An Internet of Things-based intelligent nursing system for postoperative rehabilitation of the elderly

Abstract

This invention discloses an intelligent postoperative rehabilitation nursing system for the elderly based on the Internet of Things (IoT), belonging to the field of medical rehabilitation technology. It includes: a data acquisition module for real-time acquisition of physiological and motor data; a status assessment module for calculating and generating rehabilitation progress rate and physiological fatigue level; a coupling effect analysis module for generating a coupling tension index; and an intervention decision and dynamic adjustment module for generating a final intervention intensity command and sending the command to the rehabilitation equipment actuator. By introducing a coupling tension index, this invention accurately quantifies the conflict between rehabilitation needs and fatigue inhibition, achieving a dynamic balance between rehabilitation needs and physiological fatigue, improving rehabilitation safety and efficiency, while enhancing the system's personalization and adaptability, ensuring the feasibility of the solution.

Description

Technical Field

[0001] This invention relates to the field of medical rehabilitation technology, specifically to an intelligent nursing system for postoperative rehabilitation of the elderly based on the Internet of Things. Background Technology

[0002] Postoperative rehabilitation for elderly patients is a complex and dynamic process, with the core objective of restoring physiological function as quickly as possible while ensuring patient safety. Existing rehabilitation systems, especially IoT-based intelligent rehabilitation systems, can monitor patient status in real time and adaptively adjust the intensity of rehabilitation training through closed-loop control. They also monitor indicators such as the patient's physiological fatigue level, and will forcibly reduce or suspend intervention when the fatigue level exceeds a safe threshold.

[0003] Existing technologies face significant challenges in handling a specific and critical clinical scenario: when a patient's recovery stagnates or falls short of expectations, the system's decision-making logic tends to enhance intervention. However, if the patient is experiencing severe physiological fatigue due to high-intensity training or postoperative stress, the safety monitoring logic tends to suppress intervention. This contradictory instruction stems from the negative feedback inhibition effect in the model. Existing technologies typically employ simple prioritization strategies or linear weighting methods to address this, but this leads to a significant decrease in recovery efficiency and may even cause the system to fail in the contradictory state, failing to provide optimal intervention instructions. The essence of this contradictory state is a nonlinear coupling effect. How to quantify the tension of this coupling effect and modulate nonlinear, smooth decisions based on it is a pressing technical challenge in the field. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent nursing system for postoperative rehabilitation of the elderly based on the Internet of Things, which solves the problems existing in the background technology.

[0005] To address the aforementioned technical problems, this invention provides an intelligent nursing system for postoperative rehabilitation of the elderly based on the Internet of Things, comprising: a data acquisition module for collecting patients' physiological and movement data in real time through wearable sensors;

[0006] The status assessment module is used to receive the physiological data and the movement data, and process the data to calculate and generate the rehabilitation progress rate, which characterizes rehabilitation progress, and the physiological fatigue level, which characterizes physiological risk.

[0007] The coupling effect analysis module is used for:

[0008] Based on the stated rehabilitation progress rate and the preset target rehabilitation progress rate, a standardized rehabilitation demand signal is generated.

[0009] Based on the stated physiological fatigue level and a preset physiological fatigue safety threshold, a standardized fatigue inhibition signal is generated;

[0010] The rehabilitation demand signal and the fatigue inhibition signal are then combined to generate a coupling tension index that quantifies the degree of contradiction between the two.

[0011] The intervention decision-making and dynamic adjustment module is used for:

[0012] Based on the rehabilitation demand signal and the fatigue inhibition signal, the basic intervention adjustment amount is determined;

[0013] Based on the coupling tension index, a nonlinear modulation factor is generated to suppress the basic intervention regulation amount;

[0014] The basic intervention adjustment amount is calculated with the nonlinear modulation factor to generate the actual intervention adjustment amount;

[0015] The actual intervention adjustment amount is then combined with the current intervention intensity recorded by the system to generate a final intervention intensity command, which is then sent to the rehabilitation device actuator.

[0016] Preferably, when generating the rehabilitation demand signal, the coupling effect analysis module is specifically used for:

[0017] S11, Calculate the difference between the target recovery progress rate and the recovery progress rate to obtain the progress gap;

[0018] S12, divide the progress gap by a preset demand signal sensitivity adjustment factor to obtain a normalized input value;

[0019] S13, apply an S-shaped function operation to the normalized input value to generate the rehabilitation demand signal.

[0020] Preferably, the specific steps of the coupling effect analysis module in generating the fatigue suppression signal include:

[0021] S21, calculate the difference between the physiological fatigue level and the physiological fatigue safety threshold to obtain the fatigue exceedance value;

[0022] S22, Multiply the fatigue over-limit value by a preset suppression signal activation slope factor to obtain the activation input value;

[0023] S23, apply a logical stearic function operation to the activation input value to generate the fatigue suppression signal.

[0024] Preferably, when generating the coupling tension index, the coupling effect analysis module specifically generates the coupling tension index by multiplying the rehabilitation demand signal, the fatigue inhibition signal, and the preset coupling effect weighting coefficient.

[0025] Preferably, when determining the basic intervention adjustment amount, the intervention decision and dynamic adjustment module is specifically used for:

[0026] S31. Calculate the difference between the rehabilitation demand signal and the fatigue inhibition signal to obtain the basic adjustment driving signal;

[0027] S32. Multiply the basic adjustment drive signal by a preset proportional gain constant to generate the basic intervention adjustment amount.

[0028] Preferably, when generating the final intervention intensity instruction, the intervention decision-making and dynamic adjustment module is specifically used for:

[0029] S41. Perform hyperbolic tangent function calculation on the coupling tension index to obtain the modulation reference value;

[0030] S42. Subtract the modulation reference value from the numerical value 1 to generate the nonlinear modulation factor;

[0031] S43. Multiply the basic intervention adjustment amount by the nonlinear modulation factor to generate the actual intervention adjustment amount;

[0032] S44. Add the actual intervention adjustment amount to the current intervention intensity to generate the final intervention intensity instruction.

[0033] Preferably, the target recovery rate is pre-set based on individualized information such as the patient's age, type of surgery, and preoperative physical condition; the demand signal sensitivity adjustment factor is obtained through statistical learning or offline optimization using historical clinical data.

[0034] Preferably, the physiological fatigue safety threshold is based on statistical data from published clinical guidelines or related studies and is individually set according to the patient's cardiac history and age; the inhibitory signal activation slope factor is configured according to the patient's risk level.

[0035] Preferably, the proportional gain constant is set based on the system's target response speed and stability requirements, and in combination with the patient's tolerance.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] Firstly, by introducing a coupling tension index, this invention accurately quantifies the conflict between rehabilitation needs and fatigue inhibition, enabling the system to generate logically consistent intervention instructions under any state, thus resolving decision-making contradictions and achieving logical self-consistency in decision-making.

[0038] Secondly, this invention, through the intervention decision and dynamic adjustment module, utilizes a nonlinear modulation factor generated based on the coupling tension index to smoothly and continuously suppress the basic intervention adjustment amount, thereby achieving refined dynamic balance and improving the smoothness and continuity of the intervention.

[0039] Thirdly, when a high coupling tension index is detected, the present invention automatically enters a buffer state, which avoids the risk of secondary damage caused by rashly increasing the intensity of intervention, and avoids unnecessary interruption of rehabilitation, thereby improving the safety and efficiency of rehabilitation and achieving the optimization of efficiency under risk avoidance. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a logic block diagram of an intelligent postoperative rehabilitation nursing system for the elderly based on the Internet of Things, according to the present invention.

[0042] Figure 2 This is a diagram showing the specific steps of the coupling effect analysis module in Embodiment 2. Detailed Implementation

[0043] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0044] Example 1:

[0045] Please see Figure 1This invention provides an IoT-based intelligent nursing system for postoperative rehabilitation of the elderly, comprising: a data acquisition module for real-time acquisition of the patient's physiological and movement data via wearable sensors; a status assessment module for receiving the physiological and movement data and processing the data to calculate and generate a rehabilitation progress rate characterizing rehabilitation progress and a physiological fatigue level characterizing physiological risk; a coupling effect analysis module for generating a standardized rehabilitation demand signal based on the rehabilitation progress rate and a preset target rehabilitation progress rate; generating a standardized fatigue inhibition signal based on the physiological fatigue level and a preset physiological fatigue safety threshold; and combining the rehabilitation demand signal and the fatigue inhibition signal to generate a coupling tension index quantifying the degree of contradiction between the two; and an intervention decision and dynamic adjustment module for determining a basic intervention adjustment amount based on the rehabilitation demand signal and the fatigue inhibition signal; generating a nonlinear modulation factor for inhibiting the basic intervention adjustment amount based on the coupling tension index; performing calculations on the basic intervention adjustment amount and the nonlinear modulation factor to generate an actual intervention adjustment amount; and combining the actual intervention adjustment amount with the current intervention intensity recorded by the system to generate a final intervention intensity command, and sending the command to the rehabilitation equipment actuator.

[0046] This embodiment provides an IoT-based intelligent nursing system for postoperative rehabilitation of the elderly. Its design aims to precisely solve the technical problem of low decision-making efficiency or decision-making incompetence caused by the inability to effectively handle the conflicting instructions between rehabilitation needs and physiological fatigue in existing rehabilitation technologies. Through the close collaboration and logical progression of its internal functional modules, it achieves refined and adaptive dynamic adjustment of the intensity of rehabilitation intervention.

[0047] The system begins with the data acquisition module, which uses wearable sensors deployed on the patient's body, such as heart rate monitors, electromyography sensors, and inertial measurement units, to continuously and in real-time collect the patient's physiological and motion data. The status assessment module is responsible for receiving these raw data streams and processing them using built-in algorithms to calculate two core status parameters: the rate of recovery progress, which characterizes the progress of rehabilitation, and the degree of physiological fatigue, which characterizes the physiological risk.

[0048] As the core of the system's technological innovation, the coupling effect analysis module receives the rehabilitation progress rate and physiological fatigue level output by the state assessment module. Based on these two real-time parameters and the system's preset target rehabilitation progress rate and physiological fatigue safety threshold, the module generates standardized rehabilitation demand signals and fatigue inhibition signals, respectively. Crucially, the module further combines these two signals mathematically to generate a coupling tension index that can accurately quantify the degree of conflict between the two commands.

[0049] As the core of decision-making and execution, the intervention decision and dynamic adjustment module makes the final decision based on the signals generated by the aforementioned modules. It determines a basic intervention adjustment amount based on the difference between the rehabilitation demand signal and the fatigue inhibition signal. Crucially, it uses the coupling tension index to generate a nonlinear modulation factor, which can smoothly and effectively suppress the basic adjustment amount when the signal contradiction is sharp. By operating the basic intervention adjustment amount with the nonlinear modulation factor, the actual intervention adjustment amount is generated. This actual intervention adjustment amount is accumulated with the current intervention intensity recorded by the system to generate the final intervention intensity command. This command is sent to the rehabilitation equipment actuator, such as a continuous passive motion machine or a functional electrical stimulation device, thereby constructing a complete closed-loop adaptive control process. In this way, the system transforms fuzzy clinical decision contradictions into precise mathematical models, thereby achieving a dynamic and precise balance between ensuring patient safety and maximizing rehabilitation efficiency.

[0050] Example 2:

[0051] refer to Figure 2 As shown, when generating the rehabilitation demand signal, the coupling effect analysis module is specifically used for: S11, calculating the difference between the target rehabilitation progress rate and the rehabilitation progress rate to obtain the progress gap; S12, dividing the progress gap by a preset demand signal sensitivity adjustment factor to obtain a normalized input value; S13, applying an S-shaped function operation to the normalized input value to generate the rehabilitation demand signal.

[0052] The target recovery rate is pre-set based on individualized information such as the patient's age, type of surgery, and preoperative physical condition; the demand signal sensitivity adjustment factor is obtained through statistical learning or offline optimization using historical clinical data.

[0053] The coupling effect analysis module in this embodiment performs a series of precise calculation steps in generating the rehabilitation demand signal. Its core purpose is to transform the specific rehabilitation progress gap into a standardized signal representing the intensity of the "enhanced intervention" motivation through nonlinear mapping. By executing steps S11 to S13, this module precisely and nonlinearly quantifies the clinical concept of "rehabilitation stagnation," providing the first key input variable for subsequent coupling analysis. The generation of the rehabilitation demand signal follows the formula:

[0054]

[0055] This represents a rehabilitation demand signal, which is a dimensionless output value ranging from (0,1). The closer the value is to 1, the greater the gap between the current rehabilitation progress and the goal, and the stronger the need for enhanced intervention from the system.

[0056] This represents the current rate of rehabilitation progress and is the input to this formula. It is calculated by the status assessment module through analysis of kinematic sensor data, and its unit is (degrees / day) or a similar unit. In an application scenario such as knee post-operative rehabilitation, the rate of rehabilitation progress... It can be calculated through daily measurements of the patient's active joint range of motion; for example, it can be defined as the increment of the average daily maximum AROM over the past three days. , It is the maximum active range of motion of the joint measured on day N (i.e., the current day);

[0057] The target recovery rate is the input to this formula. It is a preset value set by medical experts based on the patient's individual information.

[0058] This represents the demand signal sensitivity adjustment factor, a preset adjustable parameter, whose unit is... and The units are the same, thus ensuring that the function input is a dimensionless value. This parameter is related to rehabilitation needs and controls the steepness of the sigmoid curve, which determines the sensitivity of the demand signal to the progress gap.

[0059] The sigmoid function is a standard logistic function, and its specific form is:

[0060]

[0061] This function can be used to smoothly map the gap in recovery progress to a standardized signal between 0 and 1;

[0062] The design of this formula originates from the "error-driven" concept in control theory (driving system adjustment by calculating the difference between the actual system output and the target output), and incorporates the S-shaped response curve commonly found in biological regulatory systems, performing nonlinear optimization on it; in rehabilitation applications, if linear error is used directly ( As a driving signal, when the error is small, the system may not respond adequately, while when the error is large, the system may respond too aggressively, which may lead to system oscillation or patient discomfort. Using the sigmoid function for normalization mapping can generate a mild driving signal when the difference between progress and target is small, and a strong driving signal when the difference is significant. This nonlinear processing method ensures a smooth transition of system response, while defining the signal value within the (0,1) interval, which facilitates subsequent standardized calculations. Its characteristics are more in line with the intrinsic laws of biological regulation.

[0063] In the coupling effect analysis module, this formula is used to calculate specific gaps in recovery progress, i.e. This is transformed into a standardized signal representing the strength of the motivation to enhance intervention. This move enables precise, non-linear quantification of the clinically ambiguous concept of "rehabilitation stagnation," providing the first key input variable for subsequent coupling tension analysis, allowing the system to make decisions based on a quantified demand intensity.

[0064] Target recovery rate The settings reflect individualized treatment plans, pre-set by rehabilitation physicians or physical therapists based on a comprehensive assessment of the patient's specific circumstances, such as age, surgical complexity, and preoperative functional status; demand signal sensitivity adjustment factor. The value of is determined through statistical learning by analyzing historical clinical datasets or by using offline optimization algorithms, aiming to match the average response characteristics of a specific patient group. In clinical practice, therapists can also manually adjust it according to the treatment strategy; for example, a smaller value can be set for young patients who expect rapid recovery. This makes the system more responsive; conversely, for elderly patients at higher risk, a larger setting can be used. This makes the system response smoother and more conservative, thus ensuring the safety and personalization of treatment; sigmoid function: refers to the standard logistic function, specifically in the form of... It can smoothly map the gap in recovery progress to a standardized signal between 0 and 1; the input x in the formula corresponds to the normalized input value, i.e. .

[0065] Example 3:

[0066] The specific steps in generating the fatigue suppression signal by the coupling effect analysis module include:

[0067] S21, calculate the difference between the physiological fatigue level and the physiological fatigue safety threshold to obtain the fatigue exceedance value;

[0068] S22, Multiply the fatigue over-limit value by a preset suppression signal activation slope factor to obtain the activation input value;

[0069] S23, apply a logistic function operation to the activation input value to generate the fatigue suppression signal;

[0070] The physiological fatigue safety threshold is based on statistical data from published clinical guidelines or related studies and is individually set according to the patient's cardiac history and age; the inhibitory signal activation slope factor is configured according to the patient's risk level.

[0071] In this embodiment, the coupling effect analysis module, when generating the fatigue inhibition signal, transforms the specific physiological fatigue value into a standardized signal representing the intensity of the "inhibition intervention" motivation through steps S21 to S23. Physiological fatigue has a significant threshold effect on rehabilitation; its inhibitory effect is negligible at low fatigue levels, but once a certain physiological critical point is exceeded, the risk increases sharply, and the inhibitory effect should rapidly intensify. The calculation method used in this embodiment can accurately characterize the nonlinear characteristics of this "safety threshold" activation, and its performance is far superior to linear or step threshold judgment methods. The calculation formula is as follows:

[0072]

[0073] This represents the fatigue inhibition signal, which is a dimensionless output value ranging from (0,1). The closer the value is to 1, the more severe the patient's physiological fatigue and the stronger the need for systemic inhibition intervention.

[0074] The real-time physiological fatigue level is a dimensionless comprehensive index, which is the input of this formula. Its source is the state assessment module. This parameter is related to physiological risk. It is calculated by a weighted fusion algorithm through comprehensive analysis of multidimensional physiological data such as heart rate variability (HRV) and electromyography (EMG).

[0075] This represents the physiological fatigue safety threshold, also a dimensionless exponent, and is the input to this formula. Its source is a system preset value. It is a symbol commonly used in mathematics to represent a threshold, which is related to physiological fatigue level. Correspondingly;

[0076] The activation slope factor of the suppression signal is a dimensionless, adjustable parameter. This parameter is related to the suppression signal and controls the activation slope when the suppression signal is activated. Exceeding the threshold hour, The rate of signal growth;

[0077] The weighted fusion algorithm can be specifically represented as a normalized weighted summation of multiple physiological indicators:

[0078]

[0079] Indicates the first A primitive physiological indicator (such as the SDNN value in heart rate variability, or the mean power frequency (MPF) value in electromyography).

[0080] This represents a function that normalizes its value using the min-max normalization method.

[0081] This represents the preset weighting coefficient corresponding to the indicator, and the sum of all weighting coefficients is 1 (i.e., );

[0082] n: Represents the total number of raw physiological indicators used to calculate physiological fatigue.

[0083] These weights can be individually adjusted by clinicians based on the patient's specific condition and rehabilitation goals;

[0084] This formula is the standard logistic function, which is widely used in biology and machine learning to simulate phenomena with threshold activation characteristics. The inhibitory effect of physiological fatigue on the recovery process does not increase linearly, but rather surges near a certain safe threshold. The logistic function can accurately capture this characteristic: when fatigue is below the threshold, the inhibitory signal grows slowly; once the threshold is exceeded, the signal strengthens rapidly. This nonlinear response mechanism ensures that the system does not perform unnecessary intervention and inhibition in the low fatigue region, while providing a decisive and strong protective signal in the high fatigue region.

[0085] In the coupling effect analysis module, this formula is used to transform specific physiological fatigue values ​​into a standardized signal representing the strength of the motivation to inhibit intervention. This process achieves precise, non-linear quantification of "fatigue risk," providing a second key input for subsequent coupled analysis. It avoids unnecessary intervention and inhibition in low-fatigue areas while providing decisive and strong protective inhibition in high-fatigue areas, significantly improving the safety of the rehabilitation process.

[0086] Physiological fatigue safety threshold This is the cornerstone of the safety of this protocol, and its value is set based on clinical medical knowledge and individualized assessment. Its initial value is based on published clinical guidelines or statistical data from relevant studies, and during application, it is personalized by physicians according to the patient's specific circumstances, such as cardiac history, age, and cardiopulmonary reserve, to ensure safety. The inhibition signal activation slope factor... This defines the system's "risk aversion level," which can be automatically configured by the expert system based on the patient's risk level, or manually set by the therapist; a larger... This value is suitable for high-risk patients, enabling the system to respond extremely quickly and strongly to over-threshold fatigue, and vice versa.

[0087] Example 4:

[0088] When generating the coupling tension index, the coupling effect analysis module specifically generates the coupling tension index by multiplying the rehabilitation demand signal, the fatigue inhibition signal, and the preset coupling effect weighting coefficient.

[0089] In this embodiment, the coupling effect analysis module, after generating the rehabilitation demand signal and the fatigue inhibition signal, ultimately aims to generate a coupling tension index. The core function of this index is to quantify the degree of contradiction when the two commands, "high rehabilitation demand" and "high fatigue inhibition demand," coexist. Simple weighted summation obscures this contradiction, while this embodiment uses a product form, which accurately captures this "AND" logic. Only when both signals approach 1 is the contradiction considered sharp. The formula for calculating this index is:

[0090]

[0091] The coupling tension index is a dimensionless numerical value and is the final output of the coupling effect analysis module. It directly quantifies the conflict intensity between the two instructions, "enhancing intervention" and "inhibiting intervention".

[0092] The signal indicating the need for rehabilitation is the input to this formula, and its source is the calculation result of the first step mentioned above;

[0093] This represents the fatigue suppression signal, which is the input to this formula and originates from the calculation results in the second step mentioned above.

[0094] The coupling effect weighting coefficient is a dimensionless, preset adjustable parameter, typically set to 1, used for standardization or scaling. The order of magnitude;

[0095] This formula borrows the concept of coupling strength from systems engineering, which states that the coupling effect is strongest when multiple interacting factors exist simultaneously and are all significant; the core of the technical problem lies in handling contradictory instructions; this contradiction only exists when "rehabilitation needs are high" ( Approaching 1) and "high demand for fatigue inhibition" It is most acute when it approaches 1); the product form can precisely capture this logic, only when... and When both are significant, The value will increase significantly only if any one of the signals is very weak (close to 0); if any one of the signals is very weak, it means that there is no or only a slight instruction contradiction at this time. The value is also correspondingly small, thus avoiding overreaction to non-contradictory states;

[0096] Coupling tension index As the output of the coupling effect analysis module, it is passed to the intervention decision-making and dynamic adjustment module, serving as the core basis for resolving decision-making contradictions; this invention proposes for the first time a calculable index. To quantify command conflicts during the rehabilitation process, a vague, qualitative contradiction is transformed into a precise, quantitative engineering parameter, providing a solid foundation for solving the problem and enabling precise control of subsequent intervention decisions.

[0097] Coupling effect weighting coefficient It is usually set to 1 as a standard benchmark. In specific application scenarios, if it is necessary to amplify or reduce the impact of coupling effect, this parameter can be adjusted, but its default value of 1 is sufficient for most cases.

[0098] Example 5:

[0099] When determining the basic intervention adjustment amount, the intervention decision and dynamic adjustment module is specifically used for: S31, calculating the difference between the rehabilitation demand signal and the fatigue inhibition signal to obtain the basic adjustment driving signal; S32, multiplying the basic adjustment driving signal by a preset proportional gain constant to generate the basic intervention adjustment amount.

[0100] The proportional gain constant is set based on the system's target response speed and stability requirements, and in combination with the patient's tolerance.

[0101] In this embodiment, the intervention decision-making and dynamic adjustment module employs a proportional controller logic to determine the basic intervention adjustment amount. It calculates the difference between the rehabilitation demand signal and the fatigue inhibition signal to obtain a basic adjustment driving signal, which intuitively reflects the system's basic adjustment intention without considering coupling effects. Then, this driving signal is multiplied by a preset proportional gain constant to generate the basic intervention adjustment amount. This process forms the basis for the subsequent final intervention decision; its calculation formula is as follows:

[0102]

[0103] This represents the baseline intervention adjustment level, an intermediate calculation variable whose physical unit is consistent with the final intervention intensity unit (e.g., degrees per second or milliamperes). A positive value indicates a tendency to increase the intervention, while a negative value indicates a tendency to decrease the intervention.

[0104] This represents the proportional gain constant, which is a preset adjustable parameter with the same unit as the intervention intensity to ensure consistency in the formula's dimensions.

[0105] and These are the dimensionless rehabilitation demand signal and fatigue inhibition signal obtained from the aforementioned calculations, respectively.

[0106] This formula is based on the classic proportional control concept in feedback control theory and constructs a basic incremental adjustment model. The difference directly reflects whether the current system is "demand-driven" or "inhibition-driven," and its sign and magnitude determine the direction and basic strength of the adjustment; this provides the system with a basic adjustment logic when there is no significant conflict.

[0107] This formula calculates As the basis for subsequent calculations of actual intervention and regulation, it combines two standardized opposing signals into a driving signal with clear physical meaning, simplifying the decision-making logic and clearly expressing the system's basic regulatory tendency in the absence of conflict, namely, enhancing intervention (when...). (when) or reduce intervention (when) hour);

[0108] proportional gain constant This is the standard proportional controller gain, derived from system debugging; setting this parameter requires a trade-off between system response speed and stability; a larger... A high value will make the system respond faster to schedule deviations, but may lead to overshoot and oscillations; a smaller value will result in a smoother but potentially slower response; in practical applications, The value needs to be adjusted and set according to the patient's specific tolerance and rehabilitation goals in order to achieve the best control effect.

[0109] Example 6:

[0110] When generating the final intervention intensity command, the intervention decision and dynamic adjustment module specifically performs the following steps: S41, performs a hyperbolic tangent function operation on the coupling tension index to obtain a modulation reference value; S42, subtracts the modulation reference value from the value 1 to generate the nonlinear modulation factor; S43, multiplies the basic intervention adjustment amount by the nonlinear modulation factor to generate the actual intervention adjustment amount; S44, adds the actual intervention adjustment amount to the current intervention intensity to generate the final intervention intensity command.

[0111] The intervention decision-making and dynamic adjustment module in this embodiment demonstrates its core nonlinear modulation mechanism when generating the final intervention intensity command. Instead of simply executing basic adjustment amounts, it introduces a modulation mechanism based on the coupling tension index to automatically enter a buffering or braking state when conflicts are acute. This mechanism achieves a dynamic balance between rehabilitation needs and fatigue risk through a smooth, nonlinear modulation factor. The generation of the final intervention intensity command is the endpoint of the entire decision-making process, and its calculation formula is as follows:

[0112]

[0113] The final intervention intensity command is the final output of this formula and is sent to the rehabilitation equipment actuator. Its unit is (degrees per second) or (milliamperes), etc.

[0114] This represents the current intervention intensity and is the input to this formula. Its source is the output value recorded by the system at the previous time step. The unit is Ω. Similarly, the initial intervention strength of the system, i.e., the strength at the time of the first calculation. The value is manually preset by the rehabilitation therapist based on the patient's initial postoperative condition assessment and tolerance level;

[0115] This represents the baseline intervention adjustment amount, which is the result of the previous calculation, and the unit is also the same as... same;

[0116] The dimensionless coupling tension index is the output of the coupling effect analysis module.

[0117] The hyperbolic tangent function is a standard mathematical function known to those skilled in the art, and the formula contains... This refers to the nonlinear modulation factor, whose value is dimensionless.

[0118] This formula is based on an incremental adjustment model of feedback control theory, and innovatively introduces a nonlinear modulation term based on the hyperbolic tangent function tanh() to handle coupling tension; when the coupling tension exponent... When the value is very high, it indicates that the system is facing a serious decision-making contradiction. At this time, the system should not simply stop or choose one side, but should enter a buffer state, that is, suppress any adjustment action; modulation term This goal was achieved: when When close to 0 (no conflict), When the modulation term is close to 1, the system adjusts according to the basic logic; when When the conflict intensifies, The base adjustment value rapidly approaches 1, while the modulation term rapidly approaches 0, thus greatly compressing the base adjustment value. This makes its change to the current intensity negligible; this is equivalent to the system automatically suppressing its own regulatory impulse when the contradiction is sharp, maintaining stability, and waiting for the contradiction to be relieved; the tanh function, due to its approximately linear nature near the origin and rapid saturation far from the origin, provides a smooth braking effect that is better than linear decay.

[0119] This formula is the core of the intervention decision and dynamic adjustment module; it receives all state information and coupled analysis results to calculate the most suitable rehabilitation intensity for the next moment. This formula effectively solves the contradictory instruction problem in the background technology through a smooth, non-linear modulation factor, and achieves a dynamic balance between rehabilitation needs and fatigue risks. This mechanism ensures patient safety while avoiding complete cessation of rehabilitation due to excessive conservatism, thus maximizing the continuity and effectiveness of rehabilitation. This design enables the system to automatically enter a buffer state when faced with the risk scenario of high fatigue and rehabilitation stagnation, effectively avoiding secondary damage or excessive fatigue that may be caused by rashly increasing the intensity of intervention, and significantly improving the overall safety and efficiency of rehabilitation.

[0120] All adjustable parameters in this final formula ( , , , , The determination methods for all parameters have been explained in the preceding steps; the parameters of the entire model have clear clinical and engineering significance, and their sources and adjustment methods are clear. This makes it easy to configure and adaptively adjust the rehabilitation program according to the individual differences of patients, truly achieving individualized treatment and possessing high feasibility.

[0121] 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. An intelligent postoperative rehabilitation nursing system for the elderly based on the Internet of Things, characterized in that, include: The data acquisition module is used to collect patients' physiological and motion data in real time through wearable sensors; The status assessment module is used to receive the physiological data and the movement data, and process the data to calculate and generate the rehabilitation progress rate, which characterizes rehabilitation progress, and the physiological fatigue level, which characterizes physiological risk. The coupling effect analysis module is used for: Based on the stated rehabilitation progress rate and the preset target rehabilitation progress rate, a standardized rehabilitation demand signal is generated. Based on the stated physiological fatigue level and a preset physiological fatigue safety threshold, a standardized fatigue inhibition signal is generated; The rehabilitation demand signal and the fatigue inhibition signal are then combined to generate a coupling tension index that quantifies the degree of contradiction between the two. The intervention decision-making and dynamic adjustment module is used for: Based on the rehabilitation demand signal and the fatigue inhibition signal, the basic intervention adjustment amount is determined; Based on the coupling tension index, a nonlinear modulation factor is generated to suppress the basic intervention regulation amount; The basic intervention adjustment amount is calculated with the nonlinear modulation factor to generate the actual intervention adjustment amount; The actual intervention adjustment amount is combined with the current intervention intensity recorded by the system to generate a final intervention intensity instruction, which is then sent to the rehabilitation device actuator. When generating the rehabilitation demand signal, the coupling effect analysis module is specifically used for: S11, Calculate the difference between the target recovery progress rate and the recovery progress rate to obtain the progress gap; S12, divide the progress gap by a preset demand signal sensitivity adjustment factor to obtain a normalized input value; S13, apply an S-shaped function operation to the normalized input value to generate the rehabilitation demand signal; The specific steps in generating the fatigue suppression signal by the coupling effect analysis module include: S21, calculate the difference between the physiological fatigue level and the physiological fatigue safety threshold to obtain the fatigue exceedance value; S22, Multiply the fatigue over-limit value by a preset suppression signal activation slope factor to obtain the activation input value; S23, apply a logistic function operation to the activation input value to generate the fatigue suppression signal; When generating the coupling tension index, the coupling effect analysis module specifically generates the coupling tension index by multiplying the rehabilitation demand signal, the fatigue inhibition signal, and the preset coupling effect weighting coefficient. When determining the basic intervention adjustment amount, the intervention decision and dynamic adjustment module is specifically used for: S31. Calculate the difference between the rehabilitation demand signal and the fatigue inhibition signal to obtain the basic adjustment driving signal; S32. Multiply the basic adjustment drive signal by a preset proportional gain constant to generate the basic intervention adjustment amount; When generating the final intervention intensity instruction, the intervention decision-making and dynamic adjustment module is specifically used for: S41. Perform hyperbolic tangent function calculation on the coupling tension index to obtain the modulation reference value; S42. Subtract the modulation reference value from the numerical value 1 to generate the nonlinear modulation factor; S43. Multiply the basic intervention adjustment amount by the nonlinear modulation factor to generate the actual intervention adjustment amount; S44. Add the actual intervention adjustment amount to the current intervention intensity to generate the final intervention intensity instruction.

2. The intelligent postoperative rehabilitation nursing system for the elderly based on the Internet of Things according to claim 1, characterized in that, The target recovery rate is pre-set based on individualized information such as the patient's age, type of surgery, and preoperative physical condition; the demand signal sensitivity adjustment factor is obtained through statistical learning or offline optimization using historical clinical data.

3. The intelligent postoperative rehabilitation nursing system for the elderly based on the Internet of Things according to claim 1, characterized in that, The physiological fatigue safety threshold is based on statistical data from published clinical guidelines or related studies and is individually set according to the patient's cardiac history and age; the inhibition signal activation slope factor is configured according to the patient's risk level.

4. The intelligent postoperative rehabilitation nursing system for the elderly based on the Internet of Things according to claim 1, characterized in that, The proportional gain constant is set based on the system's target response speed and stability requirements, and in conjunction with the patient's tolerance.

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