Orthopedic patient skin traction care method and system

Abstract

The present application relates to the technical field of orthopedic nursing, and particularly relates to a skin traction nursing method and system for orthopedic patients. The method comprises the steps of setting an initial traction force, checking the skin state at regular time intervals and adjusting the traction parameters, and further comprises the steps of collecting circadian rhythm signals, inflammation marker levels and traction material creep data, constructing a traction efficacy evaluation model based on the circadian rhythm signals, inflammation marker levels and traction material creep data, and dynamically adjusting the traction cycle, the traction force and the relaxation timing based on the traction efficacy evaluation model. The traction cycle is adjusted to match the circadian physiological rhythm fluctuations of the patient, the traction force is adjusted in combination with the changes in the inflammation marker levels, and the relaxation timing is selected in reference to the traction material creep data. The present application provides a traction adjustment scheme that can adapt to the circadian physiological rhythm changes of the human body.

Description

Technical Field

[0001] This invention relates to the field of orthopedic nursing technology, and in particular to a method and system for skin traction nursing for orthopedic patients. Background Technology

[0002] In orthopedic clinics, skin traction is a common method for fracture reduction and fixation. Traditional nursing care for this procedure has long relied on fixed traction parameters, including constant traction force and uniform traction cycle. The core limitation of this approach is its failure to consider the significant impact of the body's diurnal physiological rhythms on traction tolerance. During the diurnal cycle, the body's tolerance threshold to traction force fluctuates regularly due to the regulated secretion rhythms of hormones such as melatonin and cortisol: Hormone levels change at night, leading to increased pain sensitivity; at this time, fixed traction force can easily cause significant discomfort in patients, even leading to treatment interruption due to severe pain. Conversely, during the day, tissue tolerance is relatively high, and fixed traction force may not reach effective strength, thus delaying fracture reduction. This mismatch between fixed parameters and dynamic physiological state not only reduces patient compliance but can also affect the final therapeutic effect due to excessive or insufficient traction, becoming a key bottleneck restricting the improvement of skin traction nursing care.

[0003] Based on the above problems, there is an urgent need for a traction regulation scheme that can adapt to the changes in the human body's diurnal physiological rhythm. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose a method for skin traction care for orthopedic patients, the method comprising:

[0005] Collect patient diurnal rhythm signals, inflammatory marker levels, and creep data of traction materials;

[0006] A traction effectiveness evaluation model was constructed based on the diurnal rhythm signals, inflammatory marker levels, and traction material creep data.

[0007] Based on the aforementioned traction effectiveness evaluation model, the traction cycle, traction force magnitude, and release timing are dynamically adjusted.

[0008] The traction cycle adjustment matches the patient's diurnal physiological rhythm fluctuations, the traction force adjustment is combined with changes in inflammatory marker levels, and the relaxation timing is selected with reference to the creep data of the traction material.

[0009] The construction of the traction effectiveness evaluation model includes calculating the effectiveness loss coefficient, and the formula for calculating the effectiveness loss coefficient is as follows:

[0010] ;

[0011] in, The efficiency loss coefficient, For material creep influencing factors, To accumulate traction time, The creep half-life of the traction material. Inflammation sensitivity coefficient, This represents the real-time C-reactive protein concentration. The circadian rhythm cycle is used; if the efficiency loss coefficient value is greater than the threshold, it indicates that the actual traction efficiency deviates from the theoretical value, and compensation is made by adjusting the parameters.

[0012] The method further includes:

[0013] The steps include setting the initial traction force, periodically checking the skin condition, and adjusting the traction parameters.

[0014] Preferably, the acquisition of the patient's circadian rhythm signal includes: acquiring the patient's heart rate variability data through a contact photoplethysmography (PPG) sensor; performing spectral analysis on the heart rate variability data to extract low-frequency and high-frequency components, wherein the low-frequency components are located in the 0.04-0.15 Hz frequency band and the high-frequency components are located in the 0.15-0.4 Hz frequency band; and calculating the ratio of the low-frequency components to the high-frequency components as a circadian rhythm characteristic parameter, wherein the circadian rhythm characteristic parameter characterizes the patient's melatonin and cortisol secretion rhythm.

[0015] Further preferably, the collection of inflammatory marker levels includes: using a wearable electrochemical sensor to monitor the concentration of C-reactive protein in the patient's tissue fluid in real time. The working electrode of the wearable electrochemical sensor is a carbon paste electrode modified with gold nanoparticles. During the detection process, a working voltage of 0.2-0.6V is applied, and the oxidation peak current value is recorded by differential pulse voltammetry. The real-time C-reactive protein concentration is calculated based on the calibration curve of the oxidation peak current value and the C-reactive protein concentration.

[0016] Further preferably, the acquisition of creep data of traction material includes: embedding a fiber Bragg grating sensor inside the traction belt, wherein the center wavelength of the fiber Bragg grating sensor is 1550nm, and the wavelength offset is acquired in real time by a fiber Bragg grating demodulator; the real-time elastic modulus attenuation rate of the traction belt is calculated according to a preset relationship model between the wavelength offset and the elastic modulus of the traction belt; the elastic modulus attenuation rate quantifies the degree of creep of the traction material.

[0017] A further preferred embodiment of dynamically adjusting the relaxation timing includes determining an adaptive relaxation trigger condition, wherein the formula for calculating the adaptive relaxation trigger condition is:

[0018]

[0019] In the formula To relax the trigger signal, For real-time NRS pain scores, Basic pain score, For pain rating standard deviation, Pain trigger threshold, This represents an increase in edema deformation. The standard deviation of edema deformation. The threshold for triggering edema. The efficiency loss coefficient, This is the threshold for triggering effectiveness loss.

[0020] More preferably, dynamically adjusting the traction force includes calculating the target traction force at different times, wherein the formula for calculating the target traction force is: ;

[0021] In the formula Let be the target traction force at time t. For nominal traction, For rhythm modulation depth, For individual phase delay, It is a circadian rhythm cycle. This represents the inflammation damping coefficient. This represents the real-time C-reactive protein concentration.

[0022] A skin traction nursing system for orthopedic patients, applied to a skin traction nursing method for orthopedic patients as described in any of the above, includes a traction execution device, a sensing module, and a control module. The traction execution device is used to apply traction force, the sensing module is used to collect patient physiological and environmental data, and the control module is used to process the data and control the traction execution device. The sensing module includes a contact photoplethysmography pulse wave sensor, a wearable electrochemical sensor, and a fiber optic grating sensor.

[0023] The contact-type photoplethysmography (PPG) sensor is electrically connected to the control module to transmit heart rate variability data. The wearable electrochemical sensor is connected to the control module via a wireless communication module to send C-reactive protein concentration data. The fiber optic grating sensor is connected to the control module via an optical fiber to transmit wavelength offset data. The control module has a built-in traction efficiency evaluation model algorithm and an adaptive adjustment algorithm.

[0024] More preferably, the traction actuator includes a servo motor, a force sensor, and a flexible traction belt. The servo motor is electrically connected to the control module to receive traction force adjustment signals. The force sensor is installed between the output end of the servo motor and the flexible traction belt. The force sensor is electrically connected to the control module to provide feedback on the real-time traction force value. A fiber optic grating sensor is embedded inside the flexible traction belt. The flexible traction belt is made of a composite material of polyurethane elastomer and carbon fiber.

[0025] More preferably, the control module includes a microprocessor, a memory, and a communication interface. The microprocessor uses an ARM Cortex-M4 core. The memory stores initial traction parameters, patient baseline data, and algorithm programs. The communication interface includes an SPI interface, a UART interface, and a Bluetooth module. The SPI interface is connected to a force sensor, the UART interface is connected to a servo motor driver, and the Bluetooth module communicates with a wearable electrochemical sensor. When the microprocessor executes the algorithm program, it implements the steps of any of the methods described above.

[0026] Technical effects:

[0027] The innovation of this invention lies in its ability to collect circadian rhythm signals and dynamically adjust the traction cycle accordingly, breaking through the traditional fixed-cycle model. Its core technology synchronizes the traction cycle with the patient's melatonin and cortisol secretion rhythms, automatically adjusting the traction force at night to match changes in pain threshold, and ensuring effective traction intensity during the day. This specifically addresses the tolerance contradiction caused by fixed parameters failing to adapt to circadian physiological rhythms in previous technologies, achieving precise synergy between the traction plan and human physiological laws. Attached Figure Description

[0028] Figure 1 This is a flowchart of a skin traction nursing method for orthopedic patients according to this application;

[0029] Figure 2 This is a block diagram showing the connection of the orthopedic patient skin traction nursing system module in this application;

[0030] Figure 3 This is a flowchart illustrating the data processing and execution connection for skin traction nursing care in orthopedic patients in this application. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0032] In traditional orthopedic skin traction care, the fixed traction cycle and force settings do not take into account the combined effects of individual patient physiological rhythm fluctuations, changes in inflammatory response, and creep of traction materials. This leads to a mismatch between traction effectiveness and the patient's real-time condition, easily resulting in problems such as increased pain, tissue damage, or insufficient traction effect. Fixed parameters are difficult to adapt to the influence of diurnal physiological rhythms on pain perception and tissue tolerance, and cannot dynamically respond to changes in the degree of inflammation and the degradation of material performance, affecting the safety and effectiveness of nursing care.

[0033] Based on this, please refer to Figure 1This embodiment provides a skin traction care method for orthopedic patients, including the steps of setting an initial traction force, periodically checking the skin condition, and adjusting traction parameters; it also includes collecting the patient's diurnal rhythm signals, inflammatory marker levels, and traction material creep data, constructing a traction efficacy evaluation model based on the diurnal rhythm signals, inflammatory marker levels, and traction material creep data, and dynamically adjusting the traction cycle, traction force magnitude, and relaxation timing based on the traction efficacy evaluation model. The traction cycle adjustment matches the patient's diurnal physiological rhythm fluctuations, the traction force magnitude adjustment combines changes in inflammatory marker levels, and the relaxation timing is selected with reference to the traction material creep data.

[0034] This approach breaks through the traditional fixed-parameter model by acquiring and integrating multi-dimensional data, enabling personalized and dynamic adjustment of the traction program. By capturing circadian rhythm signals to match physiological fluctuations, adjusting traction force based on inflammatory marker levels to reduce the risk of tissue damage, and selecting relaxation timing based on material creep data to maintain traction accuracy, the synergistic effect of these three factors makes the traction program more closely aligned with the patient's real-time physiological state and material properties, improving the adaptability and safety of nursing care, and providing a more precise and dynamic basis for the control of orthopedic skin traction.

[0035] Traditional technologies rely heavily on subjective observation or single physiological indicators when acquiring patients' circadian rhythm information. This fails to accurately reflect the secretion rhythms of hormones such as melatonin and cortisol, resulting in a lack of reliable data support for rhythm-based traction regulation and difficulty in matching patients' actual physiological fluctuations. Existing monitoring methods are either too simplistic or rely on limited signal analysis, failing to effectively extract characteristic parameters related to circadian rhythms and affecting the accuracy of subsequent traction cycle regulation.

[0036] Based on this, the acquisition of the patient's diurnal rhythm signal includes obtaining the patient's heart rate variability data through a contact photoplethysmography (PPG) sensor, performing spectral analysis on the heart rate variability data to extract low-frequency and high-frequency components. The low-frequency components are located in the 0.04-0.15 Hz frequency band, and the high-frequency components are located in the 0.15-0.4 Hz frequency band. The ratio of the low-frequency components to the high-frequency components is calculated as a diurnal rhythm characteristic parameter, which characterizes the patient's melatonin and cortisol secretion rhythm.

[0037] This approach utilizes a contact-type photoplethysmography (PPG) sensor to acquire heart rate variability data. Through spectral analysis, it precisely extracts low-frequency and high-frequency components within specific frequency bands. The ratio of these components effectively reflects the secretion rhythms of melatonin and cortisol, providing objective and quantitative physiological evidence for traction cycle regulation. Compared to traditional subjective assessments or single-indicator monitoring, this method, through standardized signal acquisition and analysis procedures, improves the accuracy and reliability of circadian rhythm information. This allows traction cycle regulation to better align with the patient's actual physiological state, helping to reduce the risk of pain or tissue damage caused by rhythm mismatch.

[0038] In traditional orthopedic traction care, monitoring of the patient's inflammatory status relies heavily on periodic blood tests, which are lagging and cannot reflect changes in local tissue inflammation in real time. This results in traction force adjustments failing to respond promptly to the degree of inflammation, potentially exacerbating tissue damage or affecting the traction effect. Existing detection methods struggle to obtain inflammatory marker levels in real time during care, failing to provide continuous and accurate data support for dynamic traction force adjustment, thus impacting the timeliness and targeted nature of care.

[0039] Based on this, the acquisition of inflammatory marker levels includes real-time monitoring of C-reactive protein (CRP) concentration in the patient's tissue fluid using a wearable electrochemical sensor. The working electrode of the wearable electrochemical sensor is a carbon paste electrode modified with gold nanoparticles. During detection, a working voltage of 0.2-0.6V is applied, and the oxidation peak current value is recorded using differential pulse voltammetry. The real-time CRP concentration is calculated based on a calibration curve comparing the oxidation peak current value with the CRP concentration. This approach achieves real-time monitoring of CRP concentration through a wearable electrochemical sensor. The optimized design of the working electrode and the detection method improve the sensitivity and specificity of the detection, enabling timely capture of changes in inflammatory markers in the tissue fluid.

[0040] Compared to the lag in traditional blood tests, this method can continuously acquire inflammatory status data, enabling traction force adjustment to respond quickly to changes in the degree of inflammation. When inflammation intensifies, the traction force can be adjusted appropriately to reduce tissue damage, and when inflammation subsides, effective traction can be maintained. This improves the dynamic adaptability and safety of nursing care and provides key inflammatory status data for personalized traction plans.

[0041] In traditional traction care, there is a lack of effective monitoring methods for the decrease in elastic modulus caused by creep of traction materials. This makes it impossible to accurately determine the impact of changes in material properties on the transmission of traction force, which may lead to deviations between the actual traction force and the preset value, affecting the traction effect or increasing the risk of tissue damage. Existing methods mostly rely on experience or periodic material replacement, making it difficult to quantify the degree of material creep in real time. This fails to provide accurate data support for traction force compensation and adjustment, resulting in insufficient traction precision.

[0042] Based on this, the acquisition of creep data for the traction material includes embedding a fiber Bragg grating sensor inside the traction belt. The center wavelength of the fiber Bragg grating sensor is 1550 nm. The wavelength offset is acquired in real time using a fiber Bragg grating demodulator. The real-time elastic modulus attenuation rate of the traction belt is calculated based on a preset relationship model between the wavelength offset and the elastic modulus of the traction belt. This elastic modulus attenuation rate quantifies the degree of creep in the traction material. This scheme achieves real-time quantitative monitoring of the degree of material creep by embedding a fiber Bragg grating sensor inside the traction belt and utilizing a correlation model between wavelength offset and elastic modulus.

[0043] The high sensitivity and stability of fiber Bragg grating sensors ensure data accuracy, precisely capturing changes in the elastic modulus attenuation rate and providing a basis for judging the actual traction force transmission effect. Compared with traditional experience-based judgment, this method can monitor material property changes in real time, facilitating timely compensation and adjustment of traction force, maintaining traction accuracy, and avoiding insufficient or excessive traction due to material creep, thus improving the reliability and effectiveness of traction care.

[0044] Traditional traction effectiveness assessments often consider only the magnitude of traction force or traction time, failing to integrate the combined effects of multiple factors such as material creep, inflammatory status, and circadian rhythms. This leads to discrepancies between assessment results and actual traction effects, and fails to provide a comprehensive and accurate basis for adjusting traction protocols. Existing assessment models, due to their incomplete consideration of factors, struggle to reflect the interactions between them, potentially resulting in inappropriate traction force adjustments, affecting nursing outcomes, or increasing the risk of complications.

[0045] Based on this, the traction effectiveness evaluation model is constructed by calculating the effectiveness loss coefficient, and the formula for calculating the effectiveness loss coefficient is as follows:

[0046] ;

[0047] In the formula The efficiency loss coefficient, For material creep influencing factors, To accumulate traction time, The creep half-life of the traction material. Inflammation sensitivity coefficient, This represents the real-time C-reactive protein concentration. The circadian rhythm cycle is used as the reference. This scheme calculates the efficiency loss coefficient by integrating a multi-factor formula that considers material creep, inflammation status, and circadian rhythm, comprehensively reflecting the combined impact of various factors on traction efficiency.

[0048] This formula quantifies the effectiveness decay caused by various factors during traction, and is a core indicator for evaluating the actual performance of a traction system. The left side of the formula... This is the efficiency loss coefficient. The larger the value, the more significant the deviation of the actual traction efficiency from the theoretical value, and compensation needs to be made by adjusting the parameters.

[0049] In this embodiment, the theoretical value refers to the ideal efficacy benchmark of the traction system under ideal conditions of no material creep, no inflammatory response, and matching the human body's diurnal rhythm, in order to achieve treatment goals such as fracture reduction and tissue stretching. It is obtained by first determining the basic efficacy threshold according to the type of disease based on medical guidelines, and then personalizing it by substituting baseline data such as the patient's initial pain score, edema deformation, and elastic modulus of the traction material. Finally, the nominal traction force in the instruction manual is used as the initial quantitative representation, and it can also be dynamically adjusted with the treatment stage.

[0050] The first part on the right side of the formula represents the effect of material creep on effectiveness, where... The creep influencing factor is determined by the material properties of the traction belt. It is obtained by experimentally measuring the deformation curves of different composite materials, such as the ratio of polyurethane elastomer to carbon fiber, under continuous stress. The total traction time is obtained directly through the timing unit of the control module; The creep half-life of the material, which is the time required for the material's elastic modulus to decay to 50% of its initial value, is predetermined through accelerated aging experiments and stored in memory.

[0051] Using the natural logarithm function The cumulative effect of material creep is described by logarithmic form, which accurately reflects this nonlinear characteristic because the creep process decays rapidly in the early stages and then slows down in the later stages.

[0052] The second part on the right side of the formula reflects the synergistic effect between inflammatory states and circadian rhythms. This is the inflammation sensitivity coefficient, calibrated based on clinical data. The higher the degree of influence of the inflammatory marker on the traction efficacy, the larger the value of this coefficient. The real-time C-reactive protein concentration was obtained by converting the oxidation peak current value from a wearable electrochemical sensor; trigonometric function. Used to simulate the periodic changes in circadian rhythms, among which The circadian rhythm cycle is set to 24 hours by default. This represents the phase angle corresponding to time, resulting in a larger value at night and a smaller value during the day, consistent with the secretion pattern of melatonin in the human body. This formula, by integrating material physical properties, physiological and biochemical indicators, and circadian rhythms, breaks through the traditional assessment model that only considers the magnitude of traction force. It makes the quantification of efficacy loss more closely aligned with actual clinical scenarios, providing a comprehensive basis for subsequent traction parameter adjustments and ensuring that the traction force is always maintained within an effective and safe range.

[0053] Material creep is reflected by a logarithmic function to show its decay trend over time, while the interaction between inflammatory state and circadian rhythm is reflected by a coupling of trigonometric and exponential functions, making the assessment results more consistent with the actual traction process. Compared with traditional single-factor assessment, this model can accurately quantify the degree of efficacy loss, providing a scientific basis for traction force adjustment and relaxation timing selection, helping to maintain optimal traction effect, reduce nursing risks caused by inaccurate efficacy assessment, and improve the dynamic adaptability and accuracy of traction programs.

[0054] Traditional techniques often rely on a single indicator, such as pain score or fixed time intervals, to determine the timing of traction release, failing to comprehensively consider the synergistic effects of changes in pain, degree of edema, and loss of traction effectiveness. This leads to inaccurate judgments regarding the timing of release. Relying solely on pain scores may result in misjudgments due to individual differences in pain tolerance; and releasing at fixed times may be interrupted while traction is still effective or delayed when tissue damage risks have already occurred, affecting both the effectiveness of traction and patient safety.

[0055] Based on this, dynamically adjusting the relaxation timing includes determining the adaptive relaxation trigger condition, and the calculation formula for the adaptive relaxation trigger condition is as follows:

[0056]

[0057] In the formula To relax the trigger signal, For real-time NRS pain scores, Basic pain score, For pain rating standard deviation, Pain trigger threshold, This represents an increase in edema deformation. The standard deviation of edema deformation. The threshold for triggering edema. The efficiency loss coefficient, The threshold for triggering efficacy loss is defined. This scheme constructs a relaxation triggering mechanism through multi-condition logical AND operation, incorporating changes in pain, edema increment, and efficacy loss coefficient into a unified judgment framework. All parameters are standardized to eliminate individual baseline differences.

[0058] This formula is used to determine whether a traction release operation needs to be triggered, through a logical AND operation of multiple conditions ( Ensure the accuracy of the timing of relaxation and avoid misjudgment based on a single indicator.

[0059] The left side of the formula To release the trigger signal, when the result is true, the control module sends a release command to the servo motor.

[0060] First condition It reflects the degree of deviation from the pain state. The real-time NRS pain score is input by the patient through an interactive unit; The basic pain score is the initial score at the start of traction. The standard deviation of the patient's historical pain scores is calculated from past data stored in the memory and is used to standardize the amplitude of pain variation and eliminate the influence of individual differences in pain tolerance. The pain trigger threshold is set based on clinical research to ensure a timely response when pain intensifies significantly.

[0061] The second condition Assess the risk of tissue edema. The incremental edema deformation is calculated by the difference between the skin deformation data collected by a flexible pressure sensor array and the initial value. The standard deviation of edema deformation is determined in advance based on the patient's basic tissue characteristics, such as skin elasticity and subcutaneous fat thickness. Different values ​​are set for different areas, such as the heel and the anterior tibial crest, to determine the edema trigger threshold, because the skin tolerance in these areas is different.

[0062] The third condition Loss of associated traction effect This is the efficiency loss coefficient calculated above; The threshold for triggering efficacy loss is set according to the goals of traction therapy, such as the required accuracy of fracture reduction. If the efficacy loss exceeds this value, continued traction may not achieve the expected results and needs to be paused and adjusted.

[0063] This formula eliminates individual baseline differences by standardizing each parameter and avoids false triggers caused by fluctuations in a single indicator by using multi-condition collaborative judgment. It ensures that relaxation operations are only performed when pain, edema, and loss of efficacy all reach the level requiring intervention, thus ensuring tissue safety, reducing unnecessary traction interruptions, and balancing treatment effectiveness with patient comfort.

[0064] Compared to traditional single-indicator judgments, this method achieves a synergistic assessment of multi-dimensional risks, ensuring that the timing of relaxation is neither mistakenly triggered by fluctuations in a single indicator nor overlooked by complex risk factors. This makes the relaxation procedure more closely aligned with the actual tolerance state of the tissue, maintaining the traction effect while reducing the risk of tissue damage, and providing a scientific basis for dynamic adjustment of the traction rhythm. It is worth noting that the thresholds in the formula can be dynamically calibrated according to individual patient conditions such as age and underlying diseases, enhancing the personalized adaptability of the plan and avoiding the problem of insufficient applicability of fixed thresholds to different patients.

[0065] Traditional traction force adjustment schemes often use fixed values ​​or are set solely based on body weight, failing to consider the impact of the patient's diurnal physiological rhythms on tissue tolerance and the dynamic changes in traction tolerance caused by inflammation. This leads to a mismatch between traction force and the patient's real-time condition. During the diurnal rhythm, tissue repair capacity and pain sensitivity fluctuate. Fixed traction force may cause discomfort during periods of low tolerance or exacerbate tissue damage when inflammation intensifies. Conversely, methods that ignore the adjustment of inflammation may maintain high traction force even during peak inflammation, increasing the risk of complications.

[0066] Based on this, dynamically adjusting the traction force includes calculating the target traction force at different times, and the formula for calculating the target traction force is as follows:

[0067] ;

[0068] In the formula Let be the target traction force at time t. For nominal traction, For rhythm modulation depth, For individual phase delay, It is a circadian rhythm cycle. This represents the inflammation damping coefficient. The measured C-reactive protein (CRP) concentration is real-time. This method introduces a circadian rhythm modulator through a trigonometric function, causing the traction force to dynamically adjust periodically in accordance with the secretion rhythms of melatonin and cortisol. Simultaneously, an exponential function is used to incorporate the CRP concentration during inflammation as a damping term, reducing the amplitude of traction force adjustment when inflammation intensifies. Compared to traditional fixed traction methods, this approach achieves dual dynamic regulation of physiological rhythm adaptation and inflammatory response, enabling the traction force to conform to the body's diurnal tolerance patterns while actively reducing the load and minimizing unnecessary tissue stimulation during periods of tissue inflammation.

[0069] This formula is used to dynamically calculate the target traction force at different times, achieving adaptation of the traction force to the patient's real-time condition. The left side of the formula... The target traction force at time t is directly used as the control parameter of the servo motor. (The right side of the formula...) The nominal traction force is calculated based on basic parameters such as the patient's weight and fracture type, and serves as the baseline value for the traction plan.

[0070] Correction items Used to dynamically adjust the nominal traction force, of which The rhythm modulation depth, with a value ranging from 0 to 1, reflects the magnitude of the influence of diurnal rhythm on traction force, and is determined through patient tolerance experiments. For individual phase delay, settings are made according to the patient's sleep habits, such as late sleepers or early sleepers, so that the rhythm regulation is synchronized with the actual biological clock; This represents the circadian rhythm cycle, with a 24-hour value to ensure that the regulatory cycle is consistent with the human body's physiological rhythm; trigonometric functions The correction term is set to a larger value at night corresponding to the 180° phase angle, resulting in a corresponding decrease in traction force, and a smaller value during the day, resulting in a rebound in traction force, which aligns with the characteristic that the human body's pain threshold decreases at night.

[0071] Exponential function For inflammation damping, This is the inflammation damping coefficient, reflecting the degree to which the inflammatory state inhibits rhythm regulation; the more severe the inflammation, the lower the resistance. The higher the value, the smaller the function value, and the weaker the actual effect of rhythm modulation, thus avoiding excessive fluctuations in traction force under high inflammation conditions that could cause additional stimulation to the tissue. This represents the real-time C-reactive protein concentration, consistent with the source of the parameters in the previous formula.

[0072] This formula integrates rhythmic cycles, individual differences, and inflammatory states to transform the traction force from a fixed value into a dynamic curve. It not only conforms to changes in the body's physiological rhythms but also actively reduces the adjustment amplitude during periods of inflammation sensitivity, ensuring that the traction force is always within an effective and safe range, thus enhancing the personalization and adaptability of the traction program.

[0073] Rhythm modulation depth With inflammation damping coefficient It can be calibrated based on individual patient data to ensure that the adjustment range meets the individual's tolerance threshold, thereby improving patient comfort and safety while maintaining effective traction.

[0074] Traditional orthopedic skin traction systems typically consist of a single-function traction device and independent monitoring equipment. The lack of data interaction and coordinated control between these components means that monitoring data cannot be directly used for traction parameter adjustment, requiring manual processing and introducing response delays and error risks. Existing systems' sensing modules are mostly limited to monitoring single physical quantities, such as traction force, and cannot acquire data on the patient's physiological state, such as circadian rhythms, inflammation, and material properties, making it difficult to support comprehensive traction protocol optimization. Furthermore, the connection between the control module and the actuator is simple, lacking standardized data transmission interfaces, which affects the accurate execution of adjustment commands.

[0075] Based on this, please refer to Figure 2 This embodiment provides a skin traction care system for orthopedic patients, including a traction execution device, a sensing module, and a control module. The traction execution device is used to apply traction force, the sensing module is used to collect patient physiological and environmental data, and the control module is used to process the data and control the traction execution device. The sensing module includes a contact photoplethysmography (PPG) sensor, a wearable electrochemical sensor, and a fiber Bragg grating sensor. The PPG sensor is electrically connected to the control module to transmit heart rate variability data. The wearable electrochemical sensor is connected to the control module via a wireless communication module to transmit C-reactive protein concentration data. The fiber Bragg grating sensor is connected to the control module via an optical fiber to transmit wavelength offset data. The control module has a built-in traction efficacy evaluation model algorithm and an adaptive adjustment algorithm.

[0076] This solution constructs a closed-loop system architecture integrating multi-source sensing, intelligent processing, and execution feedback. The sensing module covers multi-dimensional data such as physiological rhythms, inflammatory markers, and material creep, ensuring efficient transmission of different data types through diverse connection methods including electrical connections, wireless communication, and fiber optic transmission. The control module integrates algorithm models, directly converting sensor data into adjustment commands, eliminating the need for manual intervention. Compared to traditional discrete systems, this solution achieves real-time fusion and automatic processing of cross-dimensional data, making traction adjustment more timely and precise. Furthermore, the standardized module interface design enhances system compatibility and scalability, allowing for the addition of more sensor types according to patient needs, providing hardware support for personalized traction care.

[0077] Traditional traction actuators have a relatively simple structural design, often using a fixed traction belt and mechanical knobs to adjust the traction force. They lack precise force feedback mechanisms and flexible structures adapted to the human body, resulting in uneven application of traction force and an inability to compensate for force deviations caused by material creep or body movement in real time. Existing devices often have force sensors installed at non-critical stress points, leading to discrepancies between the feedback data and the actual force applied to the skin. Furthermore, the traction belts are mostly made of ordinary fabric, lacking a balance between elasticity and strength, making them prone to creep-induced attenuation of effective traction force and potentially causing pressure damage to the local skin.

[0078] Based on this, the traction actuator includes a servo motor, a force sensor, and a flexible traction belt. The servo motor is electrically connected to the control module to receive traction force adjustment signals. The force sensor is installed between the output end of the servo motor and the flexible traction belt. The force sensor is electrically connected to the control module to provide feedback on the real-time traction force value. A fiber optic grating sensor is embedded inside the flexible traction belt. The flexible traction belt is made of a composite material of polyurethane elastomer and carbon fiber.

[0079] This solution improves the accuracy and comfort of traction control by optimizing the hardware configuration and connection structure of the actuator. The servo motor, as the power source, can respond to the pulse signals of the control module to achieve stepless speed regulation, ensuring smooth traction adjustment. Force sensors are installed at key nodes in the force transmission path, such as between the motor and the traction belt, directly collecting the force values ​​acting on the traction belt, providing more accurate feedback data. The flexible traction belt adopts a composite material design, balancing elasticity (polyurethane) and creep resistance (carbon fiber), reducing the impact of material deformation on traction. Simultaneously, the embedded fiber optic grating sensor can monitor belt deformation in real time, providing data for material creep compensation.

[0080] Compared to traditional actuators, this solution integrates precise application of traction force, real-time feedback, and material performance monitoring, reducing force deviation and the risk of skin pressure, and improving the stability and safety of traction execution.

[0081] Traditional traction systems often employ simple microcontrollers in their control modules, which have limited processing capabilities and can only perform basic logical judgments. They are unable to run complex multi-parameter evaluation models and adaptive algorithms, resulting in low data processing accuracy and delayed generation of adjustment commands. Existing modules have small memory capacities, making it difficult to store long-term physiological baseline data and algorithm programs for patients; their communication interfaces are limited to a few sensors, restricting the acquisition and integration of multi-source data; and the connection between the microprocessor and the actuator lacks standardized design, potentially leading to unstable signal transmission that affects the execution of adjustment commands.

[0082] Based on this, please refer to Figure 3 The control module includes a microprocessor, a memory, and a communication interface. The microprocessor uses an ARM Cortex-M4 core. The memory stores initial traction parameters, patient baseline data, and algorithm programs. The communication interface includes an SPI interface, a UART interface, and a Bluetooth module. The SPI interface is connected to a force sensor, the UART interface is connected to a servo motor driver, and the Bluetooth module communicates with a wearable electrochemical sensor. When the microprocessor executes the algorithm program, it implements the steps of the method according to any one of claims 1 to 7.

[0083] This solution enhances the core processing and data interaction capabilities of the control module through high-performance hardware configuration and diverse interface design. The ARM Cortex-M4 core, equipped with a floating-point unit and DSP instruction set, can efficiently run complex algorithms such as traction efficacy evaluation models, ensuring both speed and accuracy in data processing. Memory capacity is adapted to long-term data storage needs, supporting the construction of personalized baseline databases. Multiple communication interfaces (SPI, UART, Bluetooth) are matched to the signal characteristics of different sensors and actuators, ensuring the stability and real-time performance of data transmission. Compared to traditional control modules, this solution achieves efficient coordination of data storage, algorithm execution, and instruction output, providing powerful computational support for closed-loop traction regulation. Furthermore, the standardized interface design facilitates system upgrades and functional expansion, adapting to changing needs in different clinical scenarios.

[0084] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A skin traction nursing system for orthopedic patients, comprising a traction actuator, a sensing module, and a control module, wherein the traction actuator is used to apply traction force, the sensing module is used to collect patient physiological data, and the control module is used to process the data and control the traction actuator, characterized in that, The sensing module includes: A contact-type photoplethysmography (PPG) sensor is used to acquire patient heart rate variability data. Wearable electrochemical sensors for real-time monitoring of C-reactive protein concentration in patient tissue fluid; A fiber Bragg grating sensor, embedded within the flexible traction belt of the traction actuator, is used to acquire wavelength offset data characterizing the creep degree of the traction belt in real time. The control module includes a built-in memory, a microprocessor, and an algorithm program stored in the memory and executable on the microprocessor. When the microprocessor executes the algorithm program, it is configured to perform the following operations: Based on the heart rate variability data, C-reactive protein concentration data, and wavelength offset data, a traction effectiveness assessment model was constructed and run to calculate the effectiveness loss coefficient. The formula for calculating the effectiveness loss coefficient is as follows: in, The efficiency loss coefficient, For material creep influencing factors, To accumulate traction time, The creep half-life of the traction material. Inflammation sensitivity coefficient, This represents the real-time C-reactive protein concentration. It is a diurnal rhythm cycle; Based on the efficiency loss coefficient and the preset adaptive relaxation triggering condition, a relaxation triggering signal is generated and output to control the traction actuator to perform a relaxation action; Based on the circadian rhythm cycle and the real-time C-reactive protein concentration, the target traction force at different times was calculated. And according to the target traction force A traction force adjustment signal is generated to control the traction actuator to dynamically adjust the applied traction force.

2. The system according to claim 1, characterized in that, The microprocessor is configured to extract circadian rhythm signals from heart rate variability data by performing spectral analysis on the heart rate variability data to extract low-frequency and high-frequency components, wherein the low-frequency components are located in the 0.04-0.15 Hz frequency band and the high-frequency components are located in the 0.15-0.4 Hz frequency band, and calculating the ratio of the low-frequency components to the high-frequency components as a circadian rhythm characteristic parameter, which is used to characterize the secretion rhythm of melatonin and cortisol in patients.

3. The system according to claim 1, characterized in that, The formula for calculating the adaptive relaxation trigger condition is configured as follows: in, To relax the trigger signal, For real-time NRS pain scores, Basic pain score, For pain rating standard deviation, Pain trigger threshold, This represents an increase in edema deformation. The standard deviation of edema deformation. The threshold for triggering edema. The efficiency loss coefficient is... This is the threshold for triggering effectiveness loss.

4. The system according to claim 1, characterized in that, The target traction force The calculation formula is configured as follows: in, Let be the target traction force at time t. For nominal traction, For rhythm modulation depth, For individual phase delay, It is a diurnal rhythm cycle. This represents the inflammation damping coefficient. This represents the real-time C-reactive protein concentration.

5. The system according to claim 1, characterized in that, The traction actuator includes a servo motor, a force sensor, and the flexible traction belt. The servo motor is electrically connected to the control module to receive the traction force adjustment signal and the release trigger signal. The force sensor is installed between the output end of the servo motor and the flexible traction belt, and is electrically connected to the control module to provide feedback on the real-time traction force value.

6. The system according to claim 1, characterized in that, The working electrode of the wearable electrochemical sensor is a carbon paste electrode modified with gold nanoparticles, and is configured to apply a working voltage of 0.2-0.6V. The oxidation peak current value is recorded by differential pulse voltammetry, and the real-time C-reactive protein concentration is calculated based on the calibration curve of the oxidation peak current value and the C-reactive protein concentration.

7. The system according to claim 1, characterized in that, The flexible traction belt is made of a composite material of polyurethane elastomer and carbon fiber. The center wavelength of the fiber optic grating sensor is 1550nm. The microprocessor is configured to calculate the real-time elastic modulus attenuation rate of the traction belt based on a preset relationship model between the wavelength offset acquired in real time from the fiber optic grating demodulator and the elastic modulus of the traction belt, so as to quantify the creep degree of the traction material.

8. The system according to claim 1, characterized in that, The microprocessor of the control module adopts an ARM Cortex-M4 core. The control module also includes an SPI interface, a UART interface and a Bluetooth module. The SPI interface is connected to the force sensor, the UART interface is connected to the servo motor driver, and the Bluetooth module communicates with the wearable electrochemical sensor.

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