure 1 The present invention provides a robot-based assisted rehabilitation method, comprising:

[0051] Step 1: Collect rehabilitation scene data in the rehabilitation area, generate scene abnormality from the rehabilitation scene data, and issue a health assessment instruction to the outside if the scene abnormality exceeds expectations;

[0052] The step 1 includes the following:

[0053] Step 101: After the patient enters a rehabilitation state and the patient's rehabilitation area is determined, a sensor network, such as a light sensor, a temperature and humidity sensor, and a noise sensor, is deployed in the rehabilitation area. The sensor network is used to monitor and collect environmental parameters such as light, noise, temperature, humidity, and air quality in real time. The acquired environmental parameters are aggregated to generate a rehabilitation scene data set.

[0054] Step 102: After training the machine learning algorithm with the labeled sample data, a trained scene condition assessment model is obtained; using the rehabilitation scene data as input, the trained scene condition assessment model is used to evaluate the degree of abnormality of the rehabilitation environment in the rehabilitation area, and the corresponding scene abnormality degree is output;

[0055] Based on historical data and management expectations for patient rehabilitation, abnormal thresholds are pre-set. If the acquired scene abnormality exceeds the abnormal threshold, it means that the current environmental conditions are not conducive to the patient's rehabilitation. A rehabilitation robot can be introduced in the current rehabilitation scenario to issue health assessment instructions to the outside world.

[0056] When using, combine the contents in steps 101 to 102:

[0057] After the patient enters the rehabilitation state, by collecting environmental condition data in the rehabilitation area and conducting a comprehensive abnormality analysis, the degree of abnormality in the rehabilitation area is judged and evaluated based on the abnormality of the scene to see whether it will interfere with the patient's rehabilitation effect. When the current rehabilitation area is not conducive to the patient's rehabilitation, the environment in the rehabilitation area is adjusted, or the rehabilitation plan is optimized, etc., to avoid the negative impact of abnormal rehabilitation scenes on the patient's rehabilitation process.

[0058] In existing rehabilitation robot-assisted rehabilitation methods, medical staff usually develop personalized rehabilitation plans for patients in advance, which are then assisted in execution by rehabilitation robots. The patient's rehabilitation data is used as feedback to optimize and correct the rehabilitation plan until the patient reaches the expected rehabilitation goal. However, when there are certain interference conditions in the rehabilitation area, such as noise, light conditions, temperature and humidity or other environmental factors in the rehabilitation area, it is difficult for the rehabilitation robot to adjust the current rehabilitation process and rehabilitation environment in real time according to the patient's physical signs and behavioral status. Therefore, when controlling the rehabilitation robot to assist the patient in rehabilitation, the rehabilitation process will be affected to a certain extent.

[0059] Step 2: Constrain the length of the rehabilitation observation period based on the scene abnormality, collect the patient's rehabilitation status data, identify the patient's rehabilitation needs, and match the patient with a corresponding rehabilitation plan based on the rehabilitation needs;

[0060] The second step includes the following:

[0061] Step 201: After receiving the health assessment instruction, the length of the rehabilitation observation period cT is constrained based on the abnormality levels Co of several recent consecutive scenes. The length of the rehabilitation observation period is adjusted based on the constraint conditions. The constraint method is as follows:

[0062]

[0063] Weight coefficient, 0≤α≤1, 0≤β≤1; n is the number of time nodes, Co i is the abnormality of the scene at the i-th time node, Co a is its average value;

[0064] During use, considering that environmental conditions may interfere with the patient's rehabilitation status and rehabilitation effect, in this scenario, the pre-set rehabilitation observation period can be adjusted. After the rehabilitation observation period, the patient's rehabilitation plan or the control strategy of the rehabilitation robot can be readjusted, thereby completing the preliminary construction of the feedback mechanism based on the patient's environmental conditions as feedback, and ensuring the patient's rehabilitation effect;

[0065] Step 202: The rehabilitation robot or wearable device continuously monitors and collects the patient's vital signs, such as heart rate, respiratory rate, and electromyography; mental health data, such as anxiety level and mood swings; and behavioral data in the current rehabilitation scenario, and aggregates the data to generate patient rehabilitation status data.

[0066] The machine learning algorithm is trained with the labeled sample data to obtain a trained rehabilitation needs recognition model;

[0067] Using the patient's rehabilitation status data as input, the trained rehabilitation needs identification model is used to identify the patient's rehabilitation needs and match the patient with a corresponding rehabilitation plan based on the rehabilitation needs.

[0068] When using, combine the contents in steps 201 and 202:

[0069] When introducing rehabilitation robots to assist patients in rehabilitation, wearable devices and imaging devices collect various data of the patient in the rehabilitation area. Based on data analysis and identification methods, the abnormality of the patient's current indicators is judged, and rehabilitation needs analysis and rehabilitation plan formulation are completed. By formulating targeted rehabilitation plans and assisting with rehabilitation robots, the patient's rehabilitation efficiency and effectiveness can be improved.

[0070] Step 3: After the rehabilitation robot executes the corresponding rehabilitation plan, if the number of abnormal physical signs of the patient exceeds the expected number, the abnormal physical sign emergency treatment knowledge graph will provide an emergency treatment plan based on the abnormal physical sign indicators, triggering a multi-level alarm mechanism and issuing a multi-level alarm instruction to the outside world;

[0071] The step three includes the following:

[0072] Step 301: The rehabilitation robot executes the corresponding rehabilitation program in the patient rehabilitation area and collects the patient's vital sign data, such as heart rate, respiratory rate, blood sugar and oxygen level, and mobility data, after a preset rehabilitation observation period.

[0073] After training the convolutional neural network with the labeled sample data, a trained abnormal indicator recognition model is obtained;

[0074] Taking the patient's vital signs data as input, the trained abnormal indicator recognition model is used to identify the patient's abnormal vital signs. If the number of abnormal vital signs exceeds the expected number, a first-level alarm instruction is issued to the outside world.

[0075] When in use, after the rehabilitation program is implemented, the patient's various data are used as feedback to identify abnormal physical signs and indicators, so as to monitor and alarm the patient's rehabilitation effect, and timely intervention can be made when the patient's rehabilitation effect is not good;

[0076] Step 302: After receiving the first-level alarm instruction, the abnormal physical signs emergency treatment of recovered patients is used as the target word. After deep search and entity relationship building, a knowledge graph of abnormal physical signs emergency treatment is pre-built;

[0077] Collect abnormal physical signs of patients during the rehabilitation observation period. Based on the correspondence between abnormal physical signs and emergency plans, an emergency treatment plan is given from the abnormal physical sign emergency treatment knowledge graph. The rehabilitation robot sends the emergency treatment plan to the patient, family members, medical staff, etc., or sends the emergency treatment plan to the cloud.

[0078] When in use, considering that intervention is needed when the patient's rehabilitation effect is poor, the patient's abnormal physical signs indicators are used as feedback, and the emergency treatment plan is given by the abnormal physical signs emergency treatment knowledge map. This is used as a reference and executed or guided by the rehabilitation robot to ensure the patient's rehabilitation effect.

[0079] Step 303: After the rehabilitation robot, medical staff or rehabilitation robot executes the emergency treatment plan, if the number of consecutive first-level alarm instructions received exceeds the expected number, a risk value R is generated based on the status data of the first-level alarm instructions received. t , generated as follows:

[0080]

[0081] Where: T i is the time interval between the i-th and i+1-th alarms, N iis the number of abnormal indicators at the time of the i-th alarm, γ is the time attenuation coefficient, which ranges from 0.01 to 1, α and β are weight coefficients, which are consistent with the previous values, γ is the attenuation coefficient of the cumulative impact, which ranges from 0.01 to 1, and e can be 2.713;

[0082] Pre-set risk thresholds based on historical data and patient recovery expectations;

[0083] If the risk value obtained exceeds the risk threshold, a secondary alarm command is issued to the outside world to notify the patient, medical staff and family members, and record alarm-related data, such as the patient's recovery data, vital signs data and environmental conditions data when the secondary alarm command is issued;

[0084] When using, combine the contents in steps 301 to 303:

[0085] Construct the risk value R based on the first-level alarm instruction t , according to the risk value R t Issue a secondary alarm command to complete the construction of a multi-level alarm mechanism. By building a multi-level alarm mechanism and issuing alarms multiple times, the patient's rehabilitation status can be monitored and evaluated in real time when the patient is in a rehabilitation state, and this can be used as feedback to adjust the rehabilitation plan and the control strategy of the rehabilitation robot to ensure the patient's rehabilitation effect.

[0086] Step 4: After analyzing and obtaining the key factors that affect the patient's recovery, the rehabilitation robot automatically adjusts the environmental factors in the rehabilitation area. If the patient's rehabilitation goal is still not achieved, a three-level alarm command is issued to the outside world;

[0087] The step 4 includes the following contents:

[0088] Step 401: After receiving the secondary alarm instruction, principal component analysis is used to analyze the environmental condition data in the rehabilitation area to obtain key factors affecting the patient's rehabilitation;

[0089] Set the corresponding qualified interval for each key factor according to its influence; train the machine learning algorithm with the labeled sample data to obtain the trained automatic control model;

[0090] The rehabilitation robot uses the trained automatic control model to automatically adjust environmental factors in the rehabilitation area, such as reducing light intensity and noise to increase patient comfort.

[0091] When in use, considering that the environmental conditions in the rehabilitation area may affect the patient's rehabilitation process, as a processing strategy, using a rehabilitation robot to issue control instructions, or using a trained automatic control model to control various equipment in the rehabilitation area, such as humidifiers or audio equipment in the rehabilitation area, can reduce the interference of environmental conditions on patient rehabilitation.

[0092] Step 402: Collect various vital sign data of the patient as feedback data during the rehabilitation observation period, such as heart rate, respiratory rate, blood sugar and oxygen level, and mobility data;

[0093] The machine learning algorithm is trained with the labeled sample data to obtain a trained patient rehabilitation evaluation model. The trained patient rehabilitation evaluation model uses the feedback data as input to evaluate the patient's current rehabilitation status and output a rehabilitation value, which can be used to verify the patient's degree of recovery.

[0094] Based on historical data and expectations for patient rehabilitation management, a rehabilitation threshold is pre-set. If the obtained rehabilitation value is lower than the rehabilitation threshold, it means that the patient has not achieved the expected effect after the intervention of the rehabilitation robot and the implementation of the targeted rehabilitation plan. At this time, a three-level alarm command is issued to the outside world.

[0095] When using, combine the contents in steps 401 and 402:

[0096] After making adaptive adjustments to the key influencing factors within the environmental conditions and completing the current rehabilitation process, the reliability of the current rehabilitation process can be verified by evaluating the patient's rehabilitation effect. At this time, when the rehabilitation effect does not exceed expectations, a third-level alarm instruction is issued, enriching the current multi-level alarm mechanism.

[0097] Step 5: Optimize the rehabilitation plan based on the re-determined rehabilitation needs, or conduct a comprehensive test on the rehabilitation robot and obtain corresponding test data, and intervene in the rehabilitation robot based on the test data;

[0098] The step five includes the following:

[0099] Step 501: If no third-level alarm command is received, after re-collecting the patient's rehabilitation status data, the rehabilitation needs identification model is used to identify the patient's rehabilitation needs again, and the rehabilitation plan is optimized based on the re-determined rehabilitation needs. The rehabilitation robot then executes the optimized rehabilitation plan.

[0100] When in use, when the rehabilitation effect in the current stage reaches the expected effect, the patient's various status data are re-detected and identified, and the rehabilitation robot implements the new rehabilitation plan to switch the current rehabilitation stage and adjust the patient's rehabilitation stage;

[0101] Step 502: If a level 3 alarm instruction is received, a comprehensive inspection of the rehabilitation robot is performed and corresponding inspection data, such as control accuracy data and response speed, is obtained, and the data is aggregated to generate a rehabilitation robot inspection data set;

[0102] The control coefficient Kcp is generated from the rehabilitation robot test data set as follows: linear normalization is performed on the control accuracy Jo and response speed Fo, and the corresponding data values ​​are mapped to the interval [0,1] according to the following method:

[0103]

[0104] Weight coefficient: 0≤F1≤1, 0≤F2≤1 and F2+F1=1; Jo i is the control accuracy of the i-th test node, Jo a is the mean of control accuracy, Jo b is the qualified standard value of control accuracy; Fo i is the response speed of the i-th test node, Fo a is the mean response speed, Fo b is the qualified standard value of response speed;

[0105] If the control coefficient Kcp does not exceed the preset response threshold, the rehabilitation robot stops the current task and performs robot operation guidance or direct control through remote control;

[0106] When using, combine the contents in steps 501 and 502:

[0107] As another aspect of feedback, different from switching between rehabilitation stages, when the rehabilitation effect fails to meet expectations, considering that there may be deficiencies in the control strategy or control method of the rehabilitation robot, the response effect of the rehabilitation robot is evaluated by constructing a control coefficient. With this as a reference, the rehabilitation robot that executes the rehabilitation plan can be adjusted. When the current rehabilitation effect is not good, positive feedback can be formed in time to ensure the patient's rehabilitation effect.

[0108] The construction method of the knowledge graph for emergency treatment of abnormal physical signs of rehabilitation patients can refer to the following content:

[0109] Clarify the goal of building a knowledge graph

[0110] First, we need to clarify the goal of building a knowledge graph, which is to represent and reason about the knowledge of emergency treatment for abnormal physical signs of rehabilitation patients. This includes but is not limited to basic patient information, descriptions of abnormal physical signs, emergency treatment measures, and evaluation of treatment effects.

[0111] Data collection and preprocessing

[0112] Data Source: Data on emergency management of abnormal physical signs in rehabilitation patients published by rehabilitation hospitals, rehabilitation centers, and relevant research institutions, including case reports, expert experience, and clinical guidelines, are collected. Data Preprocessing: The collected data is cleaned, deduplicated, and formatted to ensure accuracy and consistency.

[0113] Knowledge Extraction

[0114] Entity extraction: Identify entities related to emergency treatment of abnormal physical signs in recovered patients from preprocessed data, such as the patient's name, age, gender, disease type, name of abnormal physical sign, and emergency treatment measures. Relationship extraction: Leveraging technologies such as natural language processing, extract relationships between entities from text, such as "Patient A suffers from disease B," "The abnormal physical sign of disease B is C," and "The emergency treatment measure for abnormal physical sign C is D." Attribute extraction: Extract attributes from entities, such as the patient's vital signs, the severity of abnormal physical signs, and the specific steps of emergency treatment measures.

[0115] Knowledge Fusion

[0116] Entity alignment: Align identical entities across data from different sources to ensure unique entities within the knowledge graph. Relationship integration: Integrate extracted relationships to eliminate redundancy and conflict, forming a complete relationship network. Attribute filling: Fill in and complete entity attributes to ensure the integrity and accuracy of the knowledge graph.

[0117] Knowledge graph construction

[0118] Ontology Construction: Based on the knowledge domain of emergency treatment for abnormal physical signs in rehabilitation patients, an ontology model is constructed to define the types and hierarchical structure of entities, relationships, and attributes. Knowledge Representation: Knowledge in the knowledge graph is represented using triples (entity-relationship-entity) or (entity-attribute-attribute value). Knowledge Storage: A suitable database or graph database is selected to store the knowledge graph to ensure efficient knowledge retrieval and reasoning.

[0119] Quality assessment and optimization

[0120] Accuracy Assessment: The accuracy of the knowledge in the knowledge graph is assessed through methods such as manual sampling to ensure its correctness. Consistency Assessment: The relationships in the knowledge graph are checked for consistency to avoid inconsistencies. Completeness Assessment: The completeness of the knowledge graph is assessed to ensure that it covers the key knowledge points for emergency treatment of abnormal physical signs in rehabilitation patients. Optimization: Based on the assessment results, the knowledge graph is optimized, including correcting errors and supplementing missing knowledge points.

[0121] See also Figure 2 The present invention provides a robot-based assisted rehabilitation system, comprising:

[0122] a scene analysis unit, collecting rehabilitation scene data in a rehabilitation area, generating a scene abnormality degree from the rehabilitation scene data, and issuing a health assessment instruction to the outside if the scene abnormality degree exceeds an expectation;

[0123] a rehabilitation scheme generation unit, constraining a length of a rehabilitation observation period according to the scene abnormality degree, identifying a rehabilitation demand of a patient after collecting patient rehabilitation state data, and matching a corresponding rehabilitation scheme for the patient according to the rehabilitation demand;

[0124] a multi-level alarm unit, giving an emergency treatment scheme according to an abnormal sign index from an abnormal sign emergency treatment knowledge graph if the number of abnormal sign indexes of the patient exceeds an expectation after the rehabilitation robot executes the corresponding rehabilitation scheme, triggering a multi-level alarm mechanism and issuing a multi-level alarm instruction to the outside;

[0125] an adjustment unit, automatically adjusting environmental factors in the rehabilitation area by the rehabilitation robot after analyzing and obtaining key factors affecting the rehabilitation of the patient, and issuing a three-level alarm instruction to the outside if the rehabilitation target of the patient is still not achieved;

[0126] a feedback processing unit, optimizing the rehabilitation scheme according to the re-determined rehabilitation demand, or comprehensively detecting the rehabilitation robot and obtaining corresponding detection data, and intervening the rehabilitation robot according to the detection data.

[0127] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0128] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only for some logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0130] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0131] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A robot-based assisted rehabilitation method, characterized by: include, Collect rehabilitation scene data in the rehabilitation area, generate scene abnormality from the rehabilitation scene data, and issue a health assessment instruction to the outside if the scene abnormality exceeds expectations; The length of the rehabilitation observation period is constrained based on the degree of scene abnormality. After collecting the patient's rehabilitation status data, the patient's rehabilitation needs are identified and the corresponding rehabilitation plan is matched to the patient based on the rehabilitation needs. After the rehabilitation robot executes the corresponding rehabilitation plan, if the number of abnormal physical signs of the patient exceeds the expected number, the abnormal physical sign emergency treatment knowledge graph will give an emergency treatment plan based on the abnormal physical sign indicators, triggering the multi-level alarm mechanism and issuing corresponding multi-level alarm instructions to the outside world; The rehabilitation robot executes the corresponding rehabilitation plan in the patient rehabilitation area and collects the patient's vital sign data after a pre-set rehabilitation observation period. The trained abnormal indicator recognition model uses the patient's vital sign data as input to identify the patient's abnormal vital sign indicators. If the number of abnormal vital sign indicators of the patient exceeds the expected number, a first-level alarm command is issued to the outside world. After analyzing and obtaining the key factors that affect the patient's recovery, the rehabilitation robot automatically adjusts the environmental factors in the rehabilitation area. If the patient's rehabilitation goals are still not achieved, a three-level alarm command is issued to the outside world; Optimize the rehabilitation plan based on the re-determined rehabilitation needs, or conduct a comprehensive test on the rehabilitation robot and obtain corresponding test data, and intervene in the rehabilitation robot based on the test data; After receiving the health assessment instruction, the abnormality of several consecutive scenes is Rehabilitation observation period The length of the rehabilitation observation period is constrained and the length of the rehabilitation observation period is adjusted according to the constraint conditions. The constraint method is as follows: ； Weight coefficient, , ; is the number of time nodes, It is is the scene abnormality degree of the time node, is its average value; After executing the emergency response plan, if the number of consecutive first-level alarm instructions received exceeds the expected number, a risk value is generated based on the status data of the first-level alarm instructions received. ; If the risk value is obtained If the risk threshold is exceeded, a secondary alarm command will be issued to the outside world, and the patient's recovery data, vital signs data and environmental conditions data will be recorded; Under dimensionless conditions, the risk value is generated based on the status data of the first-level alarm instruction received. The way is as follows: ； Where: is the time interval between the i-th and i+1-th alarms, is the number of abnormal indicators at the time of the i-th alarm, is the time attenuation coefficient, and are weight coefficients, is the attenuation coefficient of the cumulative impact.

2. The robot-assisted rehabilitation method according to claim 1, characterized in that: Arrange a sensor network in the rehabilitation area and use the sensor network to collect environmental parameters; The acquired environmental parameters are summarized to generate a rehabilitation scene data set. The rehabilitation scene data in the rehabilitation scene data set is used as input, and the trained scene condition evaluation model is used to output the scene abnormality degree within the rehabilitation area.

3. The robot-assisted rehabilitation method according to claim 2, characterized in that: Continuously monitor and collect the patient's physical signs, mental health data, and behavioral data in the current rehabilitation scenario, and summarize them as the patient's rehabilitation status data; use the patient's rehabilitation status data as input, use the trained rehabilitation needs identification model to identify rehabilitation needs, and match the patient with the corresponding rehabilitation plan based on the rehabilitation needs.

4. The robot-assisted rehabilitation method according to claim 3, characterized in that: After receiving the first-level alarm command, the emergency treatment of abnormal physical signs of rehabilitation patients is used as the target word, and a knowledge graph of emergency treatment of abnormal physical signs is pre-built; the abnormal physical sign indicators of patients during the rehabilitation observation period are collected, and the rehabilitation robot sends the emergency treatment plan to the cloud.

5. The robot-assisted rehabilitation method according to claim 4, characterized in that: After receiving the second-level alarm command, principal component analysis is used to analyze the environmental condition data in the rehabilitation area to obtain the key factors that affect the patient's recovery, and corresponding qualified intervals are set for each key factor based on the degree of influence of the key factor; During the rehabilitation observation period, various vital signs data of the patient are collected as feedback data. The feedback data is used as input, and the trained patient rehabilitation evaluation model is used to output the rehabilitation value. If the obtained rehabilitation value is lower than the rehabilitation threshold, a three-level alarm instruction is issued to the outside.

6. The robot-assisted rehabilitation method according to claim 5, characterized in that: If the third-level alarm command is not received, the patient's rehabilitation status data is collected again, the patient's rehabilitation needs are identified again, and the rehabilitation plan is optimized based on the re-determined rehabilitation needs. The rehabilitation robot then executes the optimized rehabilitation plan. If a level 3 alarm command is received, the rehabilitation robot is comprehensively inspected and corresponding inspection data is obtained, which are summarized to generate a rehabilitation robot inspection data set; a control coefficient is generated from the inspection data of the rehabilitation robot inspection data set. If the control coefficient does not exceed the preset response threshold, the rehabilitation robot stops the current task and the robot operation is guided or directly controlled through remote control.

7. A robot-based assisted rehabilitation system, applying the assisted rehabilitation method according to any one of claims 1 to 6, characterized in that: include, The scene analysis unit collects rehabilitation scene data in the rehabilitation area and generates scene abnormality from the rehabilitation scene data. If the scene abnormality exceeds expectations, a health assessment instruction is issued to the outside world. The rehabilitation plan generation unit constrains the length of the rehabilitation observation period based on the degree of scene abnormality, collects the patient's rehabilitation status data, identifies the patient's rehabilitation needs, and matches the patient with a corresponding rehabilitation plan based on the rehabilitation needs; Multi-level alarm unit: After the rehabilitation robot executes the corresponding rehabilitation plan, if the number of abnormal physical signs of the patient exceeds the expected number, the abnormal physical sign emergency treatment knowledge map will provide an emergency treatment plan based on the abnormal physical sign indicators, triggering the multi-level alarm mechanism and issuing a multi-level alarm command to the outside world; The adjustment unit analyzes and obtains the key factors affecting the patient's recovery. The rehabilitation robot then automatically adjusts the environmental factors in the rehabilitation area. If the patient's rehabilitation goal is still not achieved, a three-level alarm command is issued to the outside world. The feedback processing unit optimizes the rehabilitation plan according to the re-determined rehabilitation needs, or performs a comprehensive inspection of the rehabilitation robot and obtains corresponding inspection data, and intervenes in the rehabilitation robot according to the inspection data.

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