Orthopedic surgery patient position pressure distribution real-time monitoring system and method

Abstract

The application discloses an orthopedic surgery patient body position pressure distribution real-time monitoring system and method, and relates to the technical field of orthopedic surgery body position monitoring; the system comprises a pressure acquisition module, a data processing module, an intelligent early warning module, a body position optimization control module and a traction tension monitoring module. The pressure acquisition module adopts a split type flexible sensing assembly to acquire global pressure and time sequence data. The data processing module quantifies pressure cumulative load through three-dimensional time sequence weighted integration, completes graded risk rating, and drives dynamic threshold adaptive correction with cumulative damage grade. The intelligent early warning module outputs graded early warning signals. The body position optimization control module automatically generates a body position optimization scheme and realizes closed-loop iterative review. The method realizes the whole-process management and control of data acquisition, cumulative quantization, dynamic threshold judgment, graded early warning and closed-loop optimization, and realizes tension and pressure coupling regulation and control for traction surgery. The application can adapt to various special body positions in orthopedics, and improves the pressure risk identification and judgment accuracy.

Description

Technical Field

[0001] This invention relates to the field of orthopedic surgical position monitoring technology, specifically to a real-time monitoring system and method for positional pressure distribution in orthopedic surgical patients. Background Technology

[0002] Orthopedic surgeries, especially spinal surgeries, joint replacements, and limb fracture surgeries, are generally characterized by prolonged operation times, fixed and monotonous patient positions, and continuous pressure on local tissues. Uneven distribution of intraoperative positional pressure can easily lead to complications such as soft tissue ischemia, nerve compression, and even postoperative pressure sores, making it a key aspect of perioperative safety management in orthopedics. Currently available positional pressure monitoring technologies mostly employ fixed pressure thresholds, instantaneous pressure acquisition, and passive alarm modes. These technologies cannot adapt to the unique postural deformations, dynamic changes in tissue tolerance, and the cumulative damage risks from long-term pressure in orthopedic procedures, making it difficult to achieve accurate, individualized, and full-cycle intraoperative pressure safety monitoring.

[0003] Existing technologies cannot quantify the cumulative effect of pressure over time, nor can they dynamically adjust the safety threshold based on individual patient differences, operation duration, and cumulative damage. Furthermore, they lack active positioning optimization and closed-loop verification mechanisms, and tension and pressure under traction cannot be coordinated and controlled, leading to frequent missed or misjudgments of intraoperative pressure risks. Positioning adjustments rely on manual experience, making it difficult to meet the clinical monitoring needs of orthopedic surgery for high precision and high safety.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] The purpose of this invention is to provide a real-time monitoring system and method for the positional pressure distribution of orthopedic surgical patients, in order to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, the present invention provides a real-time monitoring system for patient positional pressure distribution in orthopedic surgery, comprising a pressure acquisition module, a data processing module, an intelligent early warning module, and a positional optimization control module. The pressure acquisition module is used to acquire raw data of the global pressure distribution corresponding to the patient's position during orthopedic surgery in real time, and simultaneously record continuous time-series pressure data. The data processing module incorporates a pressure cumulative load quantification unit and a dynamic threshold determination unit. The pressure cumulative load quantification unit performs weighted integral calculations based on pressure values ​​and time-series data to quantify the real-time intraoperative pressure cumulative load and output a multi-level injury risk rating. The dynamic threshold determination unit binds orthopedic surgical condition parameters with individual patient parameters, adaptively and dynamically updating the positional pressure safety judgment threshold. The dynamic threshold determination unit also receives the injury risk rating output by the pressure cumulative load quantification unit as an input parameter for dynamic threshold correction. The intelligent early warning module is connected to the data processing module and outputs graded early warning signals based on the dynamically updated pressure safety judgment threshold and the injury risk rating corresponding to the pressure cumulative load. The position optimization control module, data processing module, and pressure acquisition module work in a closed-loop linkage to output standardized position fine-tuning optimization parameters based on abnormal pressure distribution data, dynamic threshold deviation, and cumulative injury risk level. It also outputs feedback verification commands to restart the pressure acquisition module for secondary data acquisition, completing closed-loop correction monitoring of the patient's surgical position. This constructs a complete system architecture for orthopedic surgical position pressure monitoring, integrating three differentiated technologies: cumulative pressure load quantification and rating, orthopedic-specific dynamic threshold adaptive judgment, and closed-loop pressure position optimization control. It clearly defines the coupling and synergistic relationship of cumulative load rating as the threshold correction input parameter, enabling pressure quantification, threshold judgment, and position intervention to form a synergistic and synergistic effect that cannot be achieved by individual technologies operating independently. This overcomes the shortcomings of existing systems that rely solely on instantaneous fixed thresholds for risk assessment, lack cumulative injury monitoring, and only provide alarms without proactive intervention. It achieves full coverage monitoring of instantaneous pressure risk and time-series cumulative latent risk, completing a technological upgrade from passive monitoring and alarm to proactive adaptive discrimination and closed-loop optimization.

[0007] Furthermore, the pressure cumulative load quantification unit incorporates a time-series integral calculation subunit. This subunit performs weighted integral calculations on real-time pressure data, pressure duration, and pressure fluctuation frequency in different pressure zones to obtain the real-time pressure cumulative load value for each zone. Through multi-dimensional time-series feature weighted integral calculations, it accurately eliminates instantaneous pressure fluctuation interference, achieving standardized quantification of pressure cumulative load. This overcomes the limitation of traditional instantaneous pressure monitoring in quantifying time-series cumulative damage, providing accurate and reliable data for assessing the risk of hidden cumulative damage.

[0008] Furthermore, the pressure cumulative load quantification unit incorporates a risk rating subunit. This subunit is used to preset orthopedic surgical pressure injury grading standards, match real-time pressure cumulative load values ​​with corresponding injury levels, and output four levels of risk results: safe, warning, mild cumulative injury, and severe cumulative injury. Relying on the orthopedic surgical-specific pressure injury grading standards, it achieves refined risk rating, accurately distinguishes gradient cumulative injury states, and provides quantifiable, gradeable, and identifiable criteria for the gradual, latent cumulative injury in long-term orthopedic surgery, significantly improving the comprehensiveness and accuracy of pressure injury risk identification.

[0009] Furthermore, the dynamic threshold determination unit has a built-in working condition parameter adaptation subunit, which is used to collect and bind the surgical type, real-time surgical duration, patient body shape parameters, and patient age parameters; to achieve precise binding and adaptation between the pressure safety determination standard and the orthopedic surgical working conditions and individual patient vital signs parameters, providing complete and effective basic input parameters for the individualized and working condition-based correction of the dynamic threshold, and ensuring the adaptability and accuracy of the dynamic adjustment of the threshold.

[0010] Furthermore, the dynamic threshold determination unit incorporates a dynamic threshold correction subunit. This subunit adaptively lowers the pressure safety determination threshold for the corresponding pressure area based on the duration of the surgery and the cumulative risk level of the received pressure damage. The longer the surgery and the higher the cumulative risk level, the greater the threshold reduction. This abandons the mechanical determination mode of fixed thresholds used in traditional orthopedic pressure monitoring. Instead, it adaptively tightens the safety determination standard by combining the progress of the surgery with the degree of cumulative damage. This aligns with the clinical pattern of continuously decreasing local tissue tolerance in patients undergoing long surgeries, fundamentally solving the problem of missed detection of high-risk cumulative damage in the later stages of surgery caused by fixed thresholds.

[0011] Furthermore, the pressure acquisition module is a modular, split-type flexible sensor acquisition component. This component is divided into independent sensing units for the head-pillow area, chest-back area, lumbar-abdomen area, limb traction area, and joint flexion area. Each sensing unit adaptively conforms to various special postural deformations during orthopedic surgery. The system also includes a traction tension monitoring module, which is matched to the orthopedic traction surgery scenario settings. This module synchronously acquires traction force values, limb stretching displacement, and local pressure data, and establishes a correlation ratio between traction tension and postural pressure. When the ratio is unbalanced, it automatically generates traction parameter correction schemes and limb positioning angle adjustment schemes. Through the modular, split-type sensing component, it achieves comprehensive, blind-spot-free pressure acquisition across all orthopedic positions. Simultaneously, it adds coupled monitoring of traction tension and postural pressure for traction surgery scenarios, filling the industry gap in the separate monitoring of tension and pressure during traction surgery. This enables bidirectional collaborative management of surgical traction reduction effects and patient postural safety, adapting to various complex orthopedic traction surgery scenarios.

[0012] A method for real-time monitoring of postural pressure distribution in orthopedic surgery patients includes the following steps: S1, real-time acquisition of postural pressure distribution data of all pressure-affected areas of the patient's body during orthopedic surgery, simultaneous recording of continuous time-series pressure data, and continuous output of a real-time dataset of full-domain pressure distribution; S2, based on the real-time pressure distribution dataset and time-series data, quantification of the cumulative pressure load of each pressure-affected area using a pressure time-series weighted integral algorithm, and completion of multi-level injury risk rating by combining with a preset orthopedic injury grading standard; S3, dynamic adaptive updating of the pressure safety judgment threshold for each pressure-affected area based on orthopedic surgery condition parameters, patient individual parameters, and the real-time output of cumulative pressure injury risk level; S4, comparison of real-time pressure distribution data with dynamic safety thresholds, combined with the cumulative injury risk rating, triggering the corresponding level. S5, based on the abnormal pressure area, dynamic threshold deviation value, and cumulative damage risk level, generates standardized body position fine-tuning optimization parameters, performs body position correction, and outputs feedback verification instructions to return to step S1 to restart data acquisition, iterating until the pressure parameters return to the dynamic safe range; This method constructs a full-process intelligent monitoring system of acquisition-quantitative rating-dynamic judgment-graded early warning-closed-loop optimization. Relying on the collaborative logic of cumulative damage risk level driving dynamic threshold update, it realizes deep coupling and linkage of multiple technical steps. Unlike the traditional linear single monitoring process, it can simultaneously identify overt instantaneous high pressure injury and latent time-series cumulative injury. Through continuous iterative closed-loop control, it ensures that the intraoperative body position pressure is maintained within the safe range throughout the operation, greatly improving the control accuracy and safety.

[0013] Further, step S2 includes the following sub-steps: S21, extracting three types of time-series feature data—real-time pressure value, pressure duration, and pressure fluctuation frequency—for each region; S22, performing weighted integral calculation on the three types of time-series feature data to eliminate interference from instantaneous pressure fluctuations and calculating the standardized cumulative pressure load value for each pressure-bearing region; S23, matching the cumulative pressure load value with a preset orthopedic surgery pressure injury grading threshold and outputting the corresponding four-level injury risk rating result; through weighted integral calculation of multi-dimensional time-series feature fusion, data errors caused by instantaneous pressure fluctuations are avoided, achieving standardized, accurate, and quantifiable pressure time-series cumulative injury, and completing risk stratification and rating based on orthopedic-specific grading standards, effectively improving the identification accuracy of latent pressure cumulative injury in long-term orthopedic surgery.

[0014] Furthermore, step S3 includes the following sub-steps: S31, real-time acquisition of current surgical type, surgical duration, patient body type, and age parameters; S32, binding real-time updated pressure cumulative injury risk level to establish a correspondence between surgical sequence and pressure tolerance; S33, progressively lowering the pressure safety judgment threshold of the corresponding pressure area according to the progress of the surgery and the degree of cumulative load increase, generating a real-time dynamic safety judgment standard; constructing a dynamic threshold iterative update mechanism adapted to the entire orthopedic surgery process, combining surgical conditions, individual patient differences, and real-time cumulative injury status to adjust the safety judgment standard in real time, completely breaking the limitations of traditional fixed thresholds, accurately adapting to the dynamically changing pressure injury risk during surgery, and effectively reducing the situation of risk misjudgment and omission.

[0015] Furthermore, it also includes a traction tension monitoring step: S6, in the context of orthopedic traction surgery, simultaneously collect traction force values, limb stretching displacement, and local pressure data; S7, construct a correlation model between traction tension and body position pressure, and automatically generate traction parameter correction schemes and limb placement angle adjustment schemes when the ratio is unbalanced; for the specific scenario of orthopedic traction surgery, it realizes coupled monitoring and coordinated control of traction mechanical parameters and body position pressure parameters, effectively avoiding traction injury and local compression injury while ensuring the accuracy of surgical traction, and achieving dual protection of surgical efficacy and patient safety.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] 1. This invention constructs a complete closed-loop system encompassing pressure acquisition, data processing, intelligent early warning, position optimization control, and traction tension monitoring, enabling real-time monitoring and dynamic adaptive control of positional pressure during orthopedic surgery. The system uses cumulative injury risk to drive dynamic threshold correction, creating a deeply coupled and synergistic system of pressure quantification, risk rating, threshold updates, and positional intervention. This overcomes the limitations of traditional fixed threshold monitoring, significantly improving the comprehensiveness and accuracy of pressure risk identification.

[0018] 2. This invention adopts a split modular flexible sensing component, which can adaptively fit various special orthopedic positions, eliminate monitoring blind spots, and achieve accurate quantification of latent cumulative damage by combining a three-dimensional time-series weighted integral model. With the help of an orthopedic-specific four-level risk rating system, it can achieve refined layered identification of progressive pressure damage, effectively making up for the shortcomings of traditional instantaneous monitoring that cannot cover time-series cumulative risks.

[0019] 3. This invention achieves standardized and precise proactive intervention through an iterative closed-loop positioning optimization process, eliminating reliance on manual experience and reducing the risk of secondary compression. Simultaneously, by coupling and monitoring traction tension and positional pressure, it balances surgical traction accuracy with patient positioning safety, comprehensively improving the level of intraoperative positioning safety management in orthopedic surgery and reducing postoperative complications. Attached Figure Description

[0020] Figure 1 A schematic diagram of the principle of a real-time monitoring system for pressure distribution in orthopedic surgical patients.

[0021] Figure 2 A flowchart for a method of real-time monitoring of positional pressure distribution in orthopedic surgical patients. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figures 1 to 2 This invention provides a technical solution: a real-time monitoring system and method for pressure distribution in orthopedic surgery patients. It is mainly applied to various long-duration, special-position orthopedic clinical surgical scenarios such as spinal surgery, limb fracture reduction surgery, joint replacement surgery, and orthopedic traction and correction surgery. It can achieve real-time monitoring of instantaneous pressure risk and cumulative pressure injury risk at the pressure points of patients in unconventional positions such as prone, lateral, limb suspension, joint flexion, and continuous traction. It also enables dynamic threshold adaptive correction, graded intelligent early warning, and closed-loop position optimization and control. This solves the industry pain points of traditional orthopedic position pressure monitoring technologies, such as fixed thresholds, single risk identification, lack of cumulative damage quantification, alarms without active intervention, lack of closed-loop review and correction, and disconnect between traction and pressure monitoring. This technical solution adopts a modular hardware architecture combined with iterative algorithm logic. Relying on multidimensional integral quantification technology of pressure time series, dynamic correction technology of load linkage threshold, and progressive closed-loop body position control technology, it constructs a full-process intelligent monitoring system for data collection, quantification, judgment, early warning, optimization and verification. Compared with the traditional fixed pressure monitoring, single instantaneous data monitoring and manual experience adjustment technology, it has stronger scenario adaptability, comprehensive risk identification and clinical safety.

[0024] Taking posterior lumbar decompression, bone grafting and internal fixation surgery in adults as an example, the estimated operation time is four hours. The patient's body mass index is in the overweight range, and the age is 56 years old. The entire operation is performed in a prone position. The head, chest and back, waist and abdomen, bilateral hip joints and lower limb joints are the main pressure areas. The limb traction reduction operation needs to be performed in stages during the operation. This operation is a typical orthopedic surgical scenario with long duration, high pressure risk and traction control requirements. It can fully adapt to all the monitoring, calculation and control functions of this system and method, as detailed below.

[0025] The overall implementation architecture of this system: The real-time monitoring system for postural pressure distribution in orthopedic surgery patients of this invention has a hardware and software collaborative architecture that includes a pressure acquisition module, a data processing module, an intelligent early warning module, a postural optimization control module, and a traction tension monitoring module. These five functional modules work together in a closed-loop linkage to achieve intelligent monitoring and control of postural pressure in orthopedic surgery across all time periods, regions, and risk types. The pressure acquisition module is responsible for the raw acquisition of pressure and time-series data across the entire area, serving as the foundation for system data input. The data processing module is the core computing center, undertaking the core calculations of cumulative load quantification, damage risk rating, and dynamic threshold adaptive correction. The intelligent early warning module is responsible for risk classification output and alert push notifications. The postural optimization control module undertakes active intervention and closed-loop iterative control functions. The traction tension monitoring module is adapted to orthopedic traction scenarios, achieving coupled and coordinated control of tension and pressure. Each module has a clear hierarchy and well-defined functions, while also exhibiting a core parameter linkage relationship, distinguishing it from the traditional system's single-module operation mode without data interaction.

[0026] Pressure Acquisition Module: Utilizing modular, split-type flexible sensing components, this module enables real-time acquisition of pressure data across the entire body's pressure-affected areas during orthopedic surgery, along with continuous temporal pressure data. This provides comprehensive raw data support for backend data processing and risk assessment. The implementation is based on the clinical characteristics of orthopedic surgery, including diverse patient positions, high degrees of deformation, and dispersed localized pressure areas. Conventional integrated sensing devices cannot adapt to these specific postural deformations, resulting in monitoring blind spots and hindering the achievement of comprehensive data acquisition. Specific technical methods include:

[0027] The pressure acquisition module employs a modular, split-type flexible sensor assembly, abandoning the traditional integrated, single-plate sensor structure. It features independent sensor units arranged according to the pressure characteristics of orthopedic surgery, specifically divided into head-and-back, chest-and-back, lumbar-abdomen, limb traction, and joint flexion zones. Each sensor unit uses a highly flexible, conforming material that adaptively deforms to fit various orthopedic positions, such as prone, lateral, limb flexion, and limb traction stretching, eliminating blind spots and monitoring dead zones. During operation, the module continuously collects raw pressure distribution data from each sensor point in real time. Simultaneously, it incorporates a time-series recording function, continuously capturing the start time, duration, and pressure fluctuations of each pressure area, and uninterruptedly outputting a real-time dataset of the overall pressure distribution and corresponding pressure duration time-series data, achieving uninterrupted data acquisition throughout the entire surgical procedure.

[0028] Example: During posterior lumbar spine surgery in a prone position, the head-occipital region, chest-back region, and lumbar-abdomen region are the core static pressure areas. The limb traction area and joint flexion area are adapted to the dynamic pressure scenario of lower limb traction and reduction operations during the operation. The split-type sensing unit can be respectively attached to the patient's head support position, chest-back support pad area, lumbar-abdomen suspended pressure edge, lower limb traction fixation position, and knee and hip joint flexion position to collect different pressure values ​​in each area in real time. At the same time, it records the duration of pressure in each area and the pressure fluctuation data caused by intraoperative position fine-tuning and limb traction, fully covering all static and dynamic pressure conditions during the operation.

[0029] Existing publicly available technologies mostly use integrated pressure-sensing mattresses, which can only collect pressure data in a flat, supine position. This is insufficient for the diverse and challenging positions required in orthopedic surgery, resulting in low accuracy and numerous blind spots in zoned monitoring. The unique feature of this technical solution lies in its use of a modular, segmented sensing architecture, specifically adapted to the diverse and challenging positions required in orthopedic surgery. This enables comprehensive, blind-spot-free data collection, effectively reducing missed or incorrect pressure data collection during surgery and significantly improving the completeness and adaptability of the original monitoring data.

[0030] The data processing module uses time-series integral quantization: based on real-time pressure data and time-series data output from the pressure acquisition module, standardized cumulative pressure load values ​​for each pressure zone are generated through multi-dimensional feature weighted integral calculation, enabling quantifiable calculation of time-series cumulative damage. The implementation is based on the understanding that the core pressure injury from long-term orthopedic surgery originates from the time-series cumulative effect of continuous compression and dynamic fluctuations. Existing technologies only collect instantaneous pressure values ​​and cannot quantify the latent damage risks caused by time accumulation. Specific technical methods include:

[0031] The pressure cumulative load quantification unit within the data processing module is equipped with a time-series integral calculation subunit. During operation, it extracts three core time-series features from each pressure point in each region: real-time pressure value, pressure duration, and pressure fluctuation frequency, abandoning the traditional single-pressure value calculation dimension. The time-series integral calculation subunit performs a weighted integral calculation on the three time-series features, distinguishing the influence weight of different features on pressure injury through weighting coefficients. It eliminates invalid pressure fluctuation interference caused by intraoperative instantaneous postural adjustments and brief limb tremors, retaining effective pressure-bearing time-series data. Through integral calculation, it integrates the coupled influence of pressure intensity and duration, ultimately calculating standardized and comparable real-time pressure cumulative load values ​​for each pressure area, achieving continuous quantification of discrete pressure time-series data.

[0032] Example: During a four-hour prone lumbar spine surgery, the patient's lumbar and abdominal regions are under prolonged high pressure. While the pressure fluctuations are gradual, the duration is extremely long. In contrast, the limb traction areas experience frequent pressure fluctuations and significant instantaneous pressure changes during intraoperative traction procedures. This unit uses weighted integral calculations to comprehensively calculate the long-term cumulative high pressure effect in the lumbar and abdominal regions and the fluctuating pressure cumulative effect in the limb regions. It filters out transient, ineffective pressure fluctuations caused by minor touches from medical staff or slight equipment movements during surgery, accurately outputting the true cumulative pressure load values ​​for each region and avoiding the distortion in quantification of cumulative damage caused by instantaneous data interference.

[0033] Existing publicly available technologies mostly rely on instantaneous pressure threshold comparisons for judgment, lacking the ability to perform time-series integration and multi-dimensional feature fusion calculations, thus failing to quantify the cumulative damage caused by long-term compression. The unique approach of this technical solution lies in its weighted integral calculation, which integrates three-dimensional features—pressure value, duration, and fluctuation frequency—to achieve precise quantification of pressure-related cumulative damage over time. This effectively compensates for the technical deficiency of instantaneous pressure monitoring in identifying latent chronic injuries, significantly improving the accuracy of quantifying pressure damage from long-term orthopedic surgery.

[0034] The data processing module employs a four-level risk rating system: based on the quantified cumulative pressure load value and relying on orthopedic-specific injury grading standards, it completes a four-level risk stratification rating, achieving a refined classification and determination of cumulative injury risk. The implementation is based on the fact that the general pressure injury grading standard is suitable for ordinary bedridden scenarios, but cannot match the high-intensity, long-duration, and positionally specific pressure injury characteristics of orthopedic surgery. This results in insufficient accuracy and poor adaptability in risk grading. Specific technical methods include:

[0035] The pressure cumulative load quantification unit incorporates a risk rating subunit. The system pre-stores a pressure injury grading standard specifically adapted for orthopedic surgical scenarios, distinct from general medical pressure grading rules. The risk rating subunit receives the pressure cumulative load values ​​for each region from the time-series integration calculation subunit. It matches the real-time cumulative load values ​​against preset four-level grading thresholds one by one, and outputs four risk level results according to the gradient range of the load values: safe level, warning level, mild cumulative injury level, and severe cumulative injury level. Different levels correspond to different degrees of tissue compression, nerve ischemia, and soft tissue injury risk, achieving stratified identification of progressive, latent cumulative injury. This transforms the previously indiscernible time-series cumulative injury into a standardized risk result that is quantifiable, gradable, and predictable.

[0036] Example: In the early stage of lumbar prone surgery, the cumulative load values ​​of various regions of the patient are low, and the system judges it to be at a safe level, requiring no intervention; in the middle stage of surgery, the cumulative load in the lumbar and abdominal and thoracic and back regions continues to increase, reaching the warning level, and the system initiates basic monitoring and warning; in the later stage of surgery, as the duration of pressure continues to increase, the cumulative load further increases, and some areas reach the level of mild cumulative injury, while special high-risk pressure areas can reach the level of severe cumulative injury. The system accurately distinguishes the risk of cumulative injury in different regions and at different degrees, providing a precise grading basis for subsequent dynamic threshold correction and postural intervention.

[0037] Existing publicly available technologies mostly employ a simple two-level risk assessment, distinguishing only between normal and abnormal states, and cannot identify tiered cumulative damage risks. The unique approach of this technical solution lies in establishing a four-level pressure injury rating system specifically for orthopedic surgery. This enables refined tiered assessment of cumulative damage risks, effectively improving the ability to identify progressive, latent injuries during long-term orthopedic surgery, making risk management more targeted and tiered.

[0038] Dynamic threshold parameter adaptation: This involves collecting and binding orthopedic surgical parameters with individual patient parameters to provide comprehensive basic parameter support for the adaptive correction of dynamic pressure safety thresholds, achieving individualized and scenario-based adaptation of threshold determination standards. The implementation is based on the fact that traditional fixed thresholds use a uniform standard, failing to differentiate based on surgical type, surgical duration, patient body size, age, and other varying factors, thus failing to adapt to the pressure tolerance differences among different patients and surgical procedures. Specific technical methods include:

[0039] The dynamic threshold determination unit incorporates a condition parameter adaptation subunit. The system collects and binds multi-dimensional core parameters in real time before and during surgery. These parameters include surgical type, real-time surgical duration, patient physical characteristics, and patient age. The condition parameter adaptation subunit integrates and binds these parameters to construct an individualized parameter database specific to the current surgery and patient. All subsequent dynamic threshold correction calculations are based on this set of exclusive parameters, ensuring that the pressure safety determination standard is no longer a universal, fixed value, but rather a dedicated judgment benchmark deeply matched to the surgical conditions and the patient's individual tolerance.

[0040] Example: This surgery is a delicate posterior lumbar spine orthopedic procedure with a long estimated duration. The patient is an elderly, overweight individual with lower tolerance to local skin and nerve pressure compared to younger, normal-weight patients. The operating condition parameter adaptation subunit records and binds the lumbar spine surgery type, four-hour estimated duration, patient overweight parameters, and age parameters in real time. Based on these individualized parameters, a threshold correction model specific to this surgery is constructed. This provides fundamental support for dynamic threshold reduction and accurate risk assessment during subsequent surgical procedures, avoiding judgment bias caused by applying general thresholds to elderly patients undergoing long surgeries.

[0041] Existing publicly available technologies generally use a uniform, fixed pressure threshold, lacking the ability to adapt and bind operating conditions to individual parameters, thus failing to achieve individualized threshold adaptation. The unique approach of this technical solution lies in its multi-dimensional binding of orthopedic surgical conditions with individual patient parameters, constructing a foundation for individualized threshold adaptation. This completely overcomes the limitations of homogeneous judgment using universal, fixed thresholds, significantly improving the individualized adaptability of pressure safety assessment standards.

[0042] Dynamic threshold linkage correction: Based on the duration of surgery and the real-time cumulative damage risk level, the pressure safety threshold is adaptively and progressively lowered to achieve dynamic linkage updates between the threshold and the surgical progress and damage severity. The rationale is that during prolonged orthopedic surgery, the patient's local tissue tolerance continuously decreases with the duration of pressure and the increase in cumulative damage. Fixed thresholds cannot adapt to dynamically changing tolerance, easily leading to missed detection of high-risk cases later. Specific technical methods include:

[0043] The dynamic threshold determination unit incorporates a dynamic threshold correction subunit. This subunit forms a core data linkage with the pressure cumulative load quantification unit, receiving the cumulative damage risk level output by the risk rating subunit in real time, while simultaneously acquiring the real-time surgical progress duration. The dynamic threshold correction subunit establishes an adaptive threshold reduction logic based on the progression of surgical time and the gradient of risk levels. The longer the surgical progress duration, the higher the cumulative damage risk level of the corresponding pressure area, and the greater the reduction in the pressure safety determination threshold for that area, achieving step-by-step, adaptive, and differentiated threshold correction. Through this linkage mechanism, the pressure safety determination standard tightens synchronously as the patient's tissue tolerance decreases, matching the dynamically changing damage risk state during surgery.

[0044] Example: In the early stages of surgery, the operation time is short and the cumulative risk of damage is at a safe level, so the threshold remains at the initial adaptation standard; in the middle stages of surgery, the operation time gradually increases and the risk rises to the warning level, so the threshold is slightly lowered and the safety judgment standard is tightened; in the later stages of surgery, the operation time is close to the estimated time and the local area reaches the level of mild or severe damage, so the threshold is significantly lowered to accurately identify subtle pressure abnormalities after the patient's tolerance decreases, avoiding the problem of missed detection of hidden damage caused by reduced tissue tolerance in the later stages of long surgery.

[0045] Existing publicly available technologies use fixed thresholds throughout the entire procedure, lacking dynamic correction and risk-linked adjustment capabilities, and thus failing to adapt to the dynamically changing tissue tolerance during surgery. The unique approach of this technical solution lies in its use of a dual-factor linkage threshold correction mechanism based on cumulative damage risk level and surgical duration. This enables dynamic adaptive tightening of safety assessment standards, effectively solving the industry-wide problem of missed assessments of pressure injuries in the later stages of long orthopedic surgeries, and significantly improving the accuracy of risk assessment throughout the entire surgical cycle.

[0046] Intelligent early warning tiered output: Combining dynamically updated pressure safety thresholds and real-time cumulative injury risk levels, corresponding tiered early warning signals are output to achieve differentiated risk alerts. This is based on the fact that traditional monitoring technologies only have a single alarm function, cannot distinguish between instantaneous pressure anomalies and cumulative pressure injury risks, lack tiered early warning capabilities, and prevent medical personnel from accurately assessing the severity of risks and intervention priorities. Specific technical measures include:

[0047] The intelligent early warning module and data processing module are fully interconnected, receiving the latest safety thresholds updated by the dynamic threshold judgment unit in real time, while simultaneously acquiring the cumulative damage risk rating results for each area. The system compares real-time pressure data across the entire domain with dynamic safety thresholds in real time, identifying anomalies in instantaneous pressure exceeding limits. It also comprehensively determines the overall risk status by combining the four-level cumulative risk level. Based on the degree of abnormality and risk level, it outputs different levels of graded early warning signals, distinguishing between mild, moderate, and severe high-risk warnings, thus achieving differentiated and graded risk alerts and providing medical staff with accurate risk priority judgment criteria.

[0048] Example: When the intraoperative local instantaneous pressure slightly exceeds the standard and the cumulative risk reaches the warning level, the system outputs a mild warning to remind medical staff to pay attention to the patient's position; when the intraoperative high pressure persists and the cumulative risk reaches the level of mild injury, a moderate warning is output to remind patients to make timely fine adjustments to the patient's position; when the intraoperative pressure is severely exceeded and the cumulative risk reaches the level of severe injury, a severe high-risk warning is output, forcibly triggering the patient's position optimization intervention process, thereby achieving differentiated warning and control for different risk scenarios.

[0049] Most existing publicly available technologies rely on single audible and visual alarms, lacking tiered early warning systems and the ability to differentiate between instantaneous and cumulative risks. The unique approach of this technical solution lies in combining dynamic threshold instantaneous anomalies with cumulative damage risk grading to achieve dual-tiered early warning. This accurately distinguishes risk types and levels, effectively improving the relevance of risk alerts and preventing medical staff from misjudging or ignoring high-risk hidden risks.

[0050] Postural optimization closed-loop control: Based on abnormal pressure data, threshold deviation, and cumulative risk level, intelligent postural fine-tuning parameters are generated, postural correction is performed, and iterative closed-loop verification is initiated until the pressure state returns to a safe range. This approach is based on the fact that traditional technologies only issue alarms without active intervention, and postural adjustments rely entirely on human experience, resulting in low accuracy, a high risk of secondary injury, and the lack of a verification and iteration mechanism, thus failing to guarantee long-term stability of the adjustment effect. Specific technical methods include:

[0051] The postural optimization control module, data processing module, and pressure acquisition module form a complete closed-loop linkage structure. The system first automatically locates the abnormal pressure area, extracts the deviation value between the current pressure data and the dynamic safety threshold, matches the corresponding cumulative injury risk level, and intelligently calculates and generates standardized postural fine-tuning optimization parameters based on the preset pressure-postural correlation model. The parameters include precise control data such as the postural elevation angle, the increase in padding thickness, and the limb offset distance. The system outputs a standardized postural optimization plan to guide medical staff to complete the postural correction operation. After the correction is completed, it immediately outputs a feedback review instruction, restarts the pressure acquisition module to carry out a new round of full-domain data acquisition, and iteratively executes the monitoring, judgment, and optimization process to continuously correct the postural pressure state until the pressure parameters of all pressure areas stably return to the dynamic safety range, completing the closed-loop correction monitoring.

[0052] Example: When a patient experiences persistent high pressure in the lumbar and abdominal region and the cumulative risk reaches the level of mild injury, the system automatically locates the high-pressure area, calculates the corresponding padding thickening parameters and the angle of slight elevation of the body position, and pushes a standardized optimization plan. After medical staff complete the fine-tuning of the body position according to the standard parameters, the system immediately restarts data collection, verifies the pressure improvement effect, and if the pressure still does not meet the standard, it continues to iterate and fine-tune until the pressure in the lumbar and abdominal region returns to the dynamic safe range, completely eliminating the risk of cumulative injury.

[0053] Existing publicly available technologies lack active parameter optimization and closed-loop iterative verification functions, relying on human experience for positional adjustments, resulting in high randomness and low safety. The unique approach of this technical solution lies in achieving risk data-driven standardized positional parameter fine-tuning and iterative closed-loop correction, eliminating reliance on human experience, effectively reducing the risk of secondary pressure imbalance caused by manual adjustments, and continuously ensuring that intraoperative positional pressure remains at a safe level.

[0054] Traction tension-pressure coupling monitoring: For orthopedic traction surgery scenarios, traction tension and postural pressure data are collected simultaneously to construct a coupled correlation model, enabling automatic correction and control of tension-pressure imbalances. This is based on the fact that traditional orthopedic monitoring techniques involve independent traction tension and postural pressure monitoring, failing to simultaneously ensure traction reduction accuracy and postural safety, easily leading to traction injuries or pressure-induced injuries due to over-traction. Specific technical methods include:

[0055] The system incorporates a dedicated traction tension monitoring module, adaptable to orthopedic traction surgeries such as fracture reduction and spinal correction. During surgery, it simultaneously collects traction force values, limb stretching displacement data, and local pressure data at corresponding sites. The module internally constructs a dynamic correlation model between traction tension and body position pressure, analyzing the coupling and matching relationship between traction force, stretching displacement, and local pressure in real time. When an imbalance in the tension-pressure ratio occurs, it automatically generates corresponding traction parameter correction schemes and limb positioning angle adjustment schemes. While ensuring the effectiveness of traction reduction, it optimizes the local pressure state, achieving coordinated control of traction regulation and pressure safety.

[0056] Example: During lower limb traction reduction in lumbar spine surgery, the system simultaneously collects data on traction force, lower limb displacement, and pressure in the hip and knee joint areas. It analyzes the matching relationship between traction force and local pressure in real time. When excessive traction force leads to abnormal increase in local pressure or insufficient traction force leads to poor reduction effect, the system automatically outputs a traction parameter fine-tuning scheme and limb placement optimization angle to ensure fracture reduction accuracy while avoiding local high pressure compression and excessive traction injury.

[0057] Existing publicly available technologies cannot achieve coupled monitoring and coordinated control of traction tension and postural pressure; the two parameters are monitored independently and without interaction. The unique approach of this technical solution lies in constructing an orthopedic-specific tension-pressure coupling correlation model, enabling bidirectional coordinated control of traction efficacy and postural safety. This fills the technical gap in the coordinated monitoring of two parameters during traction surgery and effectively reduces the dual risks of injury during traction surgery.

[0058] Full-process iterative monitoring method: This method achieves full-time, full-risk closed-loop intelligent monitoring of postural pressure in orthopedic surgery by collecting, quantifying, judging thresholds, issuing early warnings, and optimizing iteratively. The implementation is based on the fact that traditional monitoring methods are linear, single-shot processes, unable to achieve dynamic threshold updates, cumulative risk quantification, and closed-loop iterative optimization. Their monitoring completeness and dynamic adaptability are extremely poor. Specific technical means include:

[0059] This monitoring method relies on the aforementioned hardware system architecture to execute a complete iterative process. First, it collects real-time pressure and time-series data throughout the entire surgical procedure, forming a continuous monitoring dataset. Second, it quantifies the accumulated load through three-dimensional time-series weighted integral calculation, completing a four-level risk stratification rating. Third, it dynamically updates the pressure safety threshold for each region by combining surgical conditions, individual parameters, and real-time accumulated risk. Subsequently, it compares the dynamic threshold with real-time pressure data, triggering tiered early warning signals. Finally, it generates standardized postural fine-tuning parameters based on risk data, performs corrections, iteratively restarts data acquisition, and cyclically adjusts until the pressure state is stable and safe. For traction surgery scenarios, a tension-pressure coupling monitoring and correction process is simultaneously superimposed, achieving full coverage of general and special scenario monitoring. The method adopts a progressively refined logic, with each step linked and data interconnected, forming a dynamic and cyclical intelligent monitoring system, rather than the traditional static single-time monitoring mode.

[0060] Example: This iterative monitoring process runs continuously throughout a four-hour lumbar spine surgery. From the moment the patient is positioned and monitoring begins until the position is removed at the end of the surgery, it continuously performs cyclical operations of data collection, cumulative quantification, threshold updates, risk warnings, position optimization, and effect verification. It dynamically adapts to various pressure changes caused by the progress of the surgery, the aggravation of cumulative damage, intraoperative traction operations, and fine-tuning of the patient's position, ensuring the safety of the patient's position and the stability of the surgical procedure throughout the entire process.

[0061] Existing publicly available monitoring methods are linear and simplistic, lacking iterative closed-loop processes, threshold-linked updates, and cumulative damage quantification. The unique approach of this technical solution lies in constructing a coupled iterative process where cumulative risk drives threshold updates, achieving dual control over both instantaneous and latent cumulative risks. Through continuous closed-loop iterative optimization, it significantly improves the dynamic adaptability and safety control accuracy of intraoperative pressure monitoring.

[0062] In summary, most existing orthopedic positional pressure monitoring technologies, both domestically and internationally, focus on positional pressure monitoring during general surgery. They typically employ basic technical models such as fixed threshold judgment, instantaneous single-point pressure monitoring, integrated sensor acquisition, and passive alarm prompts. These technologies can only achieve simple overpressure alarm functions and have not been optimized for the specific clinical characteristics of orthopedic surgery, including long duration, special positions, traction operations, and progressive cumulative damage. They generally suffer from technical defects such as large monitoring blind spots, single risk identification, rigid judgment criteria, lack of active intervention capabilities, lack of closed-loop iteration mechanisms, and disconnect between traction and pressure monitoring.

[0063] This technical solution is unique in that it breaks through the traditional fixed threshold monitoring model and adopts a core coupling mechanism of dynamic correction of cumulative damage risk-linked thresholds. This allows pressure quantification, risk rating, threshold updates, and postural intervention to form a synergistic system, producing a collaborative monitoring effect that traditional single technologies cannot achieve. A modular, split-type sensor architecture is designed for the diverse and special postural positions in orthopedic surgery, solving the problems of poor adaptability and numerous blind spots in traditional integrated sensor devices, achieving accurate data acquisition across all positions. A three-dimensional weighted integral quantification model of pressure value duration fluctuation frequency is constructed to address the pain point of traditional instantaneous monitoring failing to identify latent temporal cumulative damage, achieving accurate quantification of chronic compression damage from long-term surgery. A four-level cumulative risk rating system specifically for orthopedics is established to achieve refined stratified identification of gradient latent damage, improving the targeting of risk management. An iterative closed-loop postural optimization and control process is designed to eliminate reliance on manual experience, achieving standardized, precise, and sustainable proactive intervention, changing the traditional passive alarm monitoring mode. A new traction tension-pressure coupling monitoring and control mechanism fills the technical gap in dual-parameter collaborative monitoring of orthopedic traction surgery, achieving dual-way collaborative protection of surgical efficacy and patient safety.

[0064] Compared to existing publicly available technical solutions, the overall technical system of this invention is specifically adapted to the clinical scenario of orthopedic surgery. It solves several technical bottlenecks that have long existed in the industry, such as missed diagnosis of cumulative damage during long-term surgery, incomplete monitoring of special positions, insufficient accuracy of manual positioning, disconnect between traction and pressure monitoring, and lack of closed-loop dynamic control. Through an innovative architecture of multi-module coupling and linkage, multi-dimensional data fusion, and iterative closed-loop control, it significantly improves the comprehensiveness of identification, accuracy of judgment, and standardization of intervention for positional pressure risks in orthopedic surgery patients, effectively reducing the probability of postoperative positional complications in orthopedic surgery and possessing strong clinical applicability.

Claims

1. A real-time monitoring system for pressure distribution in orthopedic surgical patients, characterized in that: It includes a pressure acquisition module, a data processing module, an intelligent early warning module, and a body position optimization control module; The pressure acquisition module is used to acquire raw data of global pressure distribution corresponding to the patient's position during orthopedic surgery in real time, and to record pressure duration data simultaneously. The data processing module has a built-in pressure cumulative load quantification unit and a dynamic threshold determination unit. The pressure cumulative load quantification unit is used to perform weighted integral calculation based on pressure values ​​and time series data to quantify the real-time pressure cumulative load during the operation and output a multi-level damage risk rating. The dynamic threshold determination unit is used to bind orthopedic surgical condition parameters and patient individual parameters, adaptively and dynamically update the position pressure safety determination threshold, and the dynamic threshold determination unit receives the damage risk rating output by the pressure cumulative load quantification unit as the input parameter for dynamic threshold correction. The intelligent early warning module is connected to the data processing module and is used to output graded early warning signals based on the dynamically updated pressure safety judgment threshold and the damage risk rating corresponding to the pressure cumulative load. The position optimization control module works in a closed loop with the data processing module and the pressure acquisition module. Based on abnormal pressure distribution data, dynamic threshold deviation, and cumulative injury risk level, it outputs standardized position fine-tuning optimization parameters and outputs feedback verification instructions to restart the pressure acquisition module for secondary data acquisition, thus completing the closed-loop correction monitoring of the patient's surgical position.

2. The real-time monitoring system for patient positional pressure distribution in orthopedic surgery as described in claim 1, characterized in that: The pressure cumulative load quantification unit has a built-in time-series integral calculation subunit, which is used to perform weighted integral calculation on the real-time pressure data, pressure duration, and pressure fluctuation frequency of different pressure areas to obtain the real-time pressure cumulative load value of each area.

3. The real-time monitoring system for patient positional pressure distribution in orthopedic surgery as described in claim 1, characterized in that: The pressure cumulative load quantification unit has a built-in risk rating subunit. The risk rating subunit is used to preset the orthopedic surgery pressure injury grading standard, match the real-time pressure cumulative load value with the corresponding injury level, and output four levels of risk results: safe, warning, mild cumulative injury, and severe cumulative injury.

4. The real-time monitoring system for patient positional pressure distribution in orthopedic surgery as described in claim 1, characterized in that: The dynamic threshold determination unit has a built-in working condition parameter adaptation subunit, which is used to collect and bind the surgery type, real-time surgery duration, patient body shape parameters, and patient age parameters.

5. The real-time monitoring system for patient positional pressure distribution in orthopedic surgery as described in claim 1, characterized in that: The dynamic threshold determination unit has a built-in threshold dynamic correction subunit. The threshold dynamic correction subunit is used to adaptively lower the pressure safety determination threshold of the corresponding pressure area step by step according to the duration of the operation and the cumulative damage risk level of the received pressure. The longer the operation and the higher the cumulative damage risk level, the greater the threshold reduction.

6. The real-time monitoring system for patient positional pressure distribution in orthopedic surgery as described in claim 1, characterized in that: The pressure acquisition module is a modular, split, flexible sensor acquisition component. This component is divided into independent sensing units for the head-pillow area, chest-back area, lumbar and abdominal area, limb traction area, and joint flexion area. Each sensing unit adaptively conforms to various special body position deformations during orthopedic surgery. The system also includes a traction tension monitoring module, which matches the orthopedic traction surgery scenario settings. This module synchronously acquires traction force values, limb stretching displacement, and local pressure data, and establishes a correlation between traction tension and body position pressure. When the correlation is unbalanced, it automatically generates traction parameter correction schemes and limb placement angle adjustment schemes.

7. A method for real-time monitoring of postural pressure distribution in orthopedic surgical patients, applied to the real-time monitoring system for postural pressure distribution in orthopedic surgical patients as described in any one of claims 1 to 6, characterized in that: Includes the following steps: S1 collects real-time data on the positional pressure distribution of the patient's body in all pressure-affected areas during orthopedic surgery, records continuous time-series data of pressure, and continuously outputs a real-time dataset of global pressure distribution. S2, based on real-time pressure distribution dataset and time series data, quantifies the cumulative pressure load of each pressure area through a pressure time series weighted integral algorithm, and completes multi-level injury risk rating by combining the preset orthopedic injury grading standard. S3 dynamically and adaptively updates the pressure safety judgment threshold of each pressure area based on orthopedic surgery conditions, individual patient parameters, and real-time output of pressure cumulative injury risk level. S4, compare real-time pressure distribution data with dynamic safety thresholds, and combine cumulative damage risk rating to trigger pressure risk warnings of the corresponding level; S5. Based on the abnormal pressure area, dynamic threshold deviation value, and cumulative injury risk level, generate standardized body position fine-tuning optimization parameters, perform body position correction, and output feedback verification instructions to return to step S1 to restart data acquisition. Iterate until the pressure parameters return to the dynamic safe range.

8. The method for real-time monitoring of pressure distribution in orthopedic surgical patients as described in claim 7, characterized in that: Step S2 includes the following sub-steps: S21, extracting three types of time-series feature data for each region: real-time pressure value, pressure duration, and pressure fluctuation frequency; S22, performing weighted integral calculation on the three types of time-series feature data to eliminate instantaneous pressure fluctuation interference and calculate the standardized cumulative pressure load value for each pressure-bearing region; S23, matching the cumulative pressure load value with the preset orthopedic surgical pressure injury grading threshold and outputting the corresponding four-level injury risk rating result.

9. The method for real-time monitoring of pressure distribution in orthopedic surgical patients as described in claim 8, characterized in that: Step S3 includes the following sub-steps: S31, real-time acquisition of current surgical type, surgical duration, patient body type and age individual parameters; S32, binding real-time updated pressure cumulative injury risk level, establishing a correspondence between surgical sequence and pressure tolerance; S33, according to the progress of the surgery and the degree of cumulative load increase, progressively lowering the pressure safety judgment threshold of the corresponding pressure area, generating a real-time dynamic safety judgment standard.

10. The method for real-time monitoring of pressure distribution in orthopedic surgery patients as described in claim 7, characterized in that: It also includes a traction tension monitoring step: S6, in the context of orthopedic traction surgery, simultaneously collect traction force values, limb stretch displacement and local pressure data; S7, construct a ratio correlation model between traction tension and body position pressure, and automatically generate traction parameter correction schemes and limb placement angle adjustment schemes when the ratio is unbalanced.

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