A rapid rehabilitation control system for shoulder surgery that incorporates pain management pathways

Abstract

This invention discloses a rapid rehabilitation control system for shoulder surgery that integrates analgesia pathways. The system first acquires signals in real time through a multimodal physiological sensor network to construct a dynamic functional spectrum of the shoulder reflecting pain and functional status, identifying core areas of functional abnormalities. Then, it controls a targeted drug infusion system, combining a multiphysics coupling model and a model predictive control algorithm to generate a personalized analgesia pathway that matches the patient's physiological response. Next, by analyzing the spatiotemporal correlation between the functional spectrum and the analgesia pathway, a comprehensive rehabilitation confidence score quantifying rehabilitation potential is calculated. Finally, based on the comprehensive rehabilitation confidence score, a safe, effective, and patient-tolerable personalized rehabilitation training instruction set is generated using a multi-objective rehabilitation optimization model, driving the rehabilitation equipment to execute the instructions. This invention has the following advantages and effects: it achieves precise spatiotemporal matching and dynamic synergy between analgesia and rehabilitation training, thereby significantly improving rehabilitation efficiency and quality while ensuring safety.

Description

Technical Field

[0001] This invention relates to the field of medical rehabilitation technology, and in particular to a rapid rehabilitation control system for shoulder surgery that incorporates analgesia pathways. Background Technology

[0002] Traditional postoperative rehabilitation management models typically treat analgesia and functional training as two relatively independent, sequential subsystems. In terms of analgesia, the management system generally employs protocols based on systemic drug infusion or fixed-target local nerve blocks. This approach is essentially a "static" intervention strategy; the drug dosage, infusion site, and rate are usually pre-set based on preoperative planning or general clinical guidelines, making it impossible for the management system to dynamically adjust according to the subject's real-time, localized physiological state and tissue function changes. As a result, the analgesic effect output by the system is either insufficient, causing the subject to be unable to complete effective range or intensity of movement during rehabilitation training due to discomfort; or excessive, leading to unnecessary sensorimotor inhibition or even systemic effects, which also hinder the rehabilitation process. Meanwhile, the development of rehabilitation training subsystem protocols often relies on periodic manual assessments (such as manual muscle strength tests or joint range of motion measurements every few days) and standardized rehabilitation protocols. This "delayed" assessment and "fixed" protocol make the logic of generating rehabilitation training instructions completely unable to capture the microscopic, dynamic changes in the subject's shoulder functional state. There is a significant information gap between the issuance of training instructions and the subject's current actual functional potential. This could lead to the training subsystem applying excessive pressure to the functional area before it has been adequately analgesic, resulting in undesirable conditions such as defensive muscle spasms; it could also lead to failure to apply effective stimulation in time during the window of functional recovery, missing the optimal opportunity for systemic intervention.

[0003] While existing technologies have attempted to incorporate wearable sensors for motion capture or use smart pumps for programmed drug delivery, these improvements are mostly localized and isolated. They fail to construct a complete technical system capable of interpreting multimodal physiological signals in real time and achieving closed-loop feedback control. Specifically, existing systems cannot convert multidimensional physiological signals (such as electromyography, impedance, and kinematic parameters) captured by sensor networks into precise spatial localization and quantitative assessment of functionally abnormal areas in real time; furthermore, they fail to correlate and couple this dynamic assessment with the spatial distribution of targeted analgesics in real time, thereby determining the optimal intensity and pattern of rehabilitation stimulation that a specific functional area can safely tolerate under specific analgesic support.

[0004] Therefore, the existing technology system is essentially missing a core capability: to dynamically and adaptively coordinate the planning of control strategies for "analgesia pathways" and "rehabilitation pathways" based on real-time, high-resolution perception of the physiological state of the shoulder, so as to ensure that the appropriate level of analgesia is provided at the right time and in the right place, and that appropriate rehabilitation training is performed.

[0005] This system-level disconnect and lack of coordination not only leads to a longer functional recovery period and an increased risk of re-injury, but also technically limits the improvement of the level of automation and personalization in rehabilitation. Summary of the Invention

[0006] The purpose of this invention is to provide a rapid rehabilitation control system for shoulder surgery that incorporates analgesia pathways, in order to solve the problems mentioned in the background art.

[0007] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0008] A rapid rehabilitation control system for shoulder surgery that incorporates analgesia pathways, comprising:

[0009] The dynamic perception and assessment module is configured to simultaneously collect multidimensional physiological signals through a multimodal physiological sensing network deployed on the object's shoulder and related muscle groups, and construct a dynamic functional spectrum of the shoulder reflecting pain and functional status in real time based on the shoulder biomechanical model and real-time signal feature extraction network, thereby identifying the core area of ​​functional abnormality.

[0010] The analgesia pathway planning module is configured to control the targeted drug infusion system based on the identified functional abnormality core area. It simulates the spatial distribution of analgesic drugs in shoulder tissue through low-dose exploratory infusion and a multi-physics coupling model based on finite element analysis, and dynamically adjusts the infusion parameters using a model predictive control algorithm to generate a personalized analgesia pathway that matches the individual's physiological response.

[0011] The rehabilitation training integration module is configured to establish a spatiotemporal correlation mapping between the shoulder dynamic function spectrum and the personalized analgesia pathway, and to analyze it using a multi-source information fusion model to output a comprehensive rehabilitation confidence score that can quantify the current rehabilitation potential.

[0012] The adaptive decision-making and execution module is configured to generate a personalized rehabilitation training instruction set that meets the requirements of safety, effectiveness and tolerability based on the comprehensive rehabilitation confidence level and through a multi-objective rehabilitation optimization model. The personalized rehabilitation training instruction set is then sent to the active rehabilitation training device in real time through the rehabilitation device Internet of Things protocol to drive the execution of rehabilitation actions.

[0013] By adopting the above technical solutions, the dynamic perception and assessment module utilizes a multimodal physiological sensing network to synchronously collect multidimensional physiological signals. Based on a shoulder biomechanical model and a real-time signal feature extraction network, a dynamic functional spectrum of the shoulder is constructed, enabling accurate identification of the core area of ​​functional abnormalities. This provides precise target localization for early intervention, significantly improving the accuracy and timeliness of the assessment. In the analgesia pathway planning module, the targeted drug infusion system can be controlled based on the identified core area of ​​functional abnormalities. Low-dose exploratory infusion and a multiphysics coupling model based on finite element analysis simulate the spatial distribution of analgesics in shoulder tissues. A model predictive control algorithm dynamically adjusts infusion parameters, generating a personalized analgesia pathway that matches the individual's physiological response. This ensures the precision and adaptability of analgesia intervention, avoids the risks of overdose or underdose, and significantly reduces systemic effects. Simultaneously, simulation prediction optimizes drug delivery efficiency, maximizing the utilization of analgesic resources. In the rehabilitation training integration module, a dynamic functional spectrum of the shoulder and personalized analgesia are established. The invention maps the spatiotemporal relationships between pathways and analyzes them using a multi-source information fusion model, outputting a comprehensive rehabilitation confidence score that quantifies the current rehabilitation potential. This achieves a deep integration of analgesic effect and functional status, providing a scientific basis for rehabilitation training decisions, ensuring the rationality of training timing and intensity, and avoiding poor training results due to insufficient or excessive analgesia. In the adaptive decision-making and execution module, a personalized rehabilitation training instruction set that combines safety, effectiveness, and subject tolerance is generated based on the comprehensive rehabilitation confidence score through a multi-objective rehabilitation optimization model. This instruction is then distributed in real time to the active rehabilitation training device via the rehabilitation equipment IoT protocol, thereby achieving personalized and automated rehabilitation training, greatly improving the safety and effectiveness of training, and reducing human error. Overall, this invention effectively solves the core problem of the disconnect between analgesic intervention and rehabilitation training in traditional rehabilitation through multi-module collaborative regulation, achieving dynamic, precise, and adaptive rehabilitation management. This helps accelerate the functional recovery of subjects, shorten the rehabilitation cycle, reduce the risk of re-injury, and improve the overall quality of rehabilitation.

[0014] Further settings also include:

[0015] The closed-loop learning and optimization module is configured to continuously monitor the physiological feedback data of the object after executing the personalized rehabilitation training instruction set, calculate its dynamic fit with the expected rehabilitation trajectory, and generate a rehabilitation strategy efficacy index; based on the rehabilitation strategy efficacy index, the strategy parameters of the multi-objective rehabilitation optimization model are updated online through a reinforcement learning algorithm.

[0016] By adopting the above technical solution, the physiological feedback data of the subject after executing the personalized rehabilitation training instruction set is continuously monitored, and the dynamic fit between the data and the expected rehabilitation trajectory is calculated. A rehabilitation strategy effectiveness index is generated, and then the strategy parameters of the multi-objective rehabilitation optimization model are iteratively updated online based on this index using a reinforcement learning algorithm. This enables the rehabilitation system to have the ability to learn and continuously improve itself. It can dynamically adjust the rehabilitation strategy according to the real-time response of the subject, thereby continuously improving the accuracy and effectiveness of rehabilitation intervention and avoiding the misfit problems that may be caused by fixed programs. At the same time, by continuously optimizing the model parameters, the system can better adapt to individual differences and changes in the rehabilitation process, ensuring that rehabilitation training is always in the optimal state, and greatly enhancing the intelligence and adaptability of the rehabilitation process.

[0017] A further feature is that, within the dynamic perception and assessment module, a multimodal physiological sensing network deployed on the object's shoulder and related muscle groups synchronously collects multidimensional physiological signals. Based on a shoulder biomechanical model and a real-time signal feature extraction network, the module constructs a dynamic functional spectrum of the shoulder reflecting pain and functional status in real time. This functionality includes:

[0018] High-density surface electromyography (EMG) sensors, bioimpedance sensing arrays, and inertial measurement units are deployed at key points around the anterior colliculus of the shoulder joint, supraspinatus muscle, middle deltoid muscle, and target area. These are used to capture muscle electrical activity sequences, tissue impedance change sequences, and joint kinematic parameter sequences, which together constitute the multidimensional physiological signal.

[0019] Based on the pre-set digital model of shoulder anatomy atlas, the joint capsule, target tendon area, and known sensitive nerve area are pre-marked as primary sensing focus areas, and the surrounding synergistic muscle groups and ligament attachment points are marked as secondary sensing association areas, thus constructing an initial non-uniform sensing topology network.

[0020] A real-time signal feature extraction network is introduced to process the multidimensional physiological signals in parallel and identify specific physiological events that characterize acute pain or functional abnormalities. These specific physiological events include: bursts of high-frequency discharges in muscle electrical activity sequences, dynamic changes in tissue fluid corresponding to sudden drops in local impedance shown in tissue impedance change sequences, and non-physiological tremors or abnormal movement trajectories detected in joint kinematic parameter sequences.

[0021] Based on the dynamic distribution of the specific physiological events, the non-uniform sensing topology network is updated in real time: when specific physiological events are continuously detected in the secondary sensing association area, the corresponding area is dynamically upgraded to the primary sensing focus area; for the original primary sensing focus area, if no specific physiological events occur within the continuous monitoring period, it is downgraded to the secondary sensing association area; for the primary sensing focus area, a high-frequency, high-gain data acquisition mode is adopted, and for the secondary sensing association area, a conventional frequency and gain configuration is adopted, thereby achieving adaptive allocation of sensing resources; subsequently, based on the updated non-uniform sensing topology network, the multimodal physiological sensing network is controlled to perform differentiated data acquisition, thereby obtaining optimized high-quality multidimensional physiological signals.

[0022] By employing the aforementioned technical solution, high-density surface electromyography (EMG) sensors, bioimpedance sensing arrays, and inertial measurement units are deployed in a multimodal physiological sensing network to capture muscle electrical activity sequences, tissue impedance change sequences, and joint kinematic parameter sequences, respectively, thus constructing multidimensional physiological signals. An initial non-uniform sensing topology network is built based on a digital model of shoulder anatomy atlas. Specific physiological events are then identified through a real-time signal feature extraction network, and the sensing topology network is dynamically updated to achieve adaptive allocation of sensing resources, thereby obtaining optimized, high-quality multidimensional physiological signals. This technical approach significantly improves the targeting and efficiency of signal acquisition, ensuring focused monitoring of key areas and avoiding resource waste. Furthermore, by dynamically adjusting the sensing focus, abnormal physiological events can be captured promptly, improving the accuracy and reliability of functional abnormality identification and providing a more solid data foundation for subsequent interventions.

[0023] A further feature is that, within the dynamic perception and assessment module, the function of simultaneously acquiring multidimensional physiological signals through a multimodal physiological sensing network deployed on the object's shoulder and related muscle groups, and constructing a dynamic functional spectrum of the shoulder reflecting pain and functional status in real time based on a shoulder biomechanical model and a real-time signal feature extraction network, also includes:

[0024] The optimized high-quality multidimensional physiological signals are subjected to amplitude-frequency joint normalization processing, and the processed signals are discretized into grid cells in the spatial dimension. All grid cell data are integrated to form a three-dimensional sensing data cube containing three dimensions: time, spatial location, and signal mode.

[0025] A spatiotemporal feature coding network is constructed, and the three-dimensional perception data cube is input into the spatiotemporal feature coding network for depth feature extraction and spatial reconstruction. In the coding path, a multi-granularity functional abnormality feature set is extracted and formed, which includes micro-scale features, meso-scale features, and macro-scale features. In the decoding path, the multi-granularity functional abnormality feature set is fused and upsampled to gradually restore its spatial resolution. Finally, a dynamic functional spectrum map of the shoulder is synthesized in the output layer, which intuitively displays the functional abnormality region and intensity in the form of a heat map. Each pixel unit in the heat map corresponds to a spatial grid unit.

[0026] By adopting the above technical solution, the optimized high-quality multidimensional physiological signals are subjected to amplitude-frequency joint normalization processing to form a three-dimensional perception data cube. Then, deep feature extraction and spatial reconstruction are performed through a spatiotemporal feature coding network to synthesize a dynamic functional spectrum of the shoulder. This process achieves efficient integration and visualization of multidimensional signals, allowing the abnormal functional areas and intensities to be displayed intuitively, greatly improving the interpretability and practicality of the data. At the same time, through multi-granularity feature extraction, the functional state can be captured comprehensively from the micro to the macro level, ensuring the comprehensiveness and precision of the assessment and providing strong support for accurate rehabilitation decisions.

[0027] A further setting is that, in the dynamic perception and evaluation module, the function of identifying the core area of ​​functional abnormality includes:

[0028] For each pixel unit in the shoulder dynamic functional spectrum, its composite functional abnormality score is calculated. The composite functional abnormality score is obtained by nonlinear weighted fusion of the micro-scale features, meso-scale features and macro-scale features corresponding to the pixel unit in the multi-granularity functional abnormality feature set.

[0029] A preset functional abnormality scoring threshold is set. The composite functional abnormality score of each pixel unit is compared with the functional abnormality scoring threshold and marked as a safe unit or a high-risk unit. Multiple spatially adjacent high-risk units are aggregated to form one or more continuous regions, and these regions are marked as functional abnormality core areas.

[0030] By adopting the above technical solution, the composite functional abnormality score of each pixel unit in the shoulder dynamic functional spectrum is calculated, and a preset threshold is used for comparison and labeling. Finally, the core area of ​​functional abnormality is identified, making the identification of functional abnormality areas more quantitative and objective, avoiding the error of subjective judgment. At the same time, through spatial aggregation processing, the scope of the core area can be accurately defined, providing a clear target for targeted intervention and improving the pertinence and effectiveness of rehabilitation strategies.

[0031] A further setting is that the specific functions of the analgesia pathway planning module are as follows:

[0032] Based on the three-dimensional spatial distribution of the identified functional abnormality core area, a programmable microneedle array patch consisting of multiple independent and controllable drug solution units is deployed on the skin of the corresponding area on the shoulder of the object. This programmable microneedle array patch constitutes the execution end of the targeted drug infusion system.

[0033] The targeted drug delivery system is controlled to first perform a low-dose exploratory infusion: a microneedle is used to inject a tracer into a selected area, and the tissue impedance changes are monitored simultaneously using a bioimpedance sensing array to initially obtain the basic parameters of the drug diffusion profile and rate in the subcutaneous tissue.

[0034] The diffusion profile and rate parameters are input into a multiphysics coupling model based on finite element analysis. This multiphysics coupling model comprehensively calculates tissue density, local blood perfusion rate and interstitial fluid pressure factors to simulate the spatiotemporal distribution dynamics of analgesic drugs in the target area.

[0035] A model predictive control algorithm is adopted to optimize the drug concentration in the target area to reach the effective window as quickly as possible, while minimizing the total infusion dose and potential systemic effects. The infusion parameters of each drug unit in the programmable microneedle array patch are dynamically and iteratively calculated. The infusion parameters include infusion rate, pulse frequency and start-stop sequence.

[0036] The optimized infusion parameter sequence is bound to the spatial coordinates of the functional abnormality core area to generate a personalized analgesia path that is planned in three dimensions: time, space, and dosage. Based on this path, the targeted drug infusion system is driven to perform precise drug infusion.

[0037] By adopting the above technical solution, the programmable microneedle array patch is controlled as the execution end of the targeted drug infusion system based on the three-dimensional spatial distribution of the functional abnormal core area. Drug diffusion parameters are obtained through low-dose exploratory infusion and input into a multiphysics coupling model to simulate drug distribution. Then, the infusion parameters are dynamically adjusted using a model predictive control algorithm to generate a personalized analgesia pathway. This achieves precise control and spatial positioning of drug infusion, ensuring the effective concentration of analgesic drugs in the target area. At the same time, the infusion process is optimized through model prediction, reducing trial and error costs and improving the safety and efficiency of analgesia.

[0038] A further setting is that the specific functions of the rehabilitation training integration module are as follows:

[0039] Using the anatomical center of the coracoid process of the scapula as the origin, a unified global coordinate system for shoulder rehabilitation is established. The coordinates of all pixel units in the dynamic functional spectrum of the shoulder and the spatial coordinates of each drug solution unit involved in the personalized analgesia path are transformed to this global coordinate system through a coordinate transformation matrix.

[0040] An iterative nearest-point registration algorithm is used to spatially align the set of coordinate points of the functional abnormality core area in the shoulder dynamic functional spectrum with the set of spatial coordinate points of each drug unit in the personalized analgesia pathway in the global coordinate system, and establish a spatial correlation mapping.

[0041] Based on the spatial association mapping, one or more drug liquid units with the closest spatial distance are matched for each functional abnormality core area through spatial nearest neighbor search, and a control association relationship between the functional abnormality core area and the drug liquid unit is established.

[0042] A precision clock synchronization protocol is used to synchronize the data acquisition clock of the multimodal physiological sensor network with the control clock of the targeted drug infusion system to establish a time correlation.

[0043] For each functionally abnormal core region, feature aggregation is performed in the spatial domain on the multi-granularity functionally abnormal feature set of all pixel units contained therein. The statistics of the features of all pixel units in the functionally abnormal core region are calculated to generate an aggregated feature vector representing the overall functionally abnormal state of the core region.

[0044] Based on the control correlation and time correlation, the aggregated feature vector of each functionally abnormal core area within the same time period is paired with the infusion parameters of its matching drug unit to form a spatiotemporally aligned data pair set.

[0045] The spatiotemporally aligned data pairs are input into a multi-source information fusion model. This model performs feature-level fusion of the aggregated feature vector and infusion parameters in each spatiotemporally aligned data pair. A scalar value between 0 and 1 is obtained through weighted aggregation calculation, which is the comprehensive rehabilitation confidence of the functional abnormality core area under the current analgesic intervention.

[0046] By adopting the above technical solution, a unified global coordinate system for shoulder rehabilitation is established, the functional spectrum and analgesia pathway coordinates are transformed and aligned, a spatiotemporal correlation mapping is established, and the comprehensive rehabilitation confidence is calculated through a multi-source information fusion model. This process achieves seamless integration of analgesia and rehabilitation data, making the assessment of rehabilitation potential more scientific and accurate. At the same time, spatiotemporal alignment ensures data consistency, providing a reliable basis for subsequent decision-making and greatly improving the synergy between rehabilitation training and analgesia intervention.

[0047] A further setting is that the adaptive decision-making and execution module specifically functions as follows:

[0048] A multi-objective rehabilitation optimization model is constructed, wherein the multi-objective rehabilitation optimization model includes a safety objective function, an effectiveness objective function, and a tolerability objective function;

[0049] The comprehensive rehabilitation confidence score is input into the multi-objective rehabilitation optimization model as a dynamic weight adjustment factor of the effectiveness objective function, which is used to dynamically adjust the effectiveness priority of rehabilitation training.

[0050] A multi-objective optimization algorithm is used to solve the multi-objective rehabilitation optimization model to obtain a set of Pareto optimal solutions; wherein, the multi-objective optimization algorithm is a multi-objective optimization algorithm based on genetic algorithm or a multi-objective optimization algorithm based on particle swarm optimization.

[0051] Based on a preset decision-making strategy, an optimal solution is selected from the Pareto optimal solution set, and the personalized rehabilitation training instruction set is generated according to the optimal solution; wherein, the decision-making strategy is a weighted sum method or an ε-constraint method, and the personalized rehabilitation training instruction set includes training action type, training intensity, training duration, and training frequency parameters.

[0052] The personalized rehabilitation training instruction set is sent to the active rehabilitation training device in real time via the rehabilitation equipment IoT protocol.

[0053] By adopting the above technical solution, a personalized rehabilitation training instruction set is generated through a multi-objective rehabilitation optimization model with safety, effectiveness, and tolerability as objectives. The Pareto optimal solution set is solved using a multi-objective optimization algorithm, and the optimal solution is selected and sent to the rehabilitation equipment based on the decision-making strategy. This method ensures the comprehensiveness and balance of the rehabilitation training program, avoids the drawbacks that may be caused by single-objective optimization, and improves efficiency through automated decision-making, making the training program more in line with the actual needs of the subjects and improving the rehabilitation effect.

[0054] Further settings include the adaptive decision-making and execution functions, specifically as follows:

[0055] The safety objective function is defined by a preset safety constraint function, which is constructed based on the physiological range of motion and safe load threshold limited by the object's shoulder anatomy. The effectiveness objective function is defined by a rehabilitation progress function, which is constructed based on the matching degree between historical rehabilitation data and the expected functional recovery trajectory. The tolerance objective function is defined by a pain tolerance function, which is constructed based on the deviation between the real-time acquired object pain feedback signal and the preset pain threshold.

[0056] By adopting the above technical solution, and defining a multi-objective function through preset safety constraint function, rehabilitation progress function, and pain tolerance function, the rehabilitation optimization model is made closer to clinical practice, ensuring the safety and scientific nature of the training program. At the same time, the adaptability of the model is enhanced by dynamic weight adjustment, which can better respond to changes in the state of the subject and improve the accuracy of rehabilitation intervention.

[0057] A further setting is that the function of the closed-loop learning and optimization module is as follows:

[0058] Continuously monitor the physiological feedback data of the subject after executing the personalized rehabilitation training instruction set; wherein, the physiological feedback data is real-time multidimensional physiological signals collected from the multimodal physiological sensor network;

[0059] Based on the physiological feedback data, the dynamic fit between the physiological feedback data and the expected recovery trajectory is calculated; wherein, the expected recovery trajectory is an ideal recovery path preset based on historical recovery data, and the dynamic fit is obtained by calculating the deviation between the physiological feedback data and the expected recovery trajectory at multiple time points.

[0060] Based on the dynamic adaptability, a rehabilitation strategy effectiveness index is generated; wherein, the rehabilitation strategy effectiveness index is a quantitative indicator, which is calculated by weighted fusion of the dynamic adaptability and the preset rehabilitation goal achievement, and is used to reflect the effectiveness of the current rehabilitation strategy.

[0061] Based on the rehabilitation strategy efficacy index, the strategy parameters of the multi-objective rehabilitation optimization model are updated online using a reinforcement learning algorithm. The reinforcement learning algorithm uses the rehabilitation strategy efficacy index as a reward signal to dynamically adjust the weight coefficients of the safety objective function, effectiveness objective function, and tolerance objective function in the multi-objective rehabilitation optimization model.

[0062] By adopting the above technical solution, the dynamic fit is calculated by continuously monitoring physiological feedback data, a rehabilitation strategy efficacy index is generated, and the model parameters are updated online using reinforcement learning algorithms. This mechanism enables the rehabilitation system to continuously learn and optimize from practice, maintain the timeliness and effectiveness of the strategy, and drive model improvement through reward signals, ensuring continuous optimization of the rehabilitation process and improving the overall intelligence level and performance of the rehabilitation system.

[0063] In summary, the present invention has the following beneficial effects: it achieves precise spatiotemporal matching and dynamic synergy between analgesia and rehabilitation training, thereby significantly improving rehabilitation efficiency and quality while ensuring safety. Attached Figure Description

[0064] Figure 1 This is the main flowchart of an embodiment;

[0065] Figure 2 This is a flowchart illustrating the dynamic perception and evaluation module in the embodiment;

[0066] Figure 3 This is a flowchart illustrating the analgesia pathway planning module in the embodiment.

[0067] Figure 4 This is a flowchart illustrating the rehabilitation training integration module in the embodiment.

[0068] Figure 5 This is a flowchart illustrating the adaptive decision-making and execution module in the embodiment;

[0069] Figure 6 This is a flowchart illustrating the closed-loop learning and optimization module in the embodiment. Detailed Implementation

[0070] The present invention will be further described in detail below with reference to the accompanying drawings.

[0071] like Figures 1 to 6 As shown;

[0072] This embodiment discloses a rapid rehabilitation control system for shoulder surgery that incorporates analgesia pathways, including:

[0073] S1, the dynamic perception and assessment module, is configured to simultaneously collect multi-dimensional physiological signals through a multimodal physiological sensing network deployed on the object's shoulder and related muscle groups. Based on the shoulder biomechanical model and real-time signal feature extraction network, it constructs a dynamic functional spectrum of the shoulder that reflects pain and functional status in real time, and identifies the core area of ​​functional abnormality accordingly.

[0074] The specific implementation process of this module is as follows: The system first deploys a multimodal physiological sensing network at key anatomical sites in the shoulder and related muscle groups. Specifically, high-density surface electromyography (EMG) sensors, bioimpedance sensing arrays, and inertial measurement units are deployed at key points sensitive to pain and functional abnormalities, such as the anterior deltoid, supraspinatus, middle deltoid, and the surrounding area of ​​the target region, through medical-grade adhesives or minimally invasive implantation. The high-density EMG sensors are responsible for capturing the electrical activity sequence of muscle fibers in resting and active states, which can sensitively reflect the nerve innervation state of the muscles and potential abnormal discharges. The bioimpedance sensing array obtains the tissue impedance change sequence by injecting a weak and safe alternating current into the subcutaneous tissue and measuring the response voltage. This sequence can indirectly reflect the level of inflammation, edema, and blood perfusion of the local tissue. The inertial measurement unit integrates a gyroscope and an accelerometer to accurately measure the angle, angular velocity, and linear acceleration of the shoulder joint in space, forming a joint kinematic parameter sequence to quantify the range of motion, smoothness of movement, and the presence of compensatory or pathological tremors. After the sensor network is deployed, the system constructs an initial non-uniform sensing topology network based on a pre-defined, high-precision digital model of shoulder anatomy. This digital model, derived from a large amount of human anatomy data, pre-marks key and problematic areas such as the joint capsule, target tendon region, and known sensitive nerve areas as "primary sensing focus areas"; while surrounding synergistic muscle groups (such as the trapezius and subscapularis muscles) and major ligament attachment points are marked as "secondary sensing association areas." This topology network clearly defines the initial monitoring priority of different regions.

[0075] Subsequently, the system introduces a real-time signal feature extraction network to perform parallel processing and in-depth analysis of the synchronously acquired multidimensional physiological signals. This network employs a sliding time window mechanism (window length set to 256 milliseconds, overlap rate 50%) to perform time-frequency domain analysis on electromyographic signals (e.g., short-time Fourier transform to extract power spectrum features), identifying burst-frequency discharge patterns characterizing muscle protective spasms or nerve irritation (defined as transient events with a frequency greater than 100 Hz and an amplitude exceeding three standard deviations from the baseline); it performs dynamic trend analysis on bioimpedance signals (using an exponentially weighted moving average model) to capture impedance drop events that may indicate dynamic changes in tissue fluid or worsening of local edema (defined as an impedance value decreasing by more than 15% within 1 second); and for inertial measurement unit data, it uses Kalman filtering and motion trajectory reconstruction algorithms to detect abnormal patterns such as non-physiological fine tremors (frequency in the range of 5-12 Hz and amplitude greater than 0.1 rad / s), motion trajectory deviations (root mean square error greater than 0.05 compared to the preset physiological trajectory), or sudden limitation of range of motion (joint angle change rate less than 80% of the preset threshold). These identified patterns are collectively referred to as "specific physiological events." The system possesses the ability to dynamically optimize its sensing strategy. It continuously updates the aforementioned non-uniform sensing topology network based on the real-time detection results of these specific physiological events. For example, if significant bursts of high-frequency electromyographic activity are detected in a previously secondary sensing area (such as the upper trapezius muscle) for three consecutive monitoring cycles (each cycle lasting 5 seconds), the system will determine that the functional state of that area has deteriorated and dynamically upgrade it to a primary sensing focus area, giving it higher attention. Conversely, if no specific physiological events occur in a previously primary sensing focus area for five consecutive monitoring cycles, it indicates that the condition of that area is stabilizing and can be downgraded to a secondary sensing area. In line with this strategy, the system instructs the multimodal physiological sensor network to perform differentiated data acquisition: for all primary sensing focal areas, a high-frequency, high-gain data acquisition mode is adopted, specifically setting the sampling frequency to 1000 Hz and the signal gain to 1000 times, to capture the most subtle pathological changes; for secondary sensing correlation areas, a conventional frequency and gain configuration is used, specifically setting the sampling frequency to 200 Hz and the signal gain to 500 times, to balance monitoring accuracy with system power consumption and data load. Through this adaptive resource allocation, the system ensures the quality of data acquisition in key areas, thereby outputting a set of optimized, high-quality multidimensional physiological signals with a higher signal-to-noise ratio.

[0076] After obtaining the optimized, high-quality, multidimensional physiological signals, the system preprocesses them, including amplitude normalization (using Min-Max scaling to map the signal amplitude to the [0,1] interval) and frequency normalization (unifying all signals to a 1000 Hz reference through resampling), forming a standardized signal stream. Next, in the spatial dimension, the sensing area is discretized into dense grid cells based on a human shoulder model (each grid cell corresponds to...). Each unit (the surface region of the body) is bound to a specific anatomical location. Standardized signal data from all grid units across all modalities are integrated across consecutive time slices to construct a three-dimensional perceptual data cube containing temporal, spatial grid location, and signal modality dimensions. To extract clinically meaningful insights from this three-dimensional perceptual data cube, a dedicated spatiotemporal feature encoding network is constructed. This network employs an encoder-decoder architecture. In the encoding path, the network progressively downsamples and extracts deep features from the data cube through three-dimensional convolutional layers (3×3×3 kernel size, stride 1) and spatial pooling layers (max pooling, pooling window 2×2×2), forming a "multi-granularity functional abnormality feature set" containing information at different levels of abstraction. Among them, microscale features (extracted from the shallow output of the encoder) capture transient changes and high-frequency components of signals within a single grid cell (such as abnormal discharges of a single motion unit); mesoscale features (extracted from the mid-level output of the encoder) characterize the correlation and coordination patterns of multiple signal sources within a local small region (such as muscle group coordinated contraction disorder); and macroscale features (extracted from the deep output of the encoder) depict the macroscopic state of functional connectivity and motor coordination of the entire shoulder region. In the decoding path, the network uses upsampling techniques such as transposed convolution (3×3×3 kernel size, stride of 2) to gradually restore the spatial resolution and fuse features of the compressed and abstracted multi-granularity functional abnormality feature set, and finally synthesizes a shoulder dynamic functional spectrum map presented in the form of a heatmap at the output layer. Each pixel in this shoulder dynamic functional spectrum map corresponds precisely to a spatial grid cell, and its color depth (such as from blue to red) intuitively reflects the intensity of functional abnormality in the region where the cell is located; the deeper the red, the more severe the functional abnormality.

[0077] Finally, based on the generated shoulder dynamic functional spectrum, the system performs precise identification of the core region of functional abnormality. For each pixel unit in the spectrum, the system extracts microscale features from its corresponding multi-granularity functional abnormality feature set. Mesoscale characteristics and macroscopic scale characteristics A comprehensive composite functional disorder score is calculated using a pre-defined nonlinear weighted fusion function. The specific calculation formula is as follows: ;in, , , These are corresponding dynamic weighting coefficients, which are dynamically adjusted based on the current recovery stage and disaster type (e.g., acute or chronic phase). For example, in the acute phase... (Micro-feature weights) are set to 0.5. (Mesoscopic feature weights) are set to 0.3. (Macroeconomic feature weight) is set to 0.2; For non-linear activation functions, the Sigmoid function is used here (i.e., ... The output is compressed to the [0,1] interval to represent the severity of functional abnormalities. The system presets a clinically validated functional abnormality score threshold of 0.75 and compares the composite functional abnormality score of each pixel unit to this threshold. Units with scores exceeding 0.75 are marked as "high-risk units," while the rest are "safe units." Subsequently, the system applies spatial clustering algorithms such as connected component analysis (using 8-neighborhood connectivity) to aggregate multiple geographically adjacent high-risk units, forming one or more continuous abnormal regions. These regions are spatially clustered and all exhibit severe functional abnormality scores, thus being ultimately identified and marked as the core functional abnormality areas requiring focused intervention, providing precise targets for subsequent analgesia pathway planning.

[0078] S2, the analgesia pathway planning module, is configured to deploy a targeted drug infusion system based on the identified functional abnormality core area, perform low-dose exploratory infusion and simulate the spatial distribution of analgesic drugs in shoulder tissue using a multiphysics coupling model based on finite element analysis, and dynamically adjust infusion parameters using a model predictive control algorithm to generate a personalized analgesia pathway that matches the individual's physiological response.

[0079] The specific implementation process of this module is as follows: First, based on the three-dimensional spatial distribution of the identified functional abnormality core area, the system controls the deployment of a programmable microneedle array patch consisting of multiple independent and controllable drug solution units on the skin of the corresponding area on the shoulder surface of the object. This patch serves as the execution end of the targeted drug infusion system. Each drug solution unit has an independent microneedle structure, drug reservoir, and micro piezoelectric pump, and its infusion behavior can be precisely controlled by digital signals. During patch deployment, the system ensures that the coverage area of ​​the microneedle array highly overlaps with the spatial distribution of the core area based on the coordinate mapping between the shoulder anatomical model and the functional abnormality core area. At the same time, it adheres tightly to the skin with medical adhesive to avoid displacement or leakage during the infusion process. After the targeted drug infusion system is started, the system first performs a low-dose exploratory infusion: a microneedle is injected into the selected area with a tracer (such as fluorescently labeled saline, at a dose of 0.1 ml), and the tissue impedance changes are monitored simultaneously using a bioimpedance sensing array. By measuring the spatiotemporal dynamic changes of impedance values, the diffusion profile (such as diffusion radius and penetration depth) and basic rate parameters (such as diffusion rate constant) of the drug in the subcutaneous tissue are initially obtained. These parameters provide initial input for subsequent modeling. Subsequently, the system inputs the diffusion profile and rate parameters into a multiphysics coupled model based on finite element analysis. This model integrates biomechanics, fluid dynamics, and mass transfer principles, comprehensively calculating factors such as tissue density (obtained through MRI or CT data), local blood perfusion rate (based on Doppler ultrasound or photoacoustic imaging data), and interstitial fluid pressure (measured through micro-pressure sensors) to simulate the spatiotemporal distribution dynamics of analgesic drugs (such as the local anesthetic ropivacaine) in the target area, including drug concentration gradient, diffusion path, and clearance rate. The model uses a transient solver to iteratively calculate the drug transport process in the three-dimensional tissue mesh with a time step of seconds and outputs the predicted concentration field. A model predictive control algorithm is employed to address a multi-objective optimization problem. The goal is to minimize the drug concentration in the target area to reach the effective window (defined as maintaining the concentration within the range of 0.5-2.0 μg / mL) as quickly as possible, while simultaneously minimizing the total infusion dose and potential systemic effects (such as blood drug concentration exceeding the safety threshold of 0.1 μg / mL). The algorithm dynamically and iteratively calculates the infusion parameters of each drug unit in the programmable microneedle array patch, including the infusion rate (range 0-50 μL / min), pulse frequency (range 0.1-10 Hz), and start-stop timing (accurate to the millisecond level). Through rolling time-domain optimization, the algorithm solves a constrained nonlinear programming problem within each control cycle (set to 5 seconds) and updates the optimal parameter sequence.Finally, the system binds the optimized infusion parameter sequence with the spatial coordinates of the functionally abnormal core area to generate a personalized analgesia pathway planned in three dimensions: time, space, and dosage. This pathway is stored in the form of digital instructions and drives the targeted drug infusion system to perform precise drug infusion, ensuring that the analgesia intervention and the functionally abnormal area are highly synchronized in time and space, thereby maximizing the therapeutic effect and minimizing resource consumption and risk.

[0080] S3, the rehabilitation training fusion module, is configured to establish a spatiotemporal correlation mapping between the shoulder dynamic function spectrum and the personalized analgesia pathway, and to analyze it using a multi-source information fusion model to output a comprehensive rehabilitation confidence score that can quantify the current rehabilitation potential.

[0081] The specific implementation process of this module is as follows: First, the system establishes a unified global coordinate system for shoulder rehabilitation, with the anatomical center of the coracoid process of the scapula as the origin. This coordinate system adopts the right-hand rule, with the X-axis pointing to the front of the body, the Y-axis pointing to the left side of the body, and the Z-axis pointing vertically upward, ensuring that the coordinate system is fixed to the anatomical structure of the subject and does not drift with changes in body position. The system then uses the coordinates of all pixel units in the shoulder dynamic functional spectrum (each pixel unit corresponds to one...) The spatial coordinates of the body surface mesh units and each drug solution unit involved in the personalized analgesia pathway (obtained based on the deployment position of the programmable microneedle array patch) are uniformly transformed to this global coordinate system through a predefined coordinate transformation matrix. The coordinate transformation matrix is ​​obtained by registering preoperative CT or MRI images with body surface markers. It is a 4×4 homogeneous transformation matrix containing rotation and translation components, ensuring that all spatial data are expressed in the same reference system and eliminating coordinate deviations caused by differences in sensor attachment positions or slight object movement. Subsequently, the system uses an iterative nearest-point registration algorithm to accurately align the coordinate point set of the functionally abnormal core area in the shoulder dynamic functional spectrum (composed of the spatial coordinates of multiple high-risk pixel units) with the spatial coordinate point set of the drug solution units in the personalized analgesia pathway in the global coordinate system. Specifically, the algorithm uses the coordinate point set of the functionally abnormal core area as the source point set and the coordinate point set of the drug solution units as the target point set. Iteratively calculates the optimal rigid body transformation (including the rotation matrix R and the translation vector T) between the two point sets to minimize the average distance between the transformed source point set and the target point set. The iteration termination condition is set to an average registration error of less than 1 mm or the number of iterations reaches 100. After registration, the system establishes a spatial association mapping between the functionally abnormal core area and the medication unit. This mapping is stored in the form of a lookup table, recording the spatial correspondence between each functionally abnormal core area and its neighboring medication units. Based on the established spatial association mapping, the system uses a spatial nearest neighbor search algorithm to match one or more spatially closest medication units for each functionally abnormal core area. The search algorithm uses a KD-tree structure to accelerate the query, and sets a maximum matching distance threshold of 2 cm to ensure that only spatially closely related units are matched. For each functionally abnormal core area, the system records its matched medication unit identifier and its spatial distance weight. The weight is calculated using the formula: weight = 1 / (1 + distance), thereby establishing a control association relationship between the functionally abnormal core area and the medication unit. This relationship indicates that analgesic intervention for a specific functionally abnormal core area will be performed by the medication unit that is spatially closest to it.

[0082] To achieve temporal synchronization, the system employs a precision clock synchronization protocol (specifically the IEEE 1588 PTP protocol) to precisely synchronize the data acquisition clock of the multimodal physiological sensor network with the control clock of the targeted drug infusion system, keeping the synchronization error within milliseconds. After clock synchronization, the system assigns a unified timestamp to each data acquisition event and drug infusion event, establishing a temporal correlation. This ensures that the updates to the shoulder dynamic functional spectrum and the execution of the analgesia pathway are strictly aligned in time, avoiding analytical biases caused by timing misalignments.

[0083] For each functionally abnormal core region, the system performs spatial domain feature aggregation on the multi-granularity functionally abnormal feature set of all its constituent pixel units. Specifically, for each core region, the system extracts microscale features (such as high-frequency discharge energy of muscle electrical activity), mesoscale features (such as local impedance change gradient), and macroscale features (such as joint motion coordination index) corresponding to all its internal pixel units, and calculates the statistics of these features, including mean, standard deviation, and maximum value. For example, the mean of the microscale features is the arithmetic mean of the microscale feature values ​​of all pixel units, the standard deviation reflects the dispersion of the feature values, and the maximum value captures the most severe abnormality. These statistics are concatenated into a high-dimensional aggregated feature vector, which comprehensively represents the overall functionally abnormal state of the functionally abnormal core region at the current moment. Based on the established control and temporal correlations, the system pairs the aggregated feature vector of each functionally abnormal core region within the same time period (usually a monitoring cycle, such as 5 seconds) with the infusion parameters (including infusion rate, pulse frequency, and start-stop sequence) of its matching drug unit, forming a spatiotemporally aligned data pair set. Each data pair contains an aggregated feature vector and an input parameter vector, ensuring the consistency of the data in space and time.

[0084] Finally, the system inputs the spatiotemporally aligned data pair set into the multi-source information fusion model. This model employs a feature-level fusion strategy. First, it standardizes the aggregated feature vector and input parameter vector in each data pair, unifying their numerical range to the [0,1] interval. Next, the model calculates the comprehensive rehabilitation confidence score using a weighted aggregation function, with the specific formula as follows: ;in, Indicates the overall confidence level of rehabilitation; This represents the comprehensive score of the aggregated feature vectors after dimensionality reduction through principal component analysis; This represents the effective dose-weighted sum of the infusion parameter vector, with the weights determined by both the pulse frequency and the infusion rate. and The fusion coefficients are dynamically adjusted, with initial values ​​set to 0.6 and 0.4 respectively. The Sigmoid activation function is defined as follows: The higher the overall confidence level of rehabilitation, the greater the rehabilitation potential of the core area of ​​functional abnormality under the current analgesic intervention, providing a quantitative basis for the subsequent adaptive decision-making and execution modules.

[0085] S4. The adaptive decision-making and execution module is configured to generate a personalized rehabilitation training instruction set that meets the requirements of safety, effectiveness and tolerability based on the comprehensive rehabilitation confidence level and through a multi-objective rehabilitation optimization model. The personalized rehabilitation training instruction set is then sent to the active rehabilitation training device in real time through the rehabilitation device Internet of Things protocol to drive the execution of rehabilitation actions.

[0086] The specific implementation process of this module is as follows: First, the system constructs a multi-objective rehabilitation optimization model, which includes three core objective functions: safety objective function, effectiveness objective function, and tolerability objective function.

[0087] The safety objective function is defined by a pre-defined safety constraint function. This function is constructed based on the physiological range of motion limited by the object's shoulder anatomy (e.g., shoulder abduction angle must not exceed 90 degrees, and flexion angle is limited to within 120 degrees) and a safe load threshold (e.g., the maximum resistance applied to the rotator cuff muscles must not exceed 5 Newtons). Its mathematical expression is: ;in, This represents the value of the security objective function; Indicates the first Kinematic parameters (such as joint angles and angular velocities); Indicates the first Safety center values ​​for each kinematic parameter; Indicates the first The safety objective function, which sets permissible deviation limits for each kinematic parameter, aims to penalize any movement exceeding these safety boundaries, ensuring that rehabilitation training does not induce secondary injury. The effectiveness objective function is defined by a rehabilitation progress function, constructed based on the match between historical rehabilitation data and the expected functional recovery trajectory. Its mathematical expression is: ;in, This represents the value of the objective function for effectiveness. This represents the aggregated feature vector of the core region of the current functional abnormality; This represents the feature vector of the expected functional recovery trajectory based on historical successful cases; This represents the scaling factor; This represents the convergence speed adjustment coefficient; The natural exponential function is characterized by a consistently positive output value that monotonically decreases as the input value increases. The effectiveness objective function aims to maximize the fit between rehabilitation progress and the ideal trajectory. The tolerance objective function is defined by the pain tolerance function, which is constructed based on the deviation between real-time acquired pain feedback signals (such as digital input via the Visual Analogue Scale (VAS) or pain-related electrical activity features extracted from surface electromyography) and a preset pain threshold. Its mathematical expression is: ;in, This represents the value of the tolerance objective function; It represents the real-time pain score, which is the pain value that is collected and quantified in real time through the pain-related electrical activity characteristics of the surface electromyography (VAS) scale of the object. This represents an individualized pain tolerance threshold; Indicates pain tolerance margin; This represents the normalization coefficient; the tolerance objective function aims to minimize subjective discomfort during training and improve subject compliance.

[0088] After the model is built, the system incorporates the comprehensive rehabilitation confidence score output by the rehabilitation training fusion module as a dynamic weight adjustment factor into the effectiveness objective function. Specifically, the weight coefficients of the effectiveness objective function are dynamically adjusted based on the comprehensive rehabilitation confidence score, and its mathematical expression is: ;in, This represents the adjusted effectiveness target weight coefficient; This represents the initial effectiveness target weight coefficient, with a value of 0.5; This represents the sensitivity coefficient, with a value of 0.8. This represents the overall rehabilitation confidence level. Subsequently, the system uses a multi-objective optimization algorithm to solve the above multi-objective rehabilitation optimization model. In this embodiment, a multi-objective optimization algorithm based on a genetic algorithm is preferred, with the following parameter settings: population size of 100, crossover probability of 0.85, mutation probability of 0.05, and maximum number of iterations of 200. The algorithm uses three objective functions... , and To optimize the objectives, a set of Pareto optimal solutions is generated through non-dominated ranking and crowding calculation. Each solution represents a feasible combination of rehabilitation training parameters (including training movement type, training intensity, training duration, and training frequency). Next, the system selects an optimal solution from the Pareto optimal solution set based on a preset decision-making strategy. In this embodiment, a weighted sum method is used, with weights dynamically set according to clinical priorities: safety target weight coefficient is 0.4, effectiveness target weight coefficient is 0.4, and tolerability target weight coefficient is 0.2. The weighted sum score for each solution is calculated as follows: ;in, This represents the total score of a solution in the Pareto solution set; This represents the weighting coefficient for the safety objective; This represents the adjusted effectiveness target weight coefficient; The system represents the weighting coefficient of the tolerance target; finally, the solution with the highest total score is selected as the final decision output. Based on this optimal solution, the system generates a personalized rehabilitation training instruction set, with specific parameters including: training action type (e.g., passive joint range of motion training, active assisted training, or resistance training), training intensity (expressed as a percentage, such as 30% of maximum voluntary contraction force), training duration (e.g., each training session lasts 15 minutes), and training frequency (e.g., 3 times per day). Finally, the system uses a rehabilitation device IoT protocol (e.g., a medical device communication protocol based on the IEEE 11073 standard) to send the personalized rehabilitation training instruction set to active rehabilitation training devices (e.g., intelligent shoulder joint rehabilitation machines or exoskeleton robots) in real time, driving them to execute corresponding rehabilitation actions. The protocol uses JSON format to encapsulate the instruction data, ensuring low latency (requiring end-to-end latency of less than 100 milliseconds) and high reliability transmission, and verifies the instruction execution status through device feedback loops, thereby achieving precise, safe, and adaptive control of rehabilitation training.

[0089] S5, Closed-loop learning and optimization module, configured to continuously monitor the physiological feedback data of the object after executing the personalized rehabilitation training instruction set, calculate its dynamic fit with the expected rehabilitation trajectory, and generate a rehabilitation strategy effectiveness index; based on the rehabilitation strategy effectiveness index, the strategy parameters of the multi-objective rehabilitation optimization model are updated online through reinforcement learning algorithm.

[0090] The specific implementation process of this module is as follows: The system first continuously monitors the physiological feedback data of the subject after executing the personalized rehabilitation training instruction set. This physiological feedback data originates from a multimodal physiological sensor network deployed in the subject's shoulder and related muscle groups, including muscle electrical activity sequences captured by high-density surface electromyography sensors, tissue impedance change sequences acquired by bioimpedance sensing arrays, and joint kinematic parameter sequences recorded by inertial measurement unit groups. The system synchronously acquires these multidimensional physiological signals at a fixed monitoring cycle (e.g., every 5 seconds), and performs noise reduction and standardization processing through a signal preprocessing module to ensure data quality. The acquired real-time multidimensional physiological signals are stored in a timestamped buffer for subsequent analysis.

[0091] Based on the aforementioned physiological feedback data, the system calculates its dynamic fit with the expected rehabilitation trajectory. The expected rehabilitation trajectory is an ideal recovery path preset based on historical rehabilitation data, typically represented as a time series, containing ideal functional index values ​​(such as joint range of motion, degree of normalization of electromyographic activity, etc.) for different rehabilitation stages (e.g., from postoperative day 1 to day 30). The dynamic fit is calculated by comparing the deviation values ​​between real-time physiological feedback data and the expected rehabilitation trajectory at multiple key time points. Specifically, the system selects multiple aligned time points (e.g., the starting point of each monitoring cycle), and for each time point, extracts feature vectors from the physiological feedback data (e.g., muscle electrical activity power spectrum features, tissue impedance gradient features, joint motion smoothness index), and calculates the Euclidean distance between these vectors and the corresponding feature vectors in the expected trajectory. The deviation formula is: ;in, Indicates a point in time The deviation value, For the current feature vector, The target feature vector is used. The dynamic fit is ultimately derived by aggregating the deviation values ​​across all time points, using an exponentially weighted moving average method to smooth short-term fluctuations. The calculation formula is: ;in, Indicates the dynamic fit (the value ranges from 0 to 1, with higher values ​​indicating better fit); Indicates the total number of points in time; Indicates the attenuation factor (default value is 0.1); dynamic fit. It quantifies the degree to which real-time rehabilitation progress matches the ideal path.

[0092] Next, the system generates a rehabilitation strategy effectiveness index. This index is a comprehensive quantitative indicator, calculated by weighted fusion of dynamic fit and the achievement rate of preset rehabilitation goals. The achievement rate of rehabilitation goals is based on clinically preset milestone goals (such as joint mobility reaching 50% within 7 days post-surgery and pain scores decreasing to below 3). The system assesses the current goal completion rate based on real-time data. The weighted fusion formula is as follows: ;in, Rehabilitation strategy effectiveness index; Indicates dynamic adaptability; Indicates the degree of goal achievement (normalized to the range of 0-1); and These are weighting coefficients (initial values ​​are set to 0.6 and 0.4, which can be dynamically adjusted according to the rehabilitation stage); the rehabilitation strategy effectiveness index. The higher the value, the more effective the current rehabilitation strategy.

[0093] Based on the rehabilitation strategy efficacy index, the system uses a reinforcement learning algorithm to iteratively update the strategy parameters of the multi-objective rehabilitation optimization model online. In this embodiment, the reinforcement learning algorithm employed is the proximal policy optimization algorithm, which uses the rehabilitation strategy efficacy index as a reward signal to dynamically adjust the weight coefficients of the safety, effectiveness, and tolerability objective functions in the multi-objective rehabilitation optimization model. The specific process is as follows: The algorithm maintains a policy network, whose input is the current state (including real-time physiological characteristics and historical rehabilitation strategy efficacy index sequences), and whose output is the adjustment amount of the weight coefficients (such as the safety objective weight coefficient). Effectiveness target weight coefficient Tolerance target weighting coefficient (the increment). The reward function is defined as... ;in, This indicates the effectiveness index of the current rehabilitation strategy. This indicates the magnitude of the weight change (penalty term). The balance coefficient is set to 0.01. The algorithm interacts with the environment (i.e., the rehabilitation system), collects state-action-reward sequences, and updates the policy network parameters using gradient ascent to maximize cumulative rewards. An iteration is performed every update cycle (e.g., every 30 minutes) to ensure smooth evolution of the weight coefficients and avoid abrupt changes. The updated weight coefficients are directly applied to the multi-objective rehabilitation optimization model, influencing the generation of subsequent rehabilitation training instruction sets, thus forming a closed-loop optimization. Throughout the process, the system logs all parameter changes and performance indices, supporting offline analysis and clinical auditing to ensure the transparency and security of the learning process.

[0094] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A fast rehabilitation regulation system for shoulder surgery combined with analgesic pathway, characterized in that, Comprise: A dynamic perception and evaluation module configured to synchronously collect multi-dimensional physiological signals through a multi-modal physiological sensing network deployed on the subject's shoulder and associated muscle groups, and to construct a shoulder dynamic function spectrum reflecting the pain and functional status in real time based on a shoulder biomechanical model and a real-time signal feature extraction network, and to identify the functional abnormal core area accordingly; A analgesic path planning module configured to control a targeted drug infusion system based on the identified functional abnormal core area, to simulate the spatial distribution of analgesic drugs in the shoulder tissue through a low-dose exploratory infusion and a multi-physics coupling model based on finite element analysis, and to dynamically adjust the infusion parameters using a model predictive control algorithm to generate a personalized analgesic path that matches the individual physiological response of the subject; Specifically: According to the three-dimensional spatial distribution of the identified functional abnormal core area, a programmable microneedle array patch composed of multiple independently controllable drug units is deployed on the upper skin of the corresponding area of the subject's shoulder, which constitutes the execution end of the targeted drug infusion system; The targeted drug infusion system is controlled to first perform a low-dose exploratory infusion: control the microneedles in the selected area to inject a small amount of tracer, and simultaneously monitor the tissue impedance changes using a bioimpedance sensing array to preliminarily obtain the diffusion profile and rate basic parameters of the drug in the subcutaneous tissue; The diffusion profile and rate basic parameters are input into a multi-physics coupling model based on finite element analysis, which comprehensively calculates the factors of tissue density, local blood perfusion rate and interstitial fluid pressure to simulate the spatio-temporal distribution of analgesic drugs in the target area; A model predictive control algorithm is used to dynamically and iteratively calculate the infusion parameters of each drug unit in the programmable microneedle array patch, including infusion rate, pulse frequency and start-stop timing, with the optimization goal of the target area drug concentration reaching the effective action window as quickly as possible while minimizing the total infusion dose and potential systemic impact of the system; The infusion parameter sequence obtained by optimization calculation is bound with the spatial coordinates of the functional abnormal core area to generate a personalized analgesic path that is planned in three dimensions of time, space and dose, and to drive the targeted drug infusion system to perform the corresponding drug infusion accordingly; A rehabilitation training fusion module is configured to establish a spatio-temporal correlation mapping between the shoulder dynamic function spectrum and the personalized analgesic path, analyze it using a multi-source information fusion model, and output a comprehensive rehabilitation confidence that can quantify the current rehabilitation potential; An adaptive decision and execution module is configured to generate a personalized rehabilitation training instruction set that meets safety, effectiveness and tolerance based on the comprehensive rehabilitation confidence through a multi-objective rehabilitation optimization model, and to real-time issue the personalized rehabilitation training instruction set to active rehabilitation training devices through a rehabilitation device Internet of Things protocol.

2. The fast-track rehabilitation modulation system for shoulder surgery in conjunction with analgesic pathway according to claim 1, characterized in that, Also include: A closed-loop learning and optimization module is configured to continuously monitor physiological feedback data of the subject after the execution of the personalized rehabilitation training instruction set, calculate a dynamic adaptation degree of the physiological feedback data to an expected rehabilitation trajectory, and generate a rehabilitation strategy performance index; based on the rehabilitation strategy performance index, the strategy parameters of the multi-objective rehabilitation optimization model are iteratively updated online through a reinforcement learning algorithm.

3. The fast-track rehabilitation modulation system for shoulder surgery in conjunction with analgesic pathway according to claim 1, wherein, In the dynamic perception and evaluation module, a multi-modal physiological sensing network is deployed on the shoulder and associated muscle groups of the subject to synchronously collect multi-dimensional physiological signals, and based on a shoulder biomechanical model and a real-time signal feature extraction network, a shoulder dynamic function spectrum reflecting the pain and functional status is constructed in real time. The functions include: High-density surface electromyography sensors, bioimpedance sensing arrays, and inertial measurement unit groups are arranged in the anterior bundle of the shoulder joint, the supraspinatus muscle, the middle bundle of the deltoid muscle, and the key points around the target area to capture muscle electrical activity sequences, tissue impedance change sequences, and joint kinematics parameter sequences, respectively, to form the multi-dimensional physiological signals. Based on a pre-set digital model of the shoulder anatomical atlas, the joint capsule, the target tendon region, and the known sensitive nerve region are pre-marked as the first perception focus area, and the surrounding synergistic muscle groups and ligament attachment points are marked as the second perception associated area to construct an initial non-uniform perception topology network. A real-time signal feature extraction network is introduced to process the multi-dimensional physiological signals in parallel, identify specific physiological events in the signals that represent acute pain or functional abnormalities, and the specific physiological events include burst high-frequency discharge in muscle electrical activity sequences, local impedance drop in tissue impedance change sequences, and non-physiological tremor or abnormal motion trajectory in joint kinematics parameter sequences. Based on the dynamic distribution of the specific physiological events, the non-uniform perception topology network is updated in real time: when the specific physiological events are continuously detected in the second perception associated area, the corresponding area is dynamically upgraded to the first perception focus area; for the original first perception focus area, if no specific physiological events occur within a continuous monitoring period, it is downgraded to the second perception associated area; for the first perception focus area, a high-frequency and high-gain data acquisition mode is used, and for the second perception associated area, a conventional frequency and gain configuration is used to achieve adaptive allocation of perception resources; subsequently, based on the updated non-uniform perception topology network, the multi-modal physiological sensing network is controlled to perform differentiated data acquisition, thereby obtaining optimized high-quality multi-dimensional physiological signals.

4. The fast-track rehabilitation modulation system for shoulder surgery in conjunction with analgesic pathway according to claim 3, characterized in that, In the dynamic perception and evaluation module, a multi-modal physiological sensing network is deployed on the shoulder and associated muscle groups of the subject to synchronously collect multi-dimensional physiological signals, and based on a shoulder biomechanical model and a real-time signal feature extraction network, a shoulder dynamic function spectrum reflecting the pain and functional status is constructed in real time. The functions also include: The optimized high-quality multi-dimensional physiological signals are subjected to amplitude-frequency joint normalization processing, and the processed signals are discretized into grid cells in the spatial dimension, and all grid cell data are integrated to form a three-dimensional perception data cube containing time, spatial position, and signal modalities. The spatio-temporal feature coding network is constructed, and the three-dimensional perception data cube is input into the spatio-temporal feature coding network for deep feature extraction and spatial reconstruction; wherein, in the encoding path, a multi-granularity functional abnormality feature set is extracted and formed, which includes microscopic scale features, mesoscopic scale features and macroscopic scale features; in the decoding path, the multi-granularity functional abnormality feature set is fused and up-sampled to gradually restore the spatial resolution, and finally a shoulder dynamic function spectrum graph is synthesized in the output layer, which directly shows the functional abnormality region and intensity in the form of a heat map, wherein each pixel unit in the heat map corresponds to a spatial grid unit.

5. The fast-track rehabilitation modulation system for shoulder surgery in conjunction with analgesic pathway according to claim 4, characterized in that, In the dynamic perception and evaluation module, the functions of the functional abnormality core region include: For each pixel unit in the shoulder dynamic function spectrum graph, a composite functional abnormality score is calculated, which is obtained by nonlinearly weighting and fusing the microscopic scale features, mesoscopic scale features and macroscopic scale features corresponding to the pixel unit in the multi-granularity functional abnormality feature set; A preset functional abnormality score threshold is set, and the composite functional abnormality score of each pixel unit is compared with the functional abnormality score threshold, and is marked as a safe unit or a high-risk unit. A plurality of high-risk units that are adjacent in space are aggregated to form one or more continuous regions, and these regions are marked as functional abnormality core regions.

6. The fast-track regulatory system for shoulder surgery incorporating analgesic pathway according to claim 1, wherein, The functions of the rehabilitation training fusion module are as follows: Taking the anatomical center of the scapula coracoid process of the subject as the origin, a unified shoulder rehabilitation global coordinate system is established, and the coordinates of all pixel units in the shoulder dynamic function spectrum graph and the spatial coordinates of each liquid unit involved in the individualized analgesic path are converted to the global coordinate system through a coordinate transformation matrix; An iterative closest point registration algorithm is used to spatially align the coordinate point set of the functional abnormality core region in the shoulder dynamic function spectrum graph with the spatial coordinate point set of each liquid unit in the individualized analgesic path in the global coordinate system, and a spatial correlation mapping is established; Based on the spatial correlation mapping, one or more spatially closest liquid units are matched for each functional abnormality core region through spatial nearest neighbor search, and a control correlation relationship between the functional abnormality core region and the liquid unit is established; A precise clock synchronization protocol is used to synchronize the data acquisition clock of the multi-modal physiological sensing network with the control clock of the targeted drug infusion system, and a time correlation relationship is established; For each functional abnormality core region, the multi-granularity functional abnormality feature set of all pixel units contained therein is aggregated in the spatial domain, and the statistical quantity of the features of all pixel units in the functional abnormality core region is calculated to generate an aggregated feature vector representing the overall functional abnormality state of the core region; Based on the control correlation relationship and the time correlation relationship, the aggregated feature vector of each functional abnormality core region and the infusion parameters of the matched liquid unit in the same time period are paired to form a set of spatio-temporally aligned data pairs. The set of spatio-temporal alignment data pairs is input into a multi-source information fusion model, which performs feature-level fusion on the aggregated feature vector in each spatio-temporal alignment data pair and the infusion parameter, and obtains a scalar value between 0 and 1 through weighted aggregation, i.e., the comprehensive rehabilitation confidence of the functional abnormality core area under the current analgesic intervention.

7. The fast-track rehabilitation modulation system for shoulder surgery in conjunction with analgesic pathway according to claim 2, wherein, The function of the adaptive decision and execution module is specifically: A multi-objective rehabilitation optimization model is constructed, wherein the multi-objective rehabilitation optimization model includes a safety objective function, an effectiveness objective function, and a tolerance objective function; The comprehensive rehabilitation confidence is input into the multi-objective rehabilitation optimization model as a dynamic weight adjustment factor of the effectiveness objective function, for dynamically adjusting the effectiveness priority of rehabilitation training; A multi-objective optimization algorithm is used to solve the multi-objective rehabilitation optimization model to obtain a set of Pareto optimal solutions; wherein the multi-objective optimization algorithm is a multi-objective optimization algorithm based on a genetic algorithm or a multi-objective optimization algorithm based on a particle swarm optimization; Based on a preset decision strategy, an optimal solution is selected from the set of Pareto optimal solutions, and the individualized rehabilitation training instruction set is generated according to the optimal solution; wherein the decision strategy is a weighted sum method or an epsilon-constraint method, and the individualized rehabilitation training instruction set includes training action type, training intensity, training duration, and training frequency parameters; The individualized rehabilitation training instruction set is real-time issued to the active rehabilitation training device through a rehabilitation device Internet of Things protocol.

8. The fast-track rehabilitation modulation system for shoulder surgery in conjunction with analgesic pathway according to claim 7, characterized in that, The function of the adaptive decision and execution module is specifically: The safety objective function is defined by a preset safety constraint function, which is constructed based on the physiological motion range and the safety load threshold defined by the subject's shoulder anatomy; the effectiveness objective function is defined by a rehabilitation progress function, which is constructed based on the matching degree of historical rehabilitation data and the expected functional recovery trajectory; and the tolerance objective function is defined by a pain tolerance function, which is constructed based on the deviation between the real-time collected subject pain feedback signal and the preset pain threshold.

9. The fast-track regulatory system for shoulder surgery incorporating analgesic pathway according to claim 8, wherein, The function of the closed-loop learning and optimization module is specifically: The physiological feedback data of the subject after executing the individualized rehabilitation training instruction set is continuously monitored; wherein the physiological feedback data is real-time multi-dimensional physiological signals collected from the multi-modal physiological sensing network; Based on the physiological feedback data, a dynamic adaptation degree is calculated; wherein the expected rehabilitation trajectory is an ideal recovery path preset based on historical rehabilitation data, and the dynamic adaptation degree is calculated by calculating the deviation of the physiological feedback data and the expected rehabilitation trajectory at multiple time points; Based on the dynamic adaptation degree, a rehabilitation strategy performance index is generated; wherein the rehabilitation strategy performance index is a quantitative index calculated by weighted fusion of the dynamic adaptation degree and the preset rehabilitation goal achievement degree, for reflecting the effectiveness of the current rehabilitation strategy; Based on the rehabilitation strategy performance index, the strategy parameters of the multi-objective rehabilitation optimization model are updated iteratively online by a reinforcement learning algorithm; wherein the reinforcement learning algorithm uses the rehabilitation strategy performance index as a reward signal, and dynamically adjusts the weight coefficients of the safety target function, the effectiveness target function and the tolerance target function in the multi-objective rehabilitation optimization model.

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