Postoperative rehabilitation auxiliary monitoring system for orthopedic surgery based on artificial intelligence

Abstract

The present invention discloses an orthopedic surgery postoperative rehabilitation auxiliary monitoring system based on artificial intelligence, including a data acquisition module, a rehabilitation progress evaluation module, a motion analysis module, a risk warning module, an optimization system module and a personalized rehabilitation module. The present invention belongs to the field of rehabilitation monitoring technology, specifically an orthopedic surgery postoperative rehabilitation auxiliary monitoring system based on artificial intelligence. This scheme adopts motion analysis to decompose the posture into a standard state and an error state, considers the modeling error, calculates the posture update equation to reflect the actual posture change, and compares it with the updated standard state, performs real-time estimation and correction of the error state, and adjusts the rehabilitation treatment plan; adopts system optimization, performs attention mechanism processing to extract data features to improve training efficiency, updates the forward model parameters by minimizing the prediction error, optimizes the inverse model, constructs a curiosity loss function to realize curiosity-driven exploration, introduces intrinsic rewards, and improves rehabilitation effect and efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of rehabilitation monitoring, and in particular is an artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system. Background Art

[0002] The rehabilitation auxiliary monitoring system is a system that uses wearable devices to collect patients' physical activity data in real time, monitor muscle strength, analyze possible problems in the rehabilitation process, help doctors adjust rehabilitation plans in a timely manner, and improve the rehabilitation effect and quality of life of patients after orthopedic surgery. It aims to make the rehabilitation process more scientific and efficient, reduce the risk of uncertainty in the rehabilitation process, and enhance patients' confidence and cooperation in rehabilitation treatment. However, the existing rehabilitation auxiliary monitoring system has technical problems such as the posture data collected by wearable devices is not accurate enough, there are large errors in the measurement of patients' exercise rehabilitation posture, and it affects the follow-up of patients' rehabilitation status; there are technical problems such as the system's real-time response is not timely enough in the event of an emergency, and it is difficult for the system to accurately predict and analyze complex rehabilitation scenarios. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system provided by the present invention aims at the technical problems that the posture data collected by wearable devices is not accurate enough, there are large errors in the measurement of the patient's movement rehabilitation posture, and the follow-up of the patient's rehabilitation status is affected. Motion analysis is used to decompose the posture into a standard state and an error state, and the modeling error is considered. The posture update equation is calculated to reflect the actual posture change, and compared with the updated standard state, the error state is estimated and corrected in real time, and the rehabilitation treatment plan is adjusted; in view of the technical problems that the real-time response of the system is not timely enough when encountering an emergency, and it is difficult for the system to accurately predict and analyze complex rehabilitation scenarios, the system optimization is adopted, and the attention mechanism is used to extract data features to improve training efficiency. The forward model parameters are updated by minimizing the prediction error, the inverse model is optimized, and the curiosity loss function is constructed to realize curiosity-driven exploration, introduce intrinsic rewards, and improve rehabilitation effects and efficiency.

[0004] The artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system provided by the present invention includes a data acquisition module, a rehabilitation progress assessment module, a motion analysis module, a risk warning module, an optimization system module and a personalized rehabilitation module;

[0005] The data acquisition module is specifically installed with sensors to collect rehabilitation data;

[0006] The motion analysis module specifically decomposes the posture into a standard state and an error state, considers the modeling error, calculates the posture update equation to reflect the actual posture change, and compares it with the updated standard state, estimates and corrects the error state in real time, and adjusts the rehabilitation treatment plan;

[0007] The risk warning module specifically designs different levels of warning levels and continuously monitors the changing trend of the error state;

[0008] The optimization system module specifically performs attention mechanism processing to extract data features to improve training efficiency, updates forward model parameters by minimizing prediction errors, optimizes the inverse model, constructs a curiosity loss function to achieve curiosity-driven exploration, introduces intrinsic rewards, and improves rehabilitation effects and efficiency;

[0009] The rehabilitation progress assessment module specifically assesses physical function, life activity ability and psychological state;

[0010] The personalized rehabilitation module specifically formulates an exclusive rehabilitation plan for the patient based on the data obtained by the rehabilitation progress assessment module.

[0011] Furthermore, in the data acquisition module, there are provided a sensor installation unit and a rehabilitation data acquisition unit, and the data acquisition module includes the following contents:

[0012] Install the sensor unit, select appropriate sensors, including accelerometers, gyroscopes, and pressure sensors, correctly install the sensors on the patient's body, initialize the sensors, calibrate the parameters, and start the data acquisition function;

[0013] Collect rehabilitation data units, including the following:

[0014] Collect the movement angle, angular velocity and acceleration of the patient's limbs to understand the range and movement pattern of rehabilitation exercises;

[0015] Collect plantar pressure data to assess stress conditions;

[0016] Collect physiological data, including heart rate and respiration, to reflect the patient's physical condition;

[0017] Collect rehabilitation training data, including rehabilitation training posture data, completion status of training movements, and number of repetitions.

[0018] Furthermore, in the motion analysis module, a patient posture monitoring unit, a decomposed posture composition unit, a posture updating unit, a standard state updating unit and an error state correction unit are provided, and the motion analysis module includes the following contents:

[0019] The patient posture monitoring unit obtains the patient's data during rehabilitation exercise, monitors the patient's posture changes in real time, compares them with the normal posture range and standard rehabilitation exercise trajectory, and preliminarily determines whether the patient's movement is in line with expectations and whether there are abnormal postures and movement deviations;

[0020] Decompose the posture constituent units for better posture evaluation and correction. Decompose the posture into standard state and error state. Make it clear that the posture consists of an ideal standard state and an error state with deviations. The formula used is as follows:

[0021] ;

[0022] Wherein, X represents the overall expression after the decomposition of the attitude, including the decomposition relationship of the direction cosine matrix and the gravity acceleration vector from the carrier coordinate system to the navigation coordinate system. The carrier coordinate system represents the coordinate system established with the carrier itself as the reference, with the origin located at the carrier, and the direction of the coordinate axis is determined according to the structure and motion characteristics of the carrier. The navigation coordinate system is used to represent the position coordinate system of the carrier within a fixed geographic space. represents the direction cosine matrix from the carrier coordinate system to the navigation coordinate system, I represents the identity matrix, Represents the antisymmetric matrix composed of attitude error angles, which is used to reflect the error part of the attitude. represents the gravitational acceleration vector in the carrier coordinate system, represents the gravitational acceleration vector under standard conditions, represents the error vector of gravitational acceleration, Represents the direction cosine matrix from the carrier coordinate system to the navigation coordinate system under the standard state;

[0023] The attitude update unit takes into account the modeling error and calculates the attitude update equation to accurately reflect the change of the actual attitude over time and the influence of noise. The formula used is as follows:

[0024] ;

[0025] In the formula, represents the attitude update equation, Represents the time derivative of the direction cosine matrix, which is used to reflect the change of actual posture over time. represents the angular velocity measured in the carrier coordinate system, represents the noise associated with the gravitational acceleration, is the time constant, represents the time derivative of the gravitational acceleration vector;

[0026] The standard state update unit, when not considering noise and other errors, calculates the standard state update equation and compares it with the actual situation. The formula used is as follows:

[0027] ;

[0028] In the formula, represents the standard state update equation, Indicates the actual angular velocity;

[0029] The error state correction unit estimates and corrects the error state in real time, more accurately understands the subtle changes and improvements of patients during the rehabilitation process, and provides a basis for adjusting and optimizing the rehabilitation treatment plan.

[0030] Furthermore, in the risk warning module, different levels of warning are designed to continuously monitor the changing trend of the error status, and combine the patient's specific condition and rehabilitation stage to comprehensively judge the potential impact of the error status on the rehabilitation effect, so that medical staff can adjust the rehabilitation plan in time to ensure the safety and effectiveness of the patient's rehabilitation process.

[0031] Furthermore, in the optimization system module, an attention mechanism processing unit, a prediction error minimization unit, an inverse model updating unit, a curiosity loss function construction unit and an intrinsic reward introduction unit are provided, and the optimization system module includes the following contents:

[0032] The attention mechanism processing unit is used to quickly focus on important features in complex rehabilitation scenarios, and use the attention mechanism to process input information, including patient status information and rehabilitation action information, extract useful feature information, and ensure that the input feature space is focused on, thereby improving the training efficiency of the forward model and the reverse model. The forward model is a model that predicts the corresponding rehabilitation effect based on known input data, such as patient status and rehabilitation action. The reverse model reverses the required rehabilitation action based on the desired rehabilitation goal and formulates a plan to achieve the rehabilitation goal. The formula used is as follows:

[0033] ;

[0034] ;

[0035] In the formula, represents the predicted state at time t+1, f() represents the forward model prediction function, att() represents the attention mechanism function, represents the state at time t, represents the action at time t, represents the forward model parameters, represents the inverse model parameters, represents the action predicted by the inverse model, and g represents the inverse model prediction function;

[0036] The prediction error minimization unit is used to increase the accuracy of model predictions, make more accurate judgments when facing complex situations, and iteratively update the forward model parameters to make the calculated loss function value smaller and smaller. The formula used is as follows:

[0037] ;

[0038] In the formula, represents the loss function of the forward model, represents the next state predicted by the forward model, Represents the square of the second norm, which is used to measure the gap between the predicted value and the actual value;

[0039] Update the inverse model unit to better adapt to changes in the actual situation by reducing the error between the predicted action and the actual action. The formula used is as follows:

[0040] ;

[0041] In the formula, represents the loss function of the inverse model;

[0042] Construct a curiosity loss function unit and calculate the loss function of the curiosity-driven mechanism. The curiosity-driven mechanism is used to prompt the system to pay attention to new discoveries, which helps the system to explore more actively in complex rehabilitation environments. The formula used is as follows:

[0043] ;

[0044] In the formula, The loss function representing the curiosity-driven mechanism is used to measure whether the driving mechanism has an appropriate impact on the system behavior and balance the relationship between exploring new situations and maintaining accuracy. represents the weight coefficient, Represents the transformation of the loss function of the forward model based on the attention mechanism;

[0045] An intrinsic reward unit is introduced. The intrinsic reward is the gap between the actual next stage state and the next state obtained by the forward model. The total reward is the sum of the extrinsic reward and the intrinsic reward. The extrinsic reward is directly related to achieving the goal, and the intrinsic reward is related to the state space vector. The intrinsic reward remains relatively small in a dynamic environment and does not exceed the extrinsic reward. By setting the intrinsic reward, rehabilitation monitoring can be more accurate even in a complex rehabilitation environment, thereby reducing the deviation from the rehabilitation goal due to curiosity, better serving the postoperative rehabilitation of orthopedic surgery, and improving the rehabilitation effect and efficiency.

[0046] Furthermore, in the rehabilitation progress assessment module, a physical function assessment unit, a daily living activity ability assessment unit and a psychological state assessment unit are provided, and the rehabilitation progress assessment module includes the following contents:

[0047] The physical function assessment unit evaluates the patient's range of motion, muscle strength, endurance, balance and coordination;

[0048] The daily living activities assessment unit assesses the patient's ability to complete basic daily self-care activities, including dressing, eating, going up and down stairs, and washing;

[0049] Psychological status assessment unit, which evaluates the patient's emotional state and psychological adaptation.

[0050] Furthermore, in the personalized rehabilitation module, an exclusive rehabilitation plan is formulated for the patient based on the data obtained from the rehabilitation progress assessment module, focusing on communication and interaction with the patient, providing timely psychological support and encouragement, and enhancing the patient's confidence and enthusiasm for rehabilitation. During the implementation of the rehabilitation plan, the patient's rehabilitation progress and physical reactions are monitored in real time, and the rehabilitation plan is adjusted in time according to actual conditions to ensure the effectiveness of the rehabilitation plan.

[0051] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0052] (1) There is a technical problem that the posture data collected by wearable devices is not accurate enough, and there are large errors in the measurement of patients' exercise rehabilitation posture, which affects the follow-up of patients' rehabilitation status. Motion analysis is used to decompose the posture into standard state and error state. Considering the modeling error, the posture update equation is calculated to reflect the actual posture change and compared with the updated standard state. The error state is estimated and corrected in real time to adjust the rehabilitation treatment plan;

[0053] (2) In order to address the technical issues that the system's real-time response is not timely enough when encountering emergencies and the system has difficulty in making accurate predictions and analyses of complex rehabilitation scenarios, we use system optimization and attention mechanism processing to extract data features to improve training efficiency. We also update the forward model parameters by minimizing the prediction error, optimize the inverse model, construct a curiosity loss function to achieve curiosity-driven exploration, introduce intrinsic rewards, and improve rehabilitation effects and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of an artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system provided by the present invention;

[0055] Figure 2 is a schematic diagram of a data acquisition module;

[0056] Figure 3is a schematic diagram of the motion analysis module;

[0057] Figure 4 Schematic diagram of the optimization system modules.

[0058] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0060] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0061] Example 1, see Figure 1 The artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system provided by the present invention includes a data acquisition module, a rehabilitation progress evaluation module, a motion analysis module, a risk warning module, an optimization system module and a personalized rehabilitation module;

[0062] The data acquisition module is specifically installed with sensors to collect rehabilitation data;

[0063] The motion analysis module specifically decomposes the posture into a standard state and an error state, considers the modeling error, calculates the posture update equation to reflect the actual posture change, and compares it with the updated standard state, estimates and corrects the error state in real time, and adjusts the rehabilitation treatment plan;

[0064] The risk warning module specifically designs different levels of warning levels and continuously monitors the changing trend of the error state;

[0065] The optimization system module specifically performs attention mechanism processing to extract data features to improve training efficiency, updates forward model parameters by minimizing prediction errors, optimizes the inverse model, constructs a curiosity loss function to achieve curiosity-driven exploration, introduces intrinsic rewards, and improves rehabilitation effects and efficiency;

[0066] The rehabilitation progress assessment module specifically assesses physical function, life activity ability and psychological state;

[0067] The personalized rehabilitation module specifically formulates an exclusive rehabilitation plan for the patient based on the data obtained by the rehabilitation progress assessment module.

[0068] Example 2, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In the data acquisition module, a sensor installation unit and a rehabilitation data acquisition unit are provided. The data acquisition module includes the following contents:

[0069] Install the sensor unit, select appropriate sensors, including accelerometers, gyroscopes, and pressure sensors, correctly install the sensors on the patient's body, initialize the sensors, calibrate the parameters, and start the data acquisition function;

[0070] Collect rehabilitation data units, including the following:

[0071] Collect the movement angle, angular velocity and acceleration of the patient's limbs to understand the range and movement pattern of rehabilitation exercises;

[0072] Collect plantar pressure data to assess stress conditions;

[0073] Collect physiological data, including heart rate and respiration, to reflect the patient's physical condition;

[0074] Collect rehabilitation training data, including rehabilitation training posture data, completion status of training movements, and number of repetitions.

[0075] Example 3, see Figure 1 and Figure 3 This embodiment is based on the above embodiment. In the motion analysis module, a patient posture monitoring unit, a decomposed posture composition unit, a posture updating unit, a standard state updating unit and an error state correction unit are provided. The motion analysis module includes the following contents:

[0076] The patient posture monitoring unit obtains the patient's data during rehabilitation exercise, monitors the patient's posture changes in real time, compares them with the normal posture range and standard rehabilitation exercise trajectory, and preliminarily determines whether the patient's movement is in line with expectations and whether there are abnormal postures and movement deviations;

[0077] Decompose the posture constituent units for better posture evaluation and correction. Decompose the posture into standard state and error state. Make it clear that the posture consists of an ideal standard state and an error state with deviations. The formula used is as follows:

[0078] ;

[0079] Wherein, X represents the overall expression after posture decomposition, including the decomposition relationship of the direction cosine matrix and the gravity acceleration vector from the carrier coordinate system to the navigation coordinate system. The carrier coordinate system represents the coordinate system established with the carrier itself as the reference, with the origin located at the carrier, and the direction of the coordinate axis is determined according to the structure and motion characteristics of the carrier. The carrier is the patient himself, and the navigation coordinate system is used to represent the position coordinate system of the carrier within a fixed geographic space. represents the direction cosine matrix from the carrier coordinate system to the navigation coordinate system, I represents the identity matrix, Represents the antisymmetric matrix composed of attitude error angles, which is used to reflect the error part of the attitude. represents the gravitational acceleration vector in the carrier coordinate system, represents the gravitational acceleration vector under standard conditions, represents the error vector of gravitational acceleration, Represents the direction cosine matrix from the carrier coordinate system to the navigation coordinate system under the standard state;

[0080] The attitude update unit takes into account the modeling error and calculates the attitude update equation to accurately reflect the change of the actual attitude over time and the influence of noise. The formula used is as follows:

[0081] ;

[0082] In the formula, represents the attitude update equation, Represents the time derivative of the direction cosine matrix, which is used to reflect the change of actual posture over time. represents the angular velocity measured in the carrier coordinate system, represents the noise associated with the gravitational acceleration, is the time constant, represents the time derivative of the gravitational acceleration vector;

[0083] The standard state update unit, when not considering noise and other errors, calculates the standard state update equation and compares it with the actual situation. The formula used is as follows:

[0084] ;

[0085] In the formula, represents the standard state update equation, Indicates the actual angular velocity;

[0086] The error state correction unit estimates and corrects the error state in real time, more accurately understands the subtle changes and improvements of patients during the rehabilitation process, and provides a basis for adjusting and optimizing the rehabilitation treatment plan.

[0087] By performing the above operations, motion analysis is used to decompose the posture into a standard state and an error state. The modeling error is taken into account, and the posture update equation is calculated to reflect the actual posture change. It is compared with the updated standard state, and the error state is estimated and corrected in real time. The rehabilitation treatment plan is adjusted to solve the technical problems that the posture data collected by wearable devices is not accurate enough, there are large errors in the measurement of the patient's sports rehabilitation posture, and the follow-up of the patient's rehabilitation status is affected.

[0088] Example 4, see Figure 1 This embodiment is based on the above embodiment. In the risk warning module, different levels of warning levels are designed to continuously monitor the changing trend of the error state, and comprehensively judge the potential impact of the error state on the rehabilitation effect in combination with the patient's specific condition and rehabilitation stage, so that medical staff can adjust the rehabilitation plan in time to ensure the safety and effectiveness of the patient's rehabilitation process.

[0089] Example 5, see Figure 1 and Figure 4 This embodiment is based on the above embodiment. In the optimization system module, an attention mechanism processing unit, a prediction error minimization unit, an inverse model updating unit, a curiosity loss function construction unit and an intrinsic reward introduction unit are provided. The optimization system module includes the following contents:

[0090] The attention mechanism processing unit is used to quickly focus on important features in complex rehabilitation scenarios, and use the attention mechanism to process input information, including patient status information and rehabilitation action information, extract useful feature information, and ensure that the input feature space is focused on, thereby improving the training efficiency of the forward model and the reverse model. The forward model is a model that predicts the corresponding rehabilitation effect based on known input data, such as patient status and rehabilitation action. The reverse model reverses the required rehabilitation action based on the desired rehabilitation goal and formulates a plan to achieve the rehabilitation goal. The formula used is as follows:

[0091] ;

[0092] ;

[0093] In the formula, represents the predicted state at time t+1, f() represents the forward model prediction function, att() represents the attention mechanism function, represents the state at time t, represents the action at time t, represents the forward model parameters, represents the inverse model parameters, represents the action predicted by the inverse model, and g represents the inverse model prediction function;

[0094] The prediction error minimization unit is used to increase the accuracy of model predictions, make more accurate judgments when facing complex situations, and iteratively update the forward model parameters to make the calculated loss function value smaller and smaller. The formula used is as follows:

[0095] ;

[0096] In the formula, represents the loss function of the forward model, represents the next state predicted by the forward model, Represents the square of the second norm, which is used to measure the gap between the predicted value and the actual value;

[0097] Update the inverse model unit to better adapt to changes in the actual situation by reducing the error between the predicted action and the actual action. The formula used is as follows:

[0098] ;

[0099] In the formula, represents the loss function of the inverse model;

[0100] Construct a curiosity loss function unit and calculate the loss function of the curiosity-driven mechanism. The curiosity-driven mechanism is used to encourage the system to pay attention to new discoveries and help the system explore more actively in complex environments. The formula used is as follows:

[0101] ;

[0102] In the formula, The loss function representing the curiosity-driven mechanism is used to measure whether the driving mechanism has an appropriate impact on the system behavior and balance the relationship between exploring new situations and maintaining accuracy. represents the weight coefficient, Represents the transformation of the loss function of the forward model based on the attention mechanism;

[0103] An intrinsic reward unit is introduced. The intrinsic reward is the gap between the actual next stage state and the next state obtained by the forward model. The total reward is the sum of the extrinsic reward and the intrinsic reward. The extrinsic reward is directly related to achieving the goal, and the intrinsic reward is related to the state space vector. The intrinsic reward remains relatively small in a dynamic environment and does not exceed the extrinsic reward. Setting the intrinsic reward ensures that rehabilitation monitoring can be more accurate even in a complex rehabilitation environment, thereby reducing the deviation from the rehabilitation goal due to curiosity, better serving the postoperative rehabilitation of orthopedic surgery, and improving the rehabilitation effect and efficiency.

[0104] By performing the above operations, system optimization is adopted, and attention mechanism processing is performed to extract data features to improve training efficiency. The forward model parameters are updated by minimizing the prediction error, the inverse model is optimized, and a curiosity loss function is constructed to realize curiosity-driven exploration. Intrinsic rewards are introduced to improve rehabilitation effects and efficiency. The technical problems of the system's lack of real-time response in emergency situations and the system's difficulty in accurately predicting and analyzing complex rehabilitation scenarios are solved.

[0105] Example 6, see Figure 1 This embodiment is based on the above embodiment. In the rehabilitation progress assessment module, a physical function assessment unit, a daily living activity assessment unit and a psychological state assessment unit are provided. The rehabilitation progress assessment module includes the following contents:

[0106] The physical function assessment unit evaluates the patient's range of motion, muscle strength, endurance, balance and coordination;

[0107] The daily living activities assessment unit assesses the patient's ability to complete basic daily self-care activities, including dressing, eating, going up and down stairs, and washing;

[0108] Psychological status assessment unit, which evaluates the patient's emotional state and psychological adaptation.

[0109] Embodiment 7, see Figure 1 This embodiment is based on the above embodiment. In the personalized rehabilitation module, specifically, an exclusive rehabilitation plan is formulated for the patient based on the data obtained from the rehabilitation progress assessment module, focusing on communication and interaction with the patient, providing psychological support and encouragement in a timely manner, and enhancing the patient's rehabilitation confidence and enthusiasm. During the implementation of the rehabilitation plan, the patient's rehabilitation progress and physical reaction are monitored in real time, and the rehabilitation plan is adjusted in time according to the actual situation to ensure the effectiveness of the rehabilitation plan.

[0110] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0111] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0112] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system, characterized by: The system includes a data acquisition module, a rehabilitation progress assessment module, a motion analysis module, a risk warning module, an optimization system module and a personalized rehabilitation module; The data acquisition module is specifically installed with sensors to collect rehabilitation data; The motion analysis module specifically decomposes the posture into a standard state and an error state, considers the modeling error, calculates the posture update equation to reflect the actual posture change, and compares it with the updated standard state, estimates and corrects the error state in real time, and adjusts the rehabilitation treatment plan; The risk warning module specifically designs different levels of warning levels and continuously monitors the changing trend of the error state; The optimization system module specifically performs attention mechanism processing to extract data features, updates the forward model parameters by minimizing the prediction error, optimizes the inverse model, constructs a curiosity loss function to achieve curiosity-driven exploration, and introduces intrinsic rewards; The rehabilitation progress assessment module specifically assesses physical function, life activity ability and psychological state; The personalized rehabilitation module specifically formulates an exclusive rehabilitation plan for the patient based on the data obtained by the rehabilitation progress assessment module; In the motion analysis module, there are provided a patient posture monitoring unit, a decomposed posture composition unit, a posture updating unit, a standard state updating unit and an error state correction unit. The motion analysis module includes the following contents: Patient posture monitoring unit, which obtains data of patients during rehabilitation exercises and monitors the patient's posture changes in real time; The decomposition posture component unit is used for posture evaluation and correction. The posture is decomposed into the standard state and the error state. The formula used is as follows: ； Wherein, X represents the overall expression after the decomposition of the attitude, including the decomposition relationship of the direction cosine matrix and the gravity acceleration vector from the carrier coordinate system to the navigation coordinate system. The carrier coordinate system represents the coordinate system established with the carrier itself as the reference, with the origin located at the carrier, and the direction of the coordinate axis is determined according to the structure and motion characteristics of the carrier. The navigation coordinate system is used to represent the position coordinate system of the carrier within a fixed geographic space. represents the direction cosine matrix from the carrier coordinate system to the navigation coordinate system, I represents the identity matrix, Represents the antisymmetric matrix composed of attitude error angles, which is used to reflect the error part of the attitude. represents the gravitational acceleration vector in the carrier coordinate system, represents the gravitational acceleration vector under standard conditions, represents the error vector of gravitational acceleration, Represents the direction cosine matrix from the carrier coordinate system to the navigation coordinate system under the standard state; The attitude update unit takes into account the modeling error and calculates the attitude update equation to reflect the change of the actual attitude over time and the influence of noise. The formula used is as follows: ； In the formula, represents the attitude update equation, Represents the time derivative of the direction cosine matrix, which is used to reflect the change of actual posture over time. represents the angular velocity measured in the carrier coordinate system, represents the noise associated with the gravitational acceleration, is the time constant, represents the time derivative of the gravitational acceleration vector; The standard state update unit, when not considering noise and other errors, calculates the standard state update equation and compares it with the actual situation. The formula used is as follows: ； In the formula, represents the standard state update equation, Indicates the actual angular velocity; The error state correction unit estimates and corrects the error state in real time, understands the subtle changes and improvements of the patient during the rehabilitation process, and provides a basis for adjusting and optimizing the rehabilitation treatment plan.

2. The artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system according to claim 1 is characterized by: In the optimization system module, there are an attention mechanism processing unit, a prediction error minimization unit, an inverse model updating unit, a curiosity loss function construction unit and an intrinsic reward introduction unit. The optimization system module includes the following contents: The attention mechanism processing unit is used to quickly focus on important features in complex rehabilitation scenarios. The formula used is as follows: ； ； In the formula, represents the predicted state at time t+1, f() represents the forward model prediction function, att() represents the attention mechanism function, represents the state at time t, represents the action at time t, represents the forward model parameters, represents the inverse model parameters, represents the action predicted by the inverse model, and g represents the inverse model prediction function; Minimize the prediction error unit and iteratively update the forward model parameters to make the calculated loss function value smaller and smaller. The formula used is as follows: ； In the formula, represents the loss function of the forward model, represents the next state predicted by the forward model, Represents the square of the second norm, which is used to measure the gap between the predicted value and the actual value; Update the inverse model unit using the following formula: ； In the formula, represents the loss function of the inverse model; Construct a curiosity loss function unit and calculate the loss function of the curiosity-driven mechanism. The formula used is as follows: ； In the formula, represents the loss function of the curiosity-driven mechanism, represents the weight coefficient, Represents the transformation of the loss function of the forward model based on the attention mechanism; An intrinsic reward unit is introduced. The intrinsic reward is set according to the gap between the actual next stage state and the next state predicted by the forward model to ensure that rehabilitation monitoring can be more accurate even in complex rehabilitation environments.

3. The artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system according to claim 1 is characterized by: The data acquisition module includes a sensor installation unit and a rehabilitation data acquisition unit. The data acquisition module includes the following contents: Install the sensor unit, select appropriate sensors, including accelerometers, gyroscopes, and pressure sensors, correctly install the sensors on the patient's body, initialize the sensors, calibrate the parameters, and start the data acquisition function; Collect rehabilitation data units, including the following: Collect the movement angle, angular velocity and acceleration of the patient's limbs to understand the range and movement pattern of rehabilitation exercises; Collect plantar pressure data to assess stress conditions; Collect physiological data, including heart rate and respiration, to reflect the patient's physical condition; Collect rehabilitation training data, including rehabilitation training posture data, completion status of training movements, and number of repetitions.

4. The artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system according to claim 1 is characterized by: In the risk warning module, different levels of warning are designed to continuously monitor the changing trend of the error status, and combine the patient's specific condition and rehabilitation stage to comprehensively judge the potential impact of the error status on the rehabilitation effect, so that medical staff can adjust the rehabilitation plan in time.

5. The artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system according to claim 1 is characterized by: The rehabilitation progress assessment module includes a physical function assessment unit, a daily living activities assessment unit and a psychological state assessment unit. The rehabilitation progress assessment module includes the following contents: The physical function assessment unit evaluates the patient's range of motion, muscle strength, endurance, balance and coordination; Daily living activities assessment unit, which assesses the patient's ability to complete basic daily self-care activities; Psychological status assessment unit, which evaluates the patient's emotional state and psychological adaptation.

6. The artificial intelligence-based orthopedic surgery postoperative rehabilitation auxiliary monitoring system according to claim 1 is characterized by: In the personalized rehabilitation module, an exclusive rehabilitation plan is formulated for the patient based on the data obtained from the rehabilitation progress assessment module, focusing on communication and interaction with the patient, providing timely psychological support and encouragement, and during the implementation of the rehabilitation plan, monitoring the patient's rehabilitation progress and physical reactions in real time, and adjusting the rehabilitation plan in a timely manner according to actual conditions.

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