Intelligent auxiliary fracture monitoring system for orthopedic nursing

Abstract

The invention discloses an intelligent auxiliary fracture monitoring system for orthopedic nursing, which comprises the following steps that: the system acquires mechanical parameters, movement track data and biological signals of a fracture patient through a multi-mode sensor, and intelligently analyzes the data by using a deep learning algorithm; generating a fracture state diagnosis result and recovery trend prediction data; the low-power-consumption wireless communication module realizes real-time transmission of diagnosis data and supports remote system parameter adjustment; the personalized nursing scheme generation module generates a rehabilitation plan, a load adjustment strategy and an auxiliary device configuration scheme through an optimization algorithm based on the patient biomechanical model and the diagnosis result; and the adaptive feedback adjustment module dynamically adjusts a data acquisition strategy and processing parameters to ensure the real-time response capability of the system to the state of the patient. The system is suitable for the technical field of intelligent auxiliary monitoring, and can realize real-time monitoring, intelligent diagnosis and personalized rehabilitation guidance of fracture patients.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent auxiliary monitoring, and in particular to an intelligent auxiliary monitoring system for fractures used in orthopedic care. Background Art

[0002] With the development of medical technology and monitoring technology, real-time monitoring and auxiliary diagnosis of fracture patients in orthopedic care have become key needs. During the rehabilitation process, fracture patients need to closely monitor the fracture healing status, biomechanical changes, and rehabilitation training effects. However, existing technologies still have many deficiencies in the field of fracture monitoring and diagnosis, and it is difficult to meet the needs of personalized, real-time, and intelligent orthopedic care.

[0003] In the existing technology, traditional fracture monitoring systems are usually based on a single type of sensor or fixed monitoring parameters, supplemented by simple data analysis methods. Although these methods have solved the basic needs of fracture status monitoring to a certain extent, they have the following defects when facing complex clinical environments and diverse patient needs: 1. Insufficient comprehensiveness of monitoring data: Existing systems mostly use a single sensor to collect data and lack the collaborative capabilities of multimodal sensors. It is difficult to fully capture multi-dimensional information such as mechanics, strain, and bioelectricity, resulting in insufficient accuracy and comprehensiveness of monitoring results.

[0004] 2. Limited intelligent diagnostic capabilities: Traditional monitoring systems are mostly based on rules or simple algorithms, which make it difficult to process complex, multi-dimensional fracture status data and achieve high-precision prediction and personalized diagnosis of patients' fracture healing trends.

[0005] 3. Lack of dynamic adjustment capabilities: The existing system is unable to dynamically adjust monitoring parameters and data processing strategies according to the patient's real-time status changes, resulting in a delay in the system's response to the patient's rehabilitation needs.

[0006] 4. High energy consumption of communication module: The wireless communication modules of existing systems often consume high power, are not suitable for long-term care scenarios, and lack the ability to efficiently transmit diagnostic data remotely and provide real-time feedback.

[0007] 5. Lack of personalization in nursing plans: Traditional fracture nursing plans mostly use fixed templates, which fail to combine the patient's biomechanical characteristics and real-time status to generate personalized rehabilitation plans, making it difficult to meet the patient's diverse nursing needs.

[0008] Therefore, how to provide an intelligent auxiliary monitoring system for fractures for orthopedic care is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0009] One purpose of the present invention is to propose an intelligent auxiliary monitoring system for fractures for orthopedic care. The present invention combines multimodal sensor fusion technology, deep learning intelligent diagnosis technology, low-power wireless communication technology and personalized nursing plan generation technology, and describes in detail the algorithms and methods for intelligently realizing real-time monitoring, diagnosis and personalized rehabilitation plan generation for fracture patients. It has the advantages of high comprehensiveness, strong real-time performance, low energy consumption and high accuracy of nursing plans.

[0010] A fracture intelligent auxiliary monitoring system for orthopedic care according to an embodiment of the present invention comprises the following steps: S1, multimodal sensor fusion module, integrating acceleration sensor, strain sensor and optical fiber sensor, used to collect acceleration signal, strain distribution signal and bio-electro-optical signal of fracture patients, and process the acceleration signal, strain distribution signal and bio-electro-optical signal through fusion algorithm to generate multimodal fracture data; S2, the data processing module is connected to the multimodal sensor fusion module, and is used to receive the multimodal fracture data, and perform noise filtering, data normalization and feature parameter extraction through a preprocessing algorithm, and output a feature parameter set of the fracture state; S3, the intelligent diagnosis module is connected to the data processing module, and the fracture status recognition model built based on the deep learning algorithm is used to analyze the characteristic parameter set and generate the diagnosis results of the fracture status and the recovery trend prediction data; S4, the low-power wireless communication module is connected to the intelligent diagnosis module, and adopts a low-power wireless communication architecture to transmit the diagnosis results of the fracture status and the recovery trend prediction data to the monitoring terminal in real time, and realize the dynamic adjustment of the system parameters through two-way communication; S5. The personalized nursing plan generation module is connected with the intelligent diagnosis module, and combined with the patient's biomechanical model, the biomechanical model is constructed by the patient's personalized feature data and the diagnosis result of the fracture status. The personalized feature data includes the patient's bone density, weight distribution, fracture site characteristics and muscle distribution information, and generates a personalized nursing plan set through an optimization algorithm, including a rehabilitation exercise plan, a load adjustment strategy and auxiliary device configuration parameters; S6. The adaptive feedback adjustment module is connected to the multimodal sensor fusion module and the data processing module, and is used to dynamically adjust the data acquisition strategy of the multimodal sensor fusion module and the characteristic parameter set of the data processing module based on the real-time acquired multimodal fracture data and patient status changes.

[0011] Optionally, the S1 specifically includes: S11, multi-sensor collaborative acquisition unit, including a three-axis acceleration sensor , Micro strain sensor and Fiber Bragg Grating Sensors , which are used to collect the acceleration signal, strain distribution signal and bio-electro-optical signal of the fracture site, respectively, through the time synchronizer Time-align the collected acceleration signals, strain distribution signals and bio-electro-optical signals, and output a time series data set ; S12, the multimodal signal preprocessing unit is connected to the multi-sensor collaborative acquisition unit, and the time series data set is processed by a noise filtering algorithm and a normalization processing method. Perform denoising and standardization to generate a processed standardized data set ,in , , They represent the normalized values ​​of acceleration signal, strain distribution signal and bio-electro-optical signal respectively; S13, the multimodal feature fusion unit is connected to the multimodal signal preprocessing unit, and the standardized data set is processed based on the weighted feature fusion model Fusion to generate comprehensive feature vector : ; in, , , is the weight parameter, which is dynamically adjusted according to the fracture site, recovery stage and individual needs of the patient to meet the constraints ; S14, the dynamic adaptive calibration unit is connected with the multimodal feature fusion unit, based on the real-time feedback mechanism and deep learning optimization model, to calculate the comprehensive feature vector The weight parameter The signal fusion algorithm is adaptively optimized to correct the deviation caused by environmental interference and individual differences of patients, and generate a calibrated fusion feature vector ; S15, the fusion data generation unit is connected to the dynamic adaptive calibration unit, and the calibrated fusion feature vector Transformation into multimodal fracture data , including acceleration signals, strain distribution signals and bio-electro-optical signals at the patient's fracture site.

[0012] Optionally, the S2 specifically includes: S21, the data receiving unit is connected to the multimodal sensor fusion module to receive the multimodal fracture data set ,in Indicates at time Multimodal fracture data collected at all times, including acceleration signals, strain distribution signals, and bio-electro-optical signals; S22, noise filtering unit, uses an adaptive filtering algorithm based on multi-dimensional spatiotemporal correlation to filter the multimodal fracture data set. Each data item in Processing is performed to remove random noise and nonlinear interference using the following formula: ; in, represents the data item after noise is filtered out, is the noise component associated with the signal, is a dynamic weight factor, which is optimized and adjusted according to the signal distribution characteristics; S23, the normalization processing unit is connected to the noise filtering unit to process the noise-filtered data set. Perform normalization to normalize the signal amplitude to the standard range : ; in, represents the normalized signal amplitude, and Represents a data set The minimum and maximum values ​​of generate a normalized data set ; S24, the feature extraction unit is connected to the normalization processing unit, and based on the feature analysis model driven by deep learning, the standardized data set is Perform feature extraction and construct feature parameter sets , where each characteristic parameter Represents specific mechanical properties, strain distribution, or bioelectric activity characteristics associated with the fracture state; S25, the feature parameter output unit is connected to the feature extraction unit to collect the feature parameters Passed to the intelligent diagnosis module.

[0013] Optionally, the S3 specifically includes: S31, a characteristic parameter receiving unit connected to the data processing module, used to receive the characteristic parameter set output by the data processing module , where each characteristic parameter Represents mechanical properties, strain distribution and biological signal characteristics related to fracture status; S32, the fracture state recognition model unit is connected to the characteristic parameter receiving unit, and a multi-task neural network model is constructed based on deep learning , used to process a set of feature parameters simultaneously The fracture status classification and recovery stage prediction of the model, the output of the model includes the fracture status recognition result set And the recovery phase classification result set : ; in, is the weight parameter of the model, which is dynamically updated according to the training data; S33, the adaptive model training unit is connected to the fracture state recognition model unit, and the model weight parameters are adjusted using a loss function based on an adaptive optimization strategy. To make dynamic adjustments: ; in, represents the total loss, represents the total number of samples in the training dataset, and are the loss functions for fracture status classification and recovery stage prediction, respectively. and For the The true labels of samples, The fracture state recognition model is targeted at The classification results of the sample output are To predict the outcome for the recovery phase, and is the loss weight factor, which is used to balance the training priority between the two tasks, satisfying ; S34, the diagnosis report generation unit is connected to the fracture state recognition model unit, and is used to identify the fracture state result set according to the fracture state recognition result set. And the recovery phase classification result set Generate fracture diagnosis report ,in Includes description of fracture status, analysis of recovery stages, and assessment of potential risks; S35, recovery trend prediction unit, based on fracture diagnosis report The patient's personalized feature data, including the patient's bone density, weight distribution, fracture site characteristics and muscle distribution information, uses a time series prediction algorithm based on a recursive neural network to generate a recovery trend prediction set ,in Represents an estimate of the fracture recovery state at a future time.

[0014] Optionally, the S4 specifically includes: S41, the data encoding unit is connected to the intelligent diagnosis module to receive the fracture status diagnosis result set The fracture status diagnosis result set includes the comprehensive analysis results of the fracture status recognition result set and the recovery trend prediction set, and the encoding function is optimized through low power consumption. Compress and format the fracture status diagnosis result set to generate coded data suitable for low-power transmission : ; in, represents the optimized encoding function; S42, the low-power transmission unit is connected to the data encoding unit, adopts a dynamic power adjustment algorithm and a multi-protocol support architecture, and transmits the encoded data in real time through a low-power wireless channel To the remote monitoring terminal, the wireless channel switches to use a long-distance low-power network protocol according to the communication scenario , achieving the optimal configuration of transmission distance and power consumption; S43, the data decoding unit is deployed in the remote monitoring terminal to receive the coded data transmitted through the wireless channel , through the data decoding function Decode and restore the encoded data, and output the fracture status diagnosis result set and recovery trend prediction set: ; in, is the decoding function, and the encoding function Maintain consistency to achieve data restoration integrity; S44, the two-way communication unit is connected to the low-power transmission unit to support two-way data interaction between the monitoring terminal and the fracture intelligent auxiliary monitoring system, and the unit receives the parameter adjustment instruction set sent by the monitoring terminal , and pass the parameter adjustment instruction set to the intelligent diagnosis module to dynamically adjust the weight parameters of the fracture state recognition model and other related configuration parameters; S45, the energy consumption optimization unit is connected with the data encoding unit and the low power transmission unit, and adjusts the communication power through a dynamic optimization algorithm based on the real-time communication load, data transmission frequency and channel status. and data transmission interval , to achieve the optimal balance between energy consumption and data transmission quality: ; in, represents the energy consumption optimization function, is the packet size, The data sending interval.

[0015] Optionally, the S5 specifically includes: S51, biomechanical modeling unit, connected to the intelligent diagnosis module, for receiving patient personalized characteristic data and fracture status diagnosis result set , generating a biomechanical model of the patient : ; in, represents the biomechanical modeling function; S52, the demand analysis unit is connected with the biomechanical modeling unit for and recovery trend forecast set , analyze the patient's rehabilitation needs and generate a demand vector , where each represents the weight of rehabilitation demand, and the demand vector is used as the input of the optimization calculation; S53, the multi-objective optimization unit is connected to the demand analysis unit, based on the multi-objective optimization algorithm, the demand vector , restore trend forecast collection and a collection of possible care options Perform optimization calculations, where the feasible care plan set includes a library of care plans for different fracture types and recovery stages based on orthopedic clinical care standards, as well as alternatives to feasible care plans, to generate a personalized care plan set : ; in, To optimize the objective function, is the demand quantity, is the demand weight factor, is the first The required value, is the satisfaction function of the nursing plan for the needs, Alternatives for viable care options; S54, the dynamic scheme adjustment unit is connected with the multi-objective optimization unit to be used for the diagnosis result set of the fracture status based on real-time update and recovery trend forecast set , a collection of personalized care plans Make dynamic adjustments to ensure that the program continues to match the patient's rehabilitation needs and status changes; S55, the program output unit is connected to the dynamic program adjustment unit to collect the final optimized personalized nursing program Output to the monitoring terminal includes rehabilitation exercise plan, load adjustment strategy and auxiliary device configuration parameters.

[0016] Optionally, the S6 specifically includes: S61, the real-time data acquisition monitoring unit is connected to the multi-modal sensor fusion module to receive the multi-modal fracture data set collected in real time ,in Indicates at time Multimodal fracture data collected at all times, including mechanical property signals, strain property signals and bioelectric signals; S62, the state change trend analysis unit is connected to the data processing module, and is used to obtain the characteristic parameter set based on real-time and patient status change data set , generate state change trend vector : ; in, represents the trend analysis function, Used to describe the dynamic relationship between patient characteristic parameters and status changes; S63, the acquisition strategy optimization unit is connected to the real-time data acquisition monitoring unit and the state change trend analysis unit, and is used to , dynamically adjust the multimodal fracture data collected by the multimodal sensor fusion module , the adjusted multimodal fracture data are expressed as: ; in, It is the acquisition strategy optimization function, which is used to dynamically optimize the acquisition frequency, sensitivity and data fusion strategy of the sensor; S64, the characteristic parameter adjustment unit is connected to the data processing module, and is used to adjust the characteristic parameter according to the adjusted multi-modal fracture data. and state change trend vector , optimize the feature parameter set : ; in, To optimize the objective function, A multimodal fracture dataset The total number of data items, is the feature parameter set The total number of characteristic parameters in , It is The original feature parameters, For the feature extraction parameters, is the first in the adjusted multimodal fracture dataset data items, is the mapping function; S65, a feedback adjustment output unit is connected to the acquisition strategy optimization unit and the characteristic parameter adjustment unit to convert the optimized acquisition parameter set and feature extraction parameter set The information is fed back to the multimodal sensor fusion module and the data processing module respectively to form a closed-loop adaptive feedback system to achieve real-time dynamic adjustment.

[0017] The beneficial effects of the present invention are: (1) The present invention combines multimodal sensor fusion technology with deep learning intelligent diagnosis algorithms to achieve comprehensive collection and accurate analysis of mechanical parameters, motion trajectory data and biological signals of fracture patients, enabling the system to dynamically adapt to the real-time status changes of patients, thereby effectively improving the accuracy of fracture status monitoring and diagnosis, especially the accurate identification of complex fracture status and recovery trends.

[0018] (2) The present invention uses a low-power wireless communication module, combined with multi-protocol support and dynamic power adjustment algorithm, to achieve efficient remote transmission of fracture diagnosis results and recovery trend data, while supporting two-way communication to dynamically adjust system parameters. This not only significantly reduces communication energy consumption, making it suitable for long-term care scenarios, but also improves the system's response speed and adaptability.

[0019] (3) The present invention provides a highly personalized nursing plan by generating a personalized nursing plan module based on the patient's biomechanical model and diagnostic data and combining an optimization algorithm to dynamically generate rehabilitation exercise plans, load adjustment strategies, and auxiliary device configuration parameters, thereby significantly improving the scientificity and adaptability of the nursing plan.

[0020] (4) The present invention realizes real-time dynamic optimization of data acquisition strategy and feature extraction parameters through an adaptive feedback adjustment module, forming a closed-loop adjustment mechanism. This design ensures the system's efficient response to changes in patient status and effectively solves the problem of insufficient dynamic adjustment capabilities in traditional methods.

[0021] (5) The present invention as a whole provides a comprehensive perspective on fracture monitoring and care through the integration of multiple technologies, and can automatically realize patient monitoring, diagnosis and care plan generation, reducing dependence on human intervention. At the same time, it has high intelligence and adaptability, effectively improving the accuracy and efficiency of orthopedic care. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a general framework diagram of an intelligent auxiliary monitoring system for fractures used in orthopedic care proposed by the present invention; Figure 2 A flow chart for constructing a fracture state recognition model based on deep learning for the intelligent diagnosis module of the present invention; Figure 3 This is an optimization calculation flow chart of the personalized nursing plan generation module in the present invention. DETAILED DESCRIPTION

[0023] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0024] refer to Figure 1-3 , a fracture intelligent auxiliary monitoring system for orthopedic care, comprising the following steps: S1, multimodal sensor fusion module, integrating acceleration sensor, strain sensor and optical fiber sensor, used to collect acceleration signal, strain distribution signal and bio-electro-optical signal of fracture patients, and process the acceleration signal, strain distribution signal and bio-electro-optical signal through fusion algorithm to generate multimodal fracture data; S2, the data processing module is connected to the multimodal sensor fusion module, and is used to receive the multimodal fracture data, and perform noise filtering, data normalization and feature parameter extraction through a preprocessing algorithm, and output a feature parameter set of the fracture state; S3, the intelligent diagnosis module is connected to the data processing module, and the fracture status recognition model built based on the deep learning algorithm is used to analyze the characteristic parameter set and generate the diagnosis results of the fracture status and the recovery trend prediction data; S4, the low-power wireless communication module is connected to the intelligent diagnosis module, and adopts a low-power wireless communication architecture to transmit the diagnosis results of the fracture status and the recovery trend prediction data to the monitoring terminal in real time, and realize the dynamic adjustment of the system parameters through two-way communication; S5. The personalized nursing plan generation module is connected with the intelligent diagnosis module, and combined with the patient's biomechanical model, the biomechanical model is constructed by the patient's personalized feature data and the diagnosis result of the fracture status. The personalized feature data includes the patient's bone density, weight distribution, fracture site characteristics and muscle distribution information, and generates a personalized nursing plan set through an optimization algorithm, including a rehabilitation exercise plan, a load adjustment strategy and auxiliary device configuration parameters; S6. The adaptive feedback adjustment module is connected to the multimodal sensor fusion module and the data processing module, and is used to dynamically adjust the data acquisition strategy of the multimodal sensor fusion module and the characteristic parameter set of the data processing module based on the real-time acquired multimodal fracture data and patient status changes.

[0025] In this implementation, S1 specifically includes: S11, multi-sensor collaborative acquisition unit, including a three-axis acceleration sensor , Micro strain sensor and Fiber Bragg Grating Sensors , which are used to collect the acceleration signal, strain distribution signal and bio-electro-optical signal of the fracture site, respectively, through the time synchronizer Time-align the collected acceleration signals, strain distribution signals and bio-electro-optical signals, and output a time series data set ; S12, the multimodal signal preprocessing unit is connected to the multi-sensor collaborative acquisition unit, and the time series data set is processed by a noise filtering algorithm and a normalization processing method. Perform denoising and standardization to generate a processed standardized data set ,in , , They represent the normalized values ​​of acceleration signal, strain distribution signal and bio-electro-optical signal respectively; S13, the multimodal feature fusion unit is connected to the multimodal signal preprocessing unit, and the standardized data set is processed based on the weighted feature fusion model Fusion to generate comprehensive feature vector : ; in, , , is the weight parameter, which is dynamically adjusted according to the fracture site, recovery stage and individual needs of the patient to meet the constraints ; S14, the dynamic adaptive calibration unit is connected with the multimodal feature fusion unit, based on the real-time feedback mechanism and deep learning optimization model, to calculate the comprehensive feature vector The weight parameter The signal fusion algorithm is adaptively optimized to correct the deviation caused by environmental interference and individual differences of patients, and generate a calibrated fusion feature vector ; S15, the fusion data generation unit is connected to the dynamic adaptive calibration unit, and the calibrated fusion feature vector Transformation into multimodal fracture data , including acceleration signals, strain distribution signals and bio-electro-optical signals at the patient's fracture site.

[0026] This embodiment realizes the comprehensive collection and standardized processing of multi-dimensional data of the fracture site through a multimodal sensor collaborative acquisition unit and a signal preprocessing unit. Combined with a weighted feature fusion model and a dynamic adaptive calibration unit, it effectively improves the accuracy and robustness of the comprehensive feature vector, thereby correcting the impact of environmental interference and individual differences among patients on data quality.

[0027] In this implementation, S2 specifically includes: S21, the data receiving unit is connected to the multimodal sensor fusion module to receive the multimodal fracture data set ,in Indicates at time Multimodal fracture data collected at all times, including acceleration signals, strain distribution signals, and bio-electro-optical signals; S22, noise filtering unit, uses an adaptive filtering algorithm based on multi-dimensional spatiotemporal correlation to filter the multimodal fracture data set. Each data item in Processing is performed to remove random noise and nonlinear interference using the following formula: ; in, represents the data item after noise is filtered out, is the noise component associated with the signal, is a dynamic weight factor, which is optimized and adjusted according to the signal distribution characteristics; S23, the normalization processing unit is connected to the noise filtering unit to process the noise-filtered data set. Perform normalization to normalize the signal amplitude to the standard range : ; in, represents the normalized signal amplitude, and Represents a data set The minimum and maximum values ​​of generate a normalized data set ; S24, the feature extraction unit is connected to the normalization processing unit, and based on the feature analysis model driven by deep learning, the standardized data set is Perform feature extraction and construct feature parameter sets , where each characteristic parameter Represents specific mechanical properties, strain distribution, or bioelectric activity characteristics associated with the fracture state; S25, the feature parameter output unit is connected to the feature extraction unit to collect the feature parameters Passed to the intelligent diagnosis module.

[0028] This embodiment achieves efficient preprocessing of complex fracture data through the synergy of multimodal data reception, noise filtering, normalization processing and feature extraction units. Combined with the feature analysis model based on deep learning, it accurately extracts specific parameters related to the fracture status, significantly improving the accuracy of data processing and diagnostic efficiency.

[0029] In this implementation, S3 specifically includes: S31, a characteristic parameter receiving unit connected to the data processing module, used to receive the characteristic parameter set output by the data processing module , where each characteristic parameter Represents mechanical properties, strain distribution and biological signal characteristics related to fracture status; S32, the fracture state recognition model unit is connected to the characteristic parameter receiving unit, and a multi-task neural network model is constructed based on deep learning , used to process a set of feature parameters simultaneously The fracture status classification and recovery stage prediction of the model, the output of the model includes the fracture status recognition result set And the recovery phase classification result set : ; in, is the weight parameter of the model, which is dynamically updated according to the training data; S33, the adaptive model training unit is connected to the fracture state recognition model unit, and the model weight parameters are adjusted using a loss function based on an adaptive optimization strategy. To make dynamic adjustments: ; in, represents the total loss, represents the total number of samples in the training dataset, and are the loss functions for fracture status classification and recovery stage prediction, respectively. and For the The true labels of samples, The fracture state recognition model is targeted at The classification results of the sample output are To predict the outcome for the recovery phase, and is the loss weight factor, which is used to balance the training priority between the two tasks, satisfying ; S34, the diagnosis report generation unit is connected to the fracture state recognition model unit, and is used to identify the fracture state result set according to the fracture state recognition result set. And the recovery phase classification result set Generate fracture diagnosis report ,in Includes description of fracture status, analysis of recovery stages, and assessment of potential risks; S35, recovery trend prediction unit, based on fracture diagnosis report The patient's personalized feature data, including the patient's bone density, weight distribution, fracture site characteristics and muscle distribution information, uses a time series prediction algorithm based on a recursive neural network to generate a recovery trend prediction set ,in Represents an estimate of the fracture recovery state at a future time.

[0030] This implementation combines the multi-task neural network model constructed by deep learning and the time series prediction algorithm of the recursive neural network to achieve efficient processing of fracture status classification and recovery stage prediction. By dynamically optimizing the model weight parameters and generating a comprehensive fracture diagnosis report, it not only improves the accuracy of the diagnosis results, but also predicts the patient's recovery trend, providing reliable data support for personalized care.

[0031] In this implementation, S4 specifically includes: S41, the data encoding unit is connected to the intelligent diagnosis module to receive the fracture status diagnosis result set The fracture status diagnosis result set includes the comprehensive analysis results of the fracture status recognition result set and the recovery trend prediction set, and the encoding function is optimized through low power consumption. Compress and format the fracture status diagnosis result set to generate coded data suitable for low-power transmission : ; in, represents the optimized encoding function;

[0032] S42, the low-power transmission unit is connected to the data encoding unit, adopts a dynamic power adjustment algorithm and a multi-protocol support architecture, and transmits the encoded data in real time through a low-power wireless channel To the remote monitoring terminal, the wireless channel switches to use a long-distance low-power network protocol according to the communication scenario , achieving the optimal configuration of transmission distance and power consumption;

[0033] S43, the data decoding unit is deployed in the remote monitoring terminal to receive the coded data transmitted through the wireless channel , through the data decoding function Decode and restore the encoded data, and output the fracture status diagnosis result set and recovery trend prediction set: ;

[0034] in, is the decoding function, and the encoding function Maintain consistency to achieve data restoration integrity;

[0035] S44, the two-way communication unit is connected to the low-power transmission unit to support two-way data interaction between the monitoring terminal and the fracture intelligent auxiliary monitoring system, and the unit receives the parameter adjustment instruction set sent by the monitoring terminal , and pass the parameter adjustment instruction set to the intelligent diagnosis module to dynamically adjust the weight parameters of the fracture state recognition model and other related configuration parameters;

[0036] S45, the energy consumption optimization unit is connected with the data encoding unit and the low power transmission unit, and adjusts the communication power through a dynamic optimization algorithm based on the real-time communication load, data transmission frequency and channel status. and data transmission interval , to achieve the optimal balance between energy consumption and data transmission quality:

[0037] ;

[0038] in, represents the energy consumption optimization function, is the packet size, The data sending interval.

[0039] The present invention combines low-power optimized coding and dynamic power adjustment algorithms to achieve efficient remote transmission of fracture status diagnosis results and recovery trend data, supports multi-protocol switching to adapt to different communication scenarios, and ensures data integrity and real-time performance. Combined with an energy consumption optimization unit, it significantly reduces communication energy consumption, improves the applicability of the system in long-term care scenarios, and supports two-way communication to achieve dynamic parameter adjustment, effectively improving transmission efficiency and responsiveness.

[0040] In this implementation manner, S5 specifically includes: S51, biomechanical modeling unit, connected to the intelligent diagnosis module, for receiving patient personalized characteristic data and fracture status diagnosis result set , generating a biomechanical model of the patient : ; in, represents the biomechanical modeling function; S52, the demand analysis unit is connected with the biomechanical modeling unit for and recovery trend forecast set , analyze the patient's rehabilitation needs and generate a demand vector , where each represents the weight of rehabilitation demand, and the demand vector is used as the input of the optimization calculation; S53, the multi-objective optimization unit is connected to the demand analysis unit, based on the multi-objective optimization algorithm, the demand vector , restore trend forecast collection and a collection of possible care options Perform optimization calculations, where the feasible care plan set includes a library of care plans for different fracture types and recovery stages based on orthopedic clinical care standards, as well as alternatives to feasible care plans, to generate a personalized care plan set : ; in, To optimize the objective function, is the demand quantity, is the demand weight factor, is the first The required value, is the satisfaction function of the nursing plan for the needs, Alternatives for viable care options; S54, the dynamic scheme adjustment unit is connected with the multi-objective optimization unit to be used for the diagnosis result set of the fracture status based on real-time update and recovery trend forecast set , a collection of personalized care plans Make dynamic adjustments to ensure that the program continues to match the patient's rehabilitation needs and status changes; S55, the program output unit is connected to the dynamic program adjustment unit to collect the final optimized personalized nursing program Output to the monitoring terminal includes rehabilitation exercise plan, load adjustment strategy and auxiliary device configuration parameters.

[0041] The present invention realizes the dynamic generation and adjustment of personalized nursing plans for patients by combining biomechanical modeling, rehabilitation needs analysis and multi-objective optimization algorithms. It can not only meet the diverse rehabilitation needs of patients, but also adapt to changes in patient status in real time, provide highly accurate rehabilitation exercise plans, load adjustment strategies and auxiliary device configuration parameters, and effectively improve the scientificity and adaptability of nursing.

[0042] In this implementation manner, S6 specifically includes: S61, the real-time data acquisition monitoring unit is connected to the multi-modal sensor fusion module to receive the multi-modal fracture data set collected in real time ,in Indicates at time Multimodal fracture data collected at all times, including mechanical property signals, strain property signals and bioelectric signals; S62, the state change trend analysis unit is connected to the data processing module, and is used to obtain the characteristic parameter set based on real-time and patient status change data set , generate state change trend vector : ; in, represents the trend analysis function, Used to describe the dynamic relationship between patient characteristic parameters and status changes;

[0043] S63, the acquisition strategy optimization unit is connected to the real-time data acquisition monitoring unit and the state change trend analysis unit, and is used to , dynamically adjust the multimodal fracture data collected by the multimodal sensor fusion module , the adjusted multimodal fracture data are expressed as: ; in, It is the acquisition strategy optimization function, which is used to dynamically optimize the acquisition frequency, sensitivity and data fusion strategy of the sensor;

[0044] S64, the characteristic parameter adjustment unit is connected to the data processing module, and is used to adjust the characteristic parameter according to the adjusted multi-modal fracture data. and state change trend vector , optimize the feature parameter set : ; in, To optimize the objective function, A multimodal fracture dataset The total number of data items, is the feature parameter set The total number of characteristic parameters in , It is The original feature parameters, For the feature extraction parameters, is the first in the adjusted multimodal fracture dataset data items, is the mapping function;

[0045] S65, a feedback adjustment output unit is connected to the acquisition strategy optimization unit and the characteristic parameter adjustment unit to convert the optimized acquisition parameter set and feature extraction parameter set The information is fed back to the multimodal sensor fusion module and the data processing module respectively to form a closed-loop adaptive feedback system to achieve real-time dynamic adjustment.

[0046] This implementation method dynamically adjusts the acquisition frequency, sensitivity and data fusion strategy of the multimodal sensor through real-time data acquisition, state change trend analysis and acquisition strategy optimization, and combines characteristic parameter optimization with feedback adjustment mechanism to form a closed-loop adaptive system, thereby achieving real-time response and precise monitoring of patient state changes, greatly improving the accuracy of monitoring and dynamic adaptation capabilities.

[0047] Embodiment 1:

[0048] In order to verify the feasibility and effectiveness of the present invention, the present invention is applied to the orthopedic rehabilitation center of a Class A tertiary hospital in a certain city. The rehabilitation center is responsible for the treatment and care of various fracture patients. The fracture types of patients are complex and the recovery needs are diverse. Traditional fracture monitoring and care methods are difficult to meet the real-time, accurate and personalized needs. The system is deployed in the rehabilitation center for real-time monitoring, intelligent diagnosis and personalized care plan generation of fracture patients.

[0049] In the application scenario, the system includes a multimodal sensor fusion module, a data processing module, an intelligent diagnosis module, a low-power wireless communication module, a personalized care plan generation module, and an adaptive feedback adjustment module. The system is equipped with an integrated sensor device, including a mechanical sensor, a strain sensor, and a bioelectric sensor, which is worn on the patient's fracture site to collect the patient's fracture data in real time. The intelligent diagnosis module uses a deep learning algorithm to analyze the collected multimodal data to generate diagnostic results of the fracture status and recovery trend prediction data. The personalized care plan generation module generates an accurate care plan based on the patient's diagnostic results and biomechanical model, including rehabilitation exercise guidance and auxiliary device configuration.

[0050] The test scenario selected 10 patients in the inpatient ward of the rehabilitation center, with fractures in the femur, tibia and humerus. During the test, the patients wore the system for 14 days of rehabilitation training and recorded key data. The test content included the system's performance in real-time monitoring, diagnostic accuracy, nursing plan adaptability and energy consumption management. The data summary is shown in Table 1.

[0051] Table 1 Performance comparison of fracture intelligent auxiliary monitoring system

[0052] It can be seen from the data in Table 1 that the system of the present invention is significantly superior to traditional nursing methods in many aspects. The coverage of fracture monitoring data increased from 45.2% to 96.8%, indicating that the system can capture more comprehensive patient status information. The accuracy of fracture status diagnosis increased from 72.5% to 95.3%, thanks to the precise analysis of complex data by deep learning algorithms. Patient satisfaction with the nursing plan has increased significantly, from 65.7% to 93.4%, reflecting the scientific nature and applicability of personalized nursing plans. The completion rate of rehabilitation training increased from 70.4% to 98.1%, indicating that the nursing plan is more operational and effective. The system's low-power communication module reduces the average energy consumption of data transmission by 59%, which is very suitable for long-term monitoring.

[0053] In a specific application, a patient was tested for a tibia fracture. After wearing the system, the sensor collects mechanical property signals and bioelectric data in real time, and the data processing module automatically filters noise and extracts feature parameters. The intelligent diagnosis module analyzes the data, indicating that the healing state of the patient's fracture site has reached the mid-term, and predicts that the healing trend will stabilize in the next 7 days. The personalized care plan generation module develops a low-weight rehabilitation training plan for the patient and recommends adjusting the crutch fulcrum to reduce stress at the fracture site. During the patient's training, the adaptive feedback adjustment module dynamically adjusts the sensor's acquisition sensitivity and feature extraction parameters according to real-time data to ensure the accuracy of monitoring.

[0054] In summary, the intelligent auxiliary fracture monitoring system for orthopedic care of the present invention effectively solves the problems of incomplete monitoring, inaccurate diagnosis and inappropriate nursing plans in traditional nursing methods. It not only significantly improves the efficiency and effectiveness of fracture care, but also enhances the intelligence and adaptability of the system through multimodal data fusion and real-time feedback adjustment mechanism, providing an efficient and accurate solution for orthopedic care.

[0055] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent auxiliary monitoring system for fractures used in orthopedic care, characterized in that: include: S1, multimodal sensor fusion module, integrating acceleration sensor, strain sensor and optical fiber sensor, used to collect acceleration signal, strain distribution signal and bio-electro-optical signal of fracture patients, and process the acceleration signal, strain distribution signal and bio-electro-optical signal through fusion algorithm to generate multimodal fracture data; S2, the data processing module is connected to the multimodal sensor fusion module, and is used to receive the multimodal fracture data, and perform noise filtering, data normalization and feature parameter extraction through a preprocessing algorithm, and output a feature parameter set of the fracture state; S3, the intelligent diagnosis module is connected to the data processing module, and the fracture status recognition model built based on the deep learning algorithm is used to analyze the characteristic parameter set and generate the diagnosis results of the fracture status and the recovery trend prediction data; S4, the low-power wireless communication module is connected to the intelligent diagnosis module, and adopts a low-power wireless communication architecture to transmit the diagnosis results of the fracture status and the recovery trend prediction data to the monitoring terminal in real time, and realize the dynamic adjustment of the system parameters through two-way communication; S5. The personalized nursing plan generation module is connected with the intelligent diagnosis module, and combined with the patient's biomechanical model, the biomechanical model is constructed by the patient's personalized feature data and the diagnosis result of the fracture status. The personalized feature data includes the patient's bone density, weight distribution, fracture site characteristics and muscle distribution information, and generates a personalized nursing plan set through an optimization algorithm, including a rehabilitation exercise plan, a load adjustment strategy and auxiliary device configuration parameters; S6. The adaptive feedback adjustment module is connected to the multimodal sensor fusion module and the data processing module, and is used to dynamically adjust the data acquisition strategy of the multimodal sensor fusion module and the characteristic parameter set of the data processing module based on the real-time acquired multimodal fracture data and patient status changes.

2. The intelligent auxiliary monitoring system for fractures used in orthopedic care according to claim 1 is characterized in that: The S1 specifically includes: S11, multi-sensor collaborative acquisition unit, including a three-axis acceleration sensor , Micro strain sensor and Fiber Bragg Grating Sensors , which are used to collect the acceleration signal, strain distribution signal and bio-electro-optical signal of the fracture site, respectively, through the time synchronizer Time-align the collected acceleration signals, strain distribution signals and bio-electro-optical signals, and output a time series data set ; S12, the multimodal signal preprocessing unit is connected to the multi-sensor collaborative acquisition unit, and the time series data set is processed by a noise filtering algorithm and a normalization processing method. Perform denoising and standardization to generate a processed standardized data set ,in , , They represent the normalized values ​​of acceleration signal, strain distribution signal and bio-electro-optical signal respectively; S13, the multimodal feature fusion unit is connected to the multimodal signal preprocessing unit, and the standardized data set is processed based on the weighted feature fusion model Fusion to generate comprehensive feature vector : ； in, , , is the weight parameter, which is dynamically adjusted according to the fracture site, recovery stage and individual needs of the patient to meet the constraints ; S14, the dynamic adaptive calibration unit is connected with the multimodal feature fusion unit, based on the real-time feedback mechanism and deep learning optimization model, to calculate the comprehensive feature vector The weight parameter The signal fusion algorithm is adaptively optimized to correct the deviation caused by environmental interference and individual differences of patients, and generate a calibrated fusion feature vector ; S15, the fusion data generation unit is connected to the dynamic adaptive calibration unit, and the calibrated fusion feature vector Conversion to multimodal fracture data , including acceleration signals, strain distribution signals and bio-electro-optical signals at the patient's fracture site.

3. The intelligent auxiliary monitoring system for fractures used in orthopedic care according to claim 2 is characterized in that: The S2 specifically includes: S21, the data receiving unit is connected to the multimodal sensor fusion module to receive the multimodal fracture data set ,in Indicates at time Multimodal fracture data collected at all times, including acceleration signals, strain distribution signals, and bio-electro-optical signals; S22, noise filtering unit, uses an adaptive filtering algorithm based on multi-dimensional spatiotemporal correlation to filter the multimodal fracture data set. Each data item in Processing is performed to remove random noise and nonlinear interference using the following formula: ； in, represents the data item after filtering out noise, is the noise component associated with the signal, is a dynamic weight factor, which is optimized and adjusted according to the signal distribution characteristics; S23, the normalization processing unit is connected to the noise filtering unit to process the noise-filtered data set. Perform normalization to normalize the signal amplitude to the standard range : ； in, represents the normalized signal amplitude, and Represents data sets The minimum and maximum values ​​of generate a normalized data set ; S24, the feature extraction unit is connected to the normalization processing unit, and based on the feature analysis model driven by deep learning, the standardized data set is Perform feature extraction and construct feature parameter sets , where each characteristic parameter Represents specific mechanical properties, strain distribution, or bioelectric activity characteristics associated with the fracture state; S25, the feature parameter output unit is connected to the feature extraction unit to collect the feature parameters Passed to the intelligent diagnosis module.

4. The intelligent auxiliary monitoring system for fractures used in orthopedic care according to claim 3 is characterized in that: The S3 specifically includes: S31, a characteristic parameter receiving unit connected to the data processing module, used to receive the characteristic parameter set output by the data processing module , where each characteristic parameter Represents mechanical properties, strain distribution and biological signal characteristics related to fracture status; S32, the fracture state recognition model unit is connected to the characteristic parameter receiving unit, and a multi-task neural network model is constructed based on deep learning , used to process a set of feature parameters simultaneously The fracture status classification and recovery stage prediction of the model, the output of the model includes the fracture status recognition result set And the recovery phase classification result set : ； in, is the weight parameter of the model, which is dynamically updated according to the training data; S33, the adaptive model training unit is connected to the fracture state recognition model unit, and the model weight parameters are adjusted using a loss function based on an adaptive optimization strategy. To make dynamic adjustments: ； in, represents the total loss, represents the total number of samples in the training dataset, and are the loss functions for fracture status classification and recovery stage prediction, respectively. and For the The true labels of samples, The fracture state recognition model is targeted at The classification results of the sample output are To predict the outcome for the recovery phase, and is the loss weight factor, which is used to balance the training priority between the two tasks, satisfying ; S34, the diagnosis report generation unit is connected to the fracture state recognition model unit, and is used to identify the fracture state result set according to the fracture state recognition result set. And the recovery phase classification result set Generate fracture diagnosis report ,in Includes description of fracture status, analysis of recovery stages, and assessment of potential risks; S35, recovery trend prediction unit, based on fracture diagnosis report The patient's personalized feature data, including the patient's bone density, weight distribution, fracture site characteristics and muscle distribution information, uses a time series prediction algorithm based on a recursive neural network to generate a recovery trend prediction set ,in Represents an estimate of the fracture recovery state at a future time.

5. The intelligent auxiliary monitoring system for fractures used in orthopedic care according to claim 4 is characterized in that: The S4 specifically includes: S41, the data encoding unit is connected to the intelligent diagnosis module to receive the fracture status diagnosis result set The fracture status diagnosis result set includes the comprehensive analysis results of the fracture status recognition result set and the recovery trend prediction set, and the encoding function is optimized through low power consumption. Compress and format the fracture status diagnosis result set to generate coded data suitable for low-power transmission : ； in, represents the optimized encoding function; S42, the low-power transmission unit is connected to the data encoding unit, adopts a dynamic power adjustment algorithm and a multi-protocol support architecture, and transmits the encoded data in real time through a low-power wireless channel To the remote monitoring terminal, the wireless channel switches to use a long-distance low-power network protocol according to the communication scenario , achieving the optimal configuration of transmission distance and power consumption; S43, the data decoding unit is deployed in the remote monitoring terminal to receive the coded data transmitted through the wireless channel , through the data decoding function Decode and restore the encoded data, and output the fracture status diagnosis result set and recovery trend prediction set: ； in, is the decoding function, and the encoding function Maintain consistency to achieve data restoration integrity; S44, the two-way communication unit is connected to the low-power transmission unit to support two-way data interaction between the monitoring terminal and the fracture intelligent auxiliary monitoring system, and the unit receives the parameter adjustment instruction set sent by the monitoring terminal , and pass the parameter adjustment instruction set to the intelligent diagnosis module to dynamically adjust the weight parameters of the fracture state recognition model and other related configuration parameters; S45, the energy consumption optimization unit is connected with the data encoding unit and the low power transmission unit, and adjusts the communication power through a dynamic optimization algorithm based on the real-time communication load, data transmission frequency and channel status. and data transmission interval , to achieve the optimal balance between energy consumption and data transmission quality: ； in, represents the energy consumption optimization function, is the packet size, The data sending interval.

6. The intelligent auxiliary monitoring system for fractures used in orthopedic care according to claim 5, characterized in that: The S5 specifically includes: S51, biomechanical modeling unit, connected to the intelligent diagnosis module, for receiving patient personalized characteristic data and fracture status diagnosis result set , generating a biomechanical model of the patient : ； in, represents the biomechanical modeling function; S52, the demand analysis unit is connected with the biomechanical modeling unit for and recovery trend forecast set , analyze the patient's rehabilitation needs and generate a demand vector , where each represents the weight of rehabilitation demand, and the demand vector is used as the input of the optimization calculation; S53, the multi-objective optimization unit is connected to the demand analysis unit, based on the multi-objective optimization algorithm, the demand vector , restore trend forecast collection and a collection of possible care options Perform optimization calculations, where the feasible care plan set includes a library of care plans for different fracture types and recovery stages based on orthopedic clinical care standards, as well as alternatives to feasible care plans, to generate a personalized care plan set : ； in, To optimize the objective function, is the demand quantity, is the demand weight factor, is the first The required value, is the satisfaction function of the nursing plan for the needs, Alternatives for viable care options; S54, the dynamic scheme adjustment unit is connected with the multi-objective optimization unit to be used for the diagnosis result set of the fracture status based on real-time update and recovery trend forecast set , a collection of personalized care plans Make dynamic adjustments to ensure that the program continues to match the patient's rehabilitation needs and status changes; S55, the program output unit is connected to the dynamic program adjustment unit to collect the final optimized personalized nursing program Output to the monitoring terminal includes rehabilitation exercise plan, load adjustment strategy and auxiliary device configuration parameters.

7. The intelligent auxiliary monitoring system for fractures used in orthopedic care according to claim 3 is characterized in that: The S6 specifically includes: S61, the real-time data acquisition monitoring unit is connected to the multi-modal sensor fusion module to receive the multi-modal fracture data set collected in real time ,in Indicates at time Multimodal fracture data collected at all times, including mechanical property signals, strain property signals and bioelectric signals; S62, the state change trend analysis unit is connected to the data processing module, and is used to obtain the characteristic parameter set based on real-time and patient status change data set , generate state change trend vector : ； in, represents the trend analysis function, Used to describe the dynamic relationship between patient characteristic parameters and status changes; S63, the acquisition strategy optimization unit is connected to the real-time data acquisition monitoring unit and the state change trend analysis unit, and is used to , dynamically adjust the multimodal fracture data collected by the multimodal sensor fusion module , the adjusted multimodal fracture data are expressed as: ； in, It is the acquisition strategy optimization function, which is used to dynamically optimize the acquisition frequency, sensitivity and data fusion strategy of the sensor; S64, the characteristic parameter adjustment unit is connected to the data processing module, and is used to adjust the characteristic parameter according to the adjusted multi-modal fracture data. and state change trend vector , optimize the feature parameter set : ； in, To optimize the objective function, A multimodal fracture dataset The total number of data items, is the feature parameter set The total number of characteristic parameters in , It is The original feature parameters, For the feature extraction parameters, is the first in the adjusted multimodal fracture dataset data items, is the mapping function; S65, a feedback adjustment output unit is connected to the acquisition strategy optimization unit and the characteristic parameter adjustment unit to convert the optimized acquisition parameter set and feature extraction parameter set The information is fed back to the multimodal sensor fusion module and the data processing module respectively to form a closed-loop adaptive feedback system to achieve real-time dynamic adjustment.

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