Sleep-aiding linkage system based on ankle recovery monitoring

Abstract

The invention discloses a sleep-aiding linkage system based on ankle recovery monitoring, and relates to the technical field of medical health and intelligent monitoring. According to the sleep-aiding linkage system based on ankle recovery monitoring, rich biomechanical parameters of an ankle area are collected in real time through a multi-mode sensor; the method comprises the steps that plantar pressure distribution, ankle joint motion trails, foot temperature and skin impedance are covered, the data are subjected to multi-dimensional fusion analysis, calculation is conducted in combination with a built-in neural network model, the ankle fatigue state and the recovery stage can be accurately analyzed, the method is different from a traditional fixed threshold value judgment mode, and the system can be dynamically adjusted according to individual differences of users and historical data; and furthermore, the sleep-aiding equipment linkage control module is used for finely adjusting operation parameters of mattress hardness, environment illumination intensity and sound wave frequency for sleep monitoring equipment and environment temperature control equipment according to an analysis result, so that an exclusive sleep environment matched with an ankle recovery process is created for a user.

Description

Technical Field

[0001] The present invention relates to the field of medical health and intelligent monitoring technology, and specifically to a sleep-aid linkage system based on foot and ankle recovery monitoring. Background Art

[0002] As the incidence of ankle injuries gradually increases, ankle injuries not only affect patients' normal walking and motor functions, but may also lead to decreased sleep quality, thereby affecting the overall recovery of the body. Therefore, how to effectively monitor the recovery of the ankle and improve the patient's sleep quality through a sleep-aid linkage system has become a hot topic in current rehabilitation medicine research.

[0003] Currently, existing ankle recovery monitoring systems mainly focus on collecting and analyzing biomechanical parameters of the ankle, such as plantar pressure distribution and ankle joint motion trajectory. However, these systems often have the following shortcomings: Lack of accurate judgment of ankle fatigue status and recovery stage. Existing monitoring systems usually use fixed thresholds to judge ankle fatigue status and recovery stage, and cannot dynamically adjust according to individual patient differences and historical data, resulting in inaccurate judgment results. The sleep-aid linkage system is not intelligent enough. The existing sleep-aid linkage system can often only adjust the operating mode of the sleep-aid equipment according to preset parameters, and cannot make real-time adjustments based on the recovery of the foot and ankle, resulting in poor sleep-aid effects.

[0004] In summary, the existing ankle recovery monitoring system lacks accurate judgment of the ankle fatigue state and recovery stage. Therefore, a sleep-aid linkage system based on ankle recovery monitoring is developed to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a sleep-aid linkage system based on ankle recovery monitoring. It can collect the biomechanical parameters of the ankle area in real time, and accurately judge the fatigue state and recovery stage of the ankle through multi-dimensional fusion analysis and neural network model calculation. At the same time, the system can also dynamically adjust the operating parameters of the sleep-aid device according to the ankle recovery situation, improve the sleep-aid effect, and thus promote the recovery of the ankle. In addition, the system also has a visual user interaction interface, and users can easily view the ankle recovery situation and adjust the operating parameters of the sleep-aid device to improve the user experience.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a sleep-aid linkage system based on foot and ankle recovery monitoring, the system comprising: a foot and ankle data acquisition module, a foot and ankle status analysis module, a foot pressure dynamic regulation module, a sleep-aid device linkage control module, and a user interaction module; The ankle data acquisition module is used to collect biomechanical parameters of the ankle area in real time through multimodal sensors, including plantar pressure distribution, ankle joint motion trajectory, foot temperature and skin impedance data, and send the data to the ankle status analysis module through a wireless transmission protocol; The ankle status analysis module is used to perform multi-dimensional fusion analysis on the collected data, combine the preset ankle recovery model and fatigue threshold, determine the fatigue state and recovery stage of the user's ankle, and generate dynamic control instructions; The foot pressure dynamic control module is used to adjust the foot pressure distribution in real time through the airbag array driven by the micromotor according to the instructions of the analysis module; The sleep aid device linkage control module is used to exchange data between the foot and ankle status analysis results and the sleep monitoring device and the ambient temperature control device, and dynamically adjust the operating parameters of the sleep aid device according to the foot and ankle recovery stage, including mattress hardness, ambient light intensity, and sound wave frequency; The user interaction module is used to provide a visual operation interface, support users to customize foot and ankle recovery goals, pressure adjustment intensity and sleep preference parameters, and provide real-time feedback on system operation status and data logs.

[0007] Furthermore, the foot and ankle data acquisition module includes: A high-precision pressure sensor array, embedded in the foot pad in the form of a flexible circuit board, measures the pressure value of the sole of the foot at a fixed sampling interval; The inertial measurement unit, integrated into the ankle strap, captures the three-dimensional motion trajectory of the ankle joint through a three-axis accelerometer and gyroscope to obtain the motion amplitude and frequency; Temperature-impedance composite sensors are distributed in the arch and heel areas to simultaneously measure skin surface temperature changes and impedance values ​​to assess local blood circulation status; The data preprocessing unit performs noise reduction on the original signal, removes non-periodic interference, and eliminates motion artifacts.

[0008] Furthermore, the foot and ankle status analysis module includes: The multi-source data fusion unit is used to align the plantar pressure distribution, ankle joint motion trajectory, foot temperature, and skin impedance data by timestamp, and uses a weighted fusion strategy to generate a comprehensive foot and ankle state feature vector, where the pressure data accounts for 40%, the motion data accounts for 30%, and the temperature and impedance each account for 15%; The ankle recovery model calculation unit has a built-in neural network model trained based on biomechanical characteristics. After inputting the feature vector, it outputs the current fatigue level value. The dynamic threshold judgment unit dynamically adjusts the fatigue threshold range based on the user's historical data. When the fatigue score exceeds 120% of the threshold in three consecutive samplings, the foot and ankle are judged to be in an acute fatigue state. Otherwise, the foot and ankle are judged to be in a stable fatigue state. The recovery phase decision unit compares the fatigue level value with the preset acute phase, transition phase, and stable phase threshold intervals to determine the recovery phase, and triggers the phase switching instruction when the value exceeds the upper limit of the threshold interval for three consecutive sampling periods; The instruction generation unit maps the fatigue state and recovery stage into pressure control instructions, including the airbag pressure target value, adjustment rate and partition priority.

[0009] Furthermore, the foot pressure dynamic control module includes a pressure regulation execution unit, a pressure feedback monitoring unit and a control strategy generation unit; The pressure regulation execution unit is composed of multiple independently controllable micro-motor-driven airbag arrays. The micro-motor adjusts the airbag inflation volume according to the pressure control instructions of the foot and ankle status analysis module, thereby changing the pressure distribution in different areas of the sole of the foot; The pressure feedback monitoring unit includes pressure sensors distributed on the surface of the airbag, which monitors the adjusted plantar pressure data in real time and feeds the data back to the control strategy generation unit; The control strategy generation unit receives data from the pressure feedback monitoring unit and instructions from the foot and ankle status analysis module, and judges the expected effect of the current pressure adjustment based on the plantar pressure distribution standard and the user's personalized needs. When the expected effect does not meet the user's needs, the pressure adjustment instruction is regenerated and sent to the pressure adjustment execution unit to achieve closed-loop dynamic control of the plantar pressure distribution.

[0010] Furthermore, the sleep aid device linkage control module includes a device communication unit, a parameter matching unit, a linkage coordination unit and an instruction execution unit; The device communication unit uses a unified communication protocol to establish a data exchange channel with the sleep monitoring device and the environmental temperature control device, receiving the user's sleep data collected by the sleep monitoring device and the current operating parameters of the environmental temperature control device, and at the same time transmitting the results of the foot and ankle status analysis module to each device; The parameter matching unit stores the sleep aid device parameter configuration table corresponding to different stages of foot and ankle recovery, matches the recovery stage results obtained by the foot and ankle status analysis module with the configuration table, and determines the device operating parameters such as mattress hardness, ambient light intensity, and sound wave frequency; The linkage coordination unit coordinates the working order of each sleep aid device according to the parameters determined by the parameter matching unit; The instruction execution unit sends control instructions to each sleep aid device according to the working order of the linkage coordination unit, so that the sleep monitoring equipment and environmental temperature control equipment can operate in coordination according to the set parameters.

[0011] Furthermore, the user interaction module includes: An interface display unit displays the real-time data collected by the foot and ankle data acquisition module, the analysis results of the foot and ankle status analysis module, the pressure distribution status of the foot pressure dynamic control module, and the device operating parameters of the sleep aid device linkage control module in a graphical interface; An input processing unit receives parameter information of foot and ankle recovery goals, pressure adjustment intensity, and sleep preferences input by the user on the operation interface; The data feedback unit organizes user operation records and data logs during system operation, including changes in foot and ankle data and the execution status of device control instructions, into charts and provides feedback to users; Set up a storage unit to encrypt and store user-defined parameter information, which will be automatically called the next time the system runs. At the same time, the stored data will be backed up regularly to prevent data loss.

[0012] Furthermore, the implementation process of the neural network model of the ankle recovery model calculation unit is as follows: Input layer: The input layer receives the comprehensive state feature vector of the foot and ankle ,in, And the corresponding four-dimensional data of plantar pressure distribution, ankle joint motion trajectory, foot temperature and skin impedance are normalized to obtain a four-dimensional vector, that is, ,in represents the normalized pressure data, It's sports data. is the temperature data, is the impedance data; The first hidden layer: performs a fully connected feature transformation on the data of the input layer, performs a weighted summation on the input data and introduces a nonlinear transformation to extract representative features. The calculation formula of the fully connected feature transformation is , the weight matrix It is the connection relationship and weight size between the input layer and the first hidden layer. That is, the matrix has 8 rows and 4 columns. The 4 neurons corresponding to the four types of data in the input layer are connected to the 8 neurons preset in the first hidden layer respectively. Each element in the matrix Represents the input layer The neurons in the first hidden layer The weight of each neuron when transmitting a signal, Indicates the sequence number of the first hidden layer neurons, from 1 to 8, Indicates the sequence number of the input layer neurons, from 1 to 4, bias is a constant term set separately for each neuron in the first hidden layer. The first hidden layer corresponds to neurons, is the activation function and , during the calculation process, when When the result is less than 0, the output of the neuron is 0, and the first hidden layer outputs an 8-dimensional vector ; The second hidden layer processes the output of the first hidden layer and performs weighted transformation by introducing the attention mechanism to obtain feature representation. The attention mechanism calculates the attention weight matrix ,in, Is the attention weight matrix, which is used to measure the importance of each feature of the first hidden layer output to the calculation of the second hidden layer neuron output. The elements in the matrix are Represents the first hidden layer The neurons in the second hidden layer The softmax function is used to influence the attention weight of each neuron. The calculation result is converted into a probability distribution form, so that the obtained attention weight matrix Each element in takes a value between 0 and 1, and the sum of all elements is 1, that is, ,in Indicates the effect of the first hidden layer output on the second hidden layer The attention weights of neurons are used to calculate the output of the second hidden layer. ,in is the weight matrix of the second hidden layer, which is responsible for converting the 8-dimensional output of the first hidden layer into the 4-dimensional input of the second hidden layer. Represents the first hidden layer neurons in the second hidden layer The weights of the connections between neurons, is the bias of the second hidden layer, each The second hidden layer corresponds to Neurons, symbol Represents the attention weight matrix and In this way, the attention weight is used to weight the data and obtain the second hidden layer. ; Output layer: Calculate the final ankle fatigue level value based on the features extracted by the hidden layer ,in, is the weight vector and , Indicates that the weight vector Transpose and Perform dot product operation, weight vector Each element in The second hidden layer The contribution weight of each neuron to the final fatigue level output determines the degree of influence of the output of the corresponding neuron in the second hidden layer on the final result. Is the bias value of the output layer, which is used to adjust the calculation results and is a set of real numbers, the calculated The value is mapped to the interval [0, 10] to represent the fatigue state of the ankle.

[0013] Furthermore, the dynamic threshold judgment unit calculates the fatigue level value according to the neural network model and combines the dynamically adjusted threshold value to set the initial fatigue threshold value to , record the user's historical fatigue data series ,in Indicates the The fatigue value of the sampling times is used to calculate the mean of historical data and standard deviation , the dynamically adjusted threshold ,in, and is the adjustment coefficient, Used to control the influence of the mean on the threshold. It is used to control the influence of the standard deviation on the threshold, and , , when the fatigue score is All exceeded When the ankle is in an acute fatigue state, it is determined that the ankle is in a stable fatigue state; otherwise, the ankle is determined to be in a stable fatigue state; The recovery stage decision unit will calculate the fatigue level value Compared with the preset acute phase, transition phase and stable phase threshold intervals, when the value exceeds the upper limit of the threshold interval for three consecutive sampling periods, the stage switching instruction is triggered. The acute phase threshold interval is [0, 3], the transition phase threshold interval is (3, 6], and the stable phase threshold interval is (6, 10]. When the fatigue level value exceeds the upper limit of the threshold interval for three consecutive sampling periods, the stage switching instruction is triggered. are greater than 3, the acute phase switches to the transition phase; when the If both are greater than 6, the system switches from the transition period to the stable period. In this way, the dynamic threshold judgment unit and the recovery phase decision unit are closely linked to achieve a dynamic and accurate assessment of the ankle recovery status, providing a basis for subsequent regulation and intervention of the system.

[0014] Compared with existing technologies, this sleep-aiding linkage system based on foot and ankle recovery monitoring has the following beneficial effects: 1. The sleep-aid linkage system based on ankle recovery monitoring of the present invention uses multimodal sensors to collect rich biomechanical parameters of the ankle area in real time, covering plantar pressure distribution, ankle joint movement trajectory, foot temperature and skin impedance. These data are analyzed through multi-dimensional fusion and combined with the built-in neural network model calculation to accurately analyze the fatigue state and recovery stage of the ankle. Different from the traditional fixed threshold judgment method, the system can be dynamically adjusted according to individual differences of users and historical data, greatly improving the judgment accuracy. Then, the sleep-aid device linkage control module uses this analysis result to fine-tune the operating parameters of the mattress hardness, ambient light intensity, and sound wave frequency for the sleep monitoring equipment and environmental temperature control equipment, creating a unique sleeping environment for the user that matches the ankle recovery process, effectively promoting ankle recovery, and significantly improving sleep quality.

[0015] 2. The user interaction module of this system provides an intuitive and visual operation interface. Users can easily customize foot and ankle recovery goals, pressure adjustment intensity and sleep preference parameters. They can also obtain real-time system operation status and data log feedback, enhance the sense of participation and control, continuously adapt to the unique needs of different users and changes in the foot and ankle recovery process, and comprehensively improve the overall performance of the system and user satisfaction.

[0016] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0018] Figure 1 This is an operational flow chart of the sleep-aid linkage system based on foot and ankle recovery monitoring; Figure 2 This is a schematic diagram of the module composition of the sleep-aid linkage system based on foot and ankle recovery monitoring. DETAILED DESCRIPTION

[0019] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. Example 1

[0020] This embodiment describes in detail the components and working principles of each module in the sleep aid linkage system based on foot and ankle recovery monitoring. Figure 2 As shown, the system integrates foot and ankle data acquisition module, foot and ankle status analysis module, foot pressure dynamic regulation module, sleep aid equipment linkage control module, and user interaction module. It collects foot and ankle biomechanical parameters through multimodal sensors, and accurately judges the foot and ankle fatigue status and recovery stage through multi-dimensional fusion analysis and neural network model calculation, and then dynamically adjusts the sleep aid equipment parameters. At the same time, it provides users with a visual interactive interface to enhance user experience, effectively promote foot and ankle recovery and improve sleep quality.

[0021] The ankle data acquisition module is responsible for acquiring key biomechanical parameters of the ankle area. The module consists of a high-precision pressure sensor array, an inertial measurement unit, a temperature-impedance composite sensor, and a data preprocessing unit. The high-precision pressure sensor array is embedded in the foot pad in the form of a flexible circuit board. Its purpose is to achieve accurate measurement of the pressure distribution of the sole of the foot. When the human body is walking or stationary, different areas of the sole of the foot are subjected to different pressures. These pressure changes reflect the force conditions of the ankle. The sensor works at a fixed sampling interval and continuously collects pressure values ​​at various points on the sole of the foot to form pressure distribution data; the inertial measurement unit is integrated into the ankle strap, and the internal three-axis accelerometer and gyroscope work together. The three-axis accelerometer can capture the acceleration changes of the ankle joint in three axes (usually front and back, left and right, and up and down), while the gyroscope focuses on measuring the angular velocity of the ankle joint. By analyzing these data, the three-dimensional motion trajectory of the ankle joint can be accurately calculated. Obtain movement amplitude and frequency information; temperature-impedance composite sensors are distributed in the arch and heel areas. These two areas play a key role in the blood circulation and support of the foot and ankle. The sensors synchronously measure the skin surface temperature changes and impedance values. The temperature changes can reflect the heat changes in the local blood circulation, while the changes in the impedance value are related to the physiological state of the tissue and can be used to evaluate the local blood circulation state. For example, when the blood circulation is not smooth, the temperature may drop and the impedance value will also change accordingly; the data preprocessing unit processes the original signals collected by the above sensors. Since the actual collection process will be subject to various interferences, such as environmental noise, artifacts caused by human movement, etc., noise reduction processing is required. The data preprocessing unit can effectively separate useful signals from interference signals. At the same time, by eliminating motion artifacts, it ensures that the data for subsequent analysis is accurate and reliable. The preprocessed data is sent to the foot and ankle status analysis module using a wireless transmission protocol.

[0022] The ankle state analysis module receives data from the ankle data acquisition module and conducts in-depth analysis to determine the fatigue state and recovery stage of the ankle. The module includes a multi-source data fusion unit, an ankle recovery model calculation unit, a dynamic threshold judgment unit, a recovery stage decision unit, and an instruction generation unit. Among them, the multi-source data fusion unit first aligns the plantar pressure distribution, ankle joint motion trajectory, foot temperature, and skin impedance data according to timestamps. This is because there are slight differences in the time when different sensors collect data. By aligning timestamps, the synchronization of data can be guaranteed during subsequent analysis. Afterwards, a weighted fusion strategy is used to generate a comprehensive ankle state feature vector, in which the pressure data The weight accounts for 40%, because the plantar pressure distribution can directly reflect the force of the ankle during support and movement, which is of great significance for judging the state of the ankle; the motion data accounts for 30%, and the information such as the movement trajectory and frequency of the ankle joint can reflect the movement function and fatigue accumulation of the ankle; the temperature and impedance each account for 15%, which can provide supplementary information from the aspects of blood circulation and tissue physiological state. Through this weighted fusion method, the multi-source data are integrated into a comprehensive feature vector to more comprehensively describe the state of the ankle; the ankle recovery model calculation unit has a built-in neural network model based on biomechanical feature training, and the input of the model is the comprehensive state feature vector of the ankle generated by the multi-source data fusion unit. ( , corresponding to the four-dimensional data of plantar pressure distribution, ankle joint motion trajectory, foot temperature and skin impedance). Before entering the model, the data is normalized to obtain a four-dimensional vector ,in represents the normalized pressure data, It's sports data. is the temperature data, It is the impedance data. After the input layer receives the vector, the data enters the first hidden layer. The first hidden layer performs a full-connection feature transformation on the input data. The calculation formula is , the weight matrix Describes the connection relationship and weight size between the input layer and the first hidden layer. The matrix has 8 rows and 4 columns, which means that the 4 neurons of the four types of data in the input layer are connected to the 8 neurons preset in the first hidden layer respectively. Each element in the matrix Represents the input layer The neurons in the first hidden layer The weight of each neuron when transmitting a signal, represents the sequence number of the first hidden layer neuron (from 1 to 8), Indicates the sequence number of the input layer neuron (from 1 to 4). is a constant term set separately for each neuron in the first hidden layer. The first hidden layer corresponds to neurons, activation function , during the calculation process, when When the result is less than 0, the output of the neuron is 0; when the result is greater than 0, the output of the neuron is equal to the result value. After such calculation, the first hidden layer outputs an 8-dimensional vector , realizes the preliminary feature extraction and nonlinear transformation of the input data, so that the model can learn more complex patterns. The second hidden layer introduces the attention mechanism to further process the output of the first hidden layer. First, calculate the attention weight matrix ,in Is the attention weight matrix, which is used to measure the importance of each feature of the first hidden layer output to the calculation of the second hidden layer neuron output. The elements in the matrix are Represents the first hidden layer The neurons in the second hidden layer The softmax function is used to influence the attention weight of each neuron. The calculation result is converted into a probability distribution form, so that the obtained attention weight matrix Each element in takes a value between 0 and 1, and the sum of all elements is 1, that is, ,in Indicates the effect of the first hidden layer output on the second hidden layer The attention weights of neurons are then used to calculate the output of the second hidden layer ,in is the weight matrix of the second hidden layer, which is responsible for converting the 8-dimensional output of the first hidden layer into the 4-dimensional input of the second hidden layer. Represents the first hidden layer neurons in the second hidden layer The weights of the connections between neurons, is the bias of the second hidden layer, each The second hidden layer corresponds to Neurons, symbol Represents the attention weight matrix and In this way, the attention weight is used to weight the data, highlighting the features that are more important for judging the level of ankle fatigue, and the output of the second hidden layer is obtained. The output layer calculates the final ankle fatigue level value based on the features extracted by the hidden layer ,in, is the weight vector , Represents the weight vector Transpose and Perform dot product operation, weight vector Each element in The second hidden layer The contribution weight of each neuron to the final fatigue level output determines the degree of influence of the output of the corresponding neuron in the second hidden layer on the final result. Is the bias value of the output layer, which is used to adjust the calculation results. The value is mapped to the interval [0, 10] to represent the fatigue state of the ankle. The larger the value, the higher the fatigue degree. The dynamic threshold judgment unit judges the fatigue state of the ankle based on the fatigue level value calculated by the neural network model and the dynamically adjusted threshold. First, the initial fatigue threshold is set to , record the user's historical fatigue data series ,in Indicates the The fatigue value of the sampling is calculated by averaging the historical data. and standard deviation , the dynamically adjusted threshold ,in and is the adjustment coefficient, Used to control the influence of the mean on the threshold. It is used to control the influence of the standard deviation on the threshold, and , , when the fatigue score is All exceeded When the ankle is in an acute fatigue state, it is determined that the ankle is in a stable fatigue state. This dynamic adjustment of the threshold value can more accurately judge the ankle fatigue state according to the individual differences of users and changes in historical data; the decision unit in the recovery stage will use the fatigue level value Compared with the preset acute phase, transition phase, and stable phase threshold intervals, the preset acute phase threshold interval is [0, 3], the transition phase threshold interval is (3, 6], and the stable phase threshold interval is (6, 10]. When the value exceeds the upper limit of the threshold interval for three consecutive sampling cycles, the stage switching instruction is triggered. For example, if the fatigue level value for three consecutive sampling cycles exceeds are all greater than 3, the acute phase switches to the transition phase; if the If both are greater than 6, the transition period switches to the stable period. In this way, the recovery stage of the foot and ankle can be tracked in real time, providing a basis for subsequent regulation. The instruction generation unit maps the fatigue state and recovery stage into pressure regulation instructions, including the airbag pressure target value, regulation rate and partition priority. According to different fatigue states and recovery stages, the appropriate plantar pressure regulation scheme is determined to promote relaxation of the foot and ankle muscles and improve blood circulation. For example, in an acute fatigue state, the airbag pressure needs to be adjusted more significantly, and priority should be given to regulating the plantar area that is more critical to relieving fatigue. In a stable fatigue state, the pressure regulation is relatively mild.

[0023] The foot pressure dynamic control module adjusts the plantar pressure distribution in real time according to the instructions generated by the foot and ankle status analysis module. The module consists of a pressure regulation execution unit, a pressure feedback monitoring unit and a control strategy generation unit. Among them, the pressure regulation execution unit is composed of multiple independently controllable micro-motor-driven airbag arrays. The micromotor adjusts the airbag inflation volume according to the pressure control instructions of the foot and ankle status analysis module, thereby changing the pressure distribution in different areas of the plantar. For example, when the instruction requires increasing the pressure in the plantar area, the corresponding micromotor will drive the airbag to increase the inflation volume, thereby increasing the pressure in this area; otherwise, the inflation volume will be reduced to reduce the pressure. In this way, precise control of plantar pressure is achieved; the pressure feedback monitoring unit includes pressure sensors distributed on the surface of the airbag, which monitor the adjusted plantar pressure data in real time. After the pressure regulation execution unit adjusts the airbag pressure, the pressure feedback monitoring unit immediately collects the current plantar pressure data and feeds it back to the control strategy generation unit. This ensures that the system understands the actual effect of pressure regulation in a timely manner and provides a basis for subsequent regulation; the control strategy generation unit receives the data from the pressure feedback monitoring unit and the instructions of the ankle status analysis module, and judges the expected effect of the current pressure regulation in combination with the plantar pressure distribution standard and the user's personalized needs. The plantar pressure distribution standard is based on medical Research and clinical experience have determined that different ankle states and recovery stages correspond to different reasonable pressure distribution ranges. User personalized needs take into account the user's special circumstances, such as personal sensitivity to pressure, habitual walking style, etc. When the expected effect does not meet user needs, the control strategy generation unit regenerates the pressure adjustment instruction and sends it to the pressure adjustment execution unit. For example, if the pressure in a certain area still deviates from the standard range after the current adjustment and does not meet the user's personalized needs, the control strategy generation unit will adjust the airbag pressure target value, adjustment rate and other parameters, and resend the instruction to achieve closed-loop dynamic control of the plantar pressure distribution, ensuring that the plantar pressure is always in a state that is conducive to ankle recovery.

[0024] The sleep aid device linkage control module is responsible for exchanging data between the foot and ankle status analysis results and the sleep monitoring device and the environmental temperature control device, and dynamically adjusting the operating parameters of the sleep aid device according to the foot and ankle recovery stage. The module consists of a device communication unit, a parameter matching unit, a linkage coordination unit and an instruction execution unit. The device communication unit adopts a unified communication protocol to establish a data interaction channel with the sleep monitoring device and the environmental temperature control device. Through this channel, the device communication unit receives the user sleep data collected by the sleep monitoring device, such as sleep duration, sleep depth, number of turns, etc., as well as the current operating parameters of the environmental temperature control device, such as indoor temperature and humidity. At the same time, the results of the ankle status analysis module (including information such as ankle fatigue status and recovery stage) are transmitted to each device to realize data sharing and interaction; the parameter matching unit stores the sleep aid device parameter configuration table corresponding to different stages of ankle recovery. After receiving the recovery stage results obtained by the ankle status analysis module, the parameter matching unit matches it with the configuration table to determine the device operating parameters such as mattress hardness, ambient light intensity, and sound wave frequency. For example, in the acute stage, in order to reduce the pressure on the ankle, the mattress hardness will be adjusted to a softer state, and the ambient light intensity will be reduced, and soothing low-frequency sound waves will be played to help users relax and promote sleep; in the transition period and stable period, these parameters will be gradually adjusted according to the recovery situation; the linkage coordination unit coordinates the working order between the sleep aid devices according to the parameters determined by the parameter matching unit. The adjustments affect each other, and the linkage coordination unit needs to arrange their working order reasonably to avoid conflicts. For example, first adjust the hardness of the mattress, then adjust the ambient light intensity, and finally adjust the sound wave frequency to ensure that the adjustments of each device are coordinated to create a comfortable sleeping environment for the user; the instruction execution unit sends control instructions to each sleep-aid device according to the working order of the linkage coordination unit. These instructions accurately control the sleep monitoring equipment and the ambient temperature control equipment to operate in coordination with the set parameters. For example, instructions are sent to the mattress control system to adjust the supporting structure inside the mattress and change the hardness of the mattress; instructions are sent to the lighting control system to adjust the ambient light intensity; instructions are sent to the audio playback device to play sound waves of a specific frequency. Through the coordinated work of various devices, a sleep-aid environment that is adapted to the foot and ankle recovery stage is provided to the user, thereby improving the sleep-aid effect and promoting foot and ankle recovery.

[0025] The user interaction module provides users with a visual operation interface for interacting with the system, which is convenient for users to customize various parameters and understand the system operation status and data in real time. The module consists of an interface display unit, an input processing unit, a data feedback unit and a setting storage unit. Among them, the interface display unit uses a graphical interface to display the real-time data collected by the ankle data acquisition module, the analysis results of the ankle status analysis module, the pressure distribution status of the foot pressure dynamic control module, and the equipment operation parameters of the sleep aid device linkage control module. For example, a graph is used to display the curve of plantar pressure change over time, a dynamic graph of the ankle joint movement trajectory, an intuitive identification of the ankle fatigue level and recovery stage, as well as the current values ​​of the mattress hardness, ambient light intensity and sound wave frequency, so that users can intuitively understand the real-time status of the ankle and the operation of the sleep aid device; the input processing unit receives the ankle recovery goal, pressure adjustment intensity, sleep preference and other parameter information entered by the user in the operation interface. After receiving the user input, the input processing unit performs format verification and validity on this information. The system verifies the authenticity of the data, and only the data that passes the verification will be accepted by the system for subsequent processing; the data feedback unit organizes the user operation records and data logs during the system operation process, including changes in ankle data, execution status of device control instructions, etc., into charts and feeds them back to the user. Through these charts, users can clearly see the impact of their operations on the system, as well as the data change trend during the ankle recovery process. For example, users can view the change curve of ankle fatigue level over a period of time to understand their own recovery status; or view the execution record of the control instructions of the sleep aid device to confirm whether the device has been adjusted according to their requirements; the storage unit is set to encrypt and store the user-defined parameter information, and automatically call it when the system runs next time, so that users do not need to reset the parameters every time they use the system, which improves the convenience of use. At the same time, the storage unit is set to back up the stored data regularly to prevent data loss. Even in the event of system failure or unexpected situation, the user's setting information can be protected to ensure the stability of the system and the security of user data.

[0026] To sum up, this embodiment demonstrates in detail the combination and working principle of each module of the sleep aid linkage system based on ankle recovery monitoring. The ankle data acquisition module obtains comprehensive biomechanical parameters, the ankle status analysis module uses multi-dimensional fusion analysis and neural network model to make accurate judgments, the foot pressure dynamic regulation module realizes real-time pressure adjustment, the sleep aid device linkage control module optimizes the sleeping environment according to the recovery stage, and the user interaction module provides convenient operation and feedback. The various modules work closely together to form an efficient and intelligent system. The system can dynamically adjust various parameters according to the individual differences of users and the ankle recovery process, which not only improves the accuracy of judging the ankle fatigue state and recovery stage, but also significantly improves the sleep aid effect and enhances the user experience. Example 2

[0027] like Figure 1 As shown, this embodiment describes in detail the specific steps for a patient to perform ankle recovery monitoring through a sleep aid linkage system based on ankle recovery monitoring. The specific steps are: The patient inputs information from the visual operation interface of the user interaction module; Enter your basic personal information, such as age, gender, height, weight, etc. This information will help the system analyze the foot and ankle condition more accurately; Customize your foot and ankle recovery goals, such as the desired level of recovery and recovery time; set the pressure adjustment intensity and choose the appropriate adjustment intensity based on your tolerance and comfort level; Enter sleep preference parameters, such as preferred sleeping position, desired ambient temperature, and preferred sleep-aiding sound types; The high-precision pressure sensor array in the foot and ankle data acquisition module continuously measures the pressure value of the sole of the foot at a fixed sampling interval, monitoring the pressure changes in different areas of the sole of the foot in real time when the patient is walking, standing or resting; The three-axis accelerometer and gyroscope in the inertial measurement unit work together to capture the three-dimensional motion trajectory of the ankle joint and obtain motion amplitude and frequency data; The temperature-impedance composite sensor simultaneously measures the skin surface temperature changes and impedance values ​​in the arch and heel areas to assess the local blood circulation status; The collected raw data will first be transmitted to the data preprocessing unit to remove non-periodic interference and motion artifacts to ensure the accuracy and reliability of the data, and then sent to the foot and ankle status analysis module via a wireless transmission protocol; After the foot and ankle state analysis module receives the data, the multi-source data fusion unit aligns the plantar pressure distribution, ankle joint motion trajectory, foot temperature, and skin impedance data according to timestamps and uses a weighted fusion strategy to generate a comprehensive foot and ankle state feature vector; The ankle recovery model calculation unit evaluates the ankle fatigue level based on the eigenvector. Meanwhile, the dynamic threshold judgment unit dynamically adjusts the fatigue threshold range based on the patient's historical fatigue data to determine whether the ankle is in an acute fatigue state or a stable fatigue state. The recovery phase decision unit compares the fatigue level value with the preset acute phase, transition phase, and stable phase threshold intervals to determine the current ankle recovery phase. If the value exceeds the upper limit of a certain phase threshold interval for three consecutive sampling cycles, the phase switching instruction is triggered. The instruction generation unit generates pressure control instructions based on the fatigue state and recovery stage, including information such as the airbag pressure target value, adjustment rate, and zone priority, and sends the instructions to the foot pressure dynamic control module; The pressure regulation execution unit of the foot pressure dynamic control module receives instructions, and multiple independently controllable micromotors drive the airbag array to adjust the inflation volume, changing the pressure distribution in different areas of the sole of the foot; The pressure feedback monitoring unit monitors the adjusted plantar pressure data in real time through pressure sensors distributed on the surface of the airbag, and feeds the data back to the control strategy generation unit; The control strategy generation unit combines the plantar pressure distribution standard and the patient's personalized needs to determine the current pressure regulation effect. If it does not meet expectations, it regenerates the pressure regulation instruction and sends it to the pressure regulation execution unit again to achieve closed-loop dynamic control of plantar pressure distribution. The device communication unit of the sleep aid device linkage control module obtains the results of the foot and ankle status analysis module, and simultaneously interacts with the sleep monitoring device and the ambient temperature control device to obtain relevant data; The parameter matching unit matches the appropriate operating parameters of the sleep aid device from the preset configuration table according to the foot and ankle recovery stage, such as mattress hardness, ambient light intensity, sound wave frequency, etc. The linkage coordination unit coordinates the working order of each sleep aid device according to the determined parameters to avoid conflicts between devices; The instruction execution unit sends control instructions to the sleep monitoring equipment and environmental temperature control equipment according to the working sequence, so that these devices can work together to create a sleeping environment suitable for the patient's foot and ankle recovery; Patients can view the real-time data collected by the foot and ankle data acquisition module, the analysis results of the foot and ankle status analysis module, the pressure distribution status of the foot pressure dynamic control module, and the equipment operating parameters of the sleep aid device linkage control module at any time through the interface display unit of the user interaction module; During the operation of the system, the data feedback unit will organize the patient's operation records, foot and ankle data changes, and the execution status of equipment control instructions into charts and provide feedback to the patient on the interface, so that the patient can understand the working status of the system and the recovery progress of his or her foot and ankle. If the patient is not satisfied with the system's performance or has new needs, he or she can modify the foot and ankle recovery goals, pressure adjustment intensity, sleep preferences and other parameters again through the input processing unit. The system will re-analyze and adjust according to the new parameters.

[0028] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. The sleep-aid linkage system based on foot and ankle recovery monitoring is characterized by: The system consists of: foot and ankle data acquisition module, foot and ankle status analysis module, foot pressure dynamic control module, sleep aid device linkage control module, and user interaction module; The ankle data acquisition module is used to collect biomechanical parameters of the ankle area in real time through multimodal sensors, including plantar pressure distribution, ankle joint motion trajectory, foot temperature and skin impedance data, and send the data to the ankle status analysis module through a wireless transmission protocol; The ankle status analysis module is used to perform multi-dimensional fusion analysis on the collected data, combine the preset ankle recovery model and fatigue threshold, determine the fatigue state and recovery stage of the user's ankle, and generate dynamic control instructions; The foot pressure dynamic control module is used to adjust the foot pressure distribution in real time through the airbag array driven by the micromotor according to the instructions of the analysis module; The sleep aid device linkage control module is used to exchange data between the foot and ankle status analysis results and the sleep monitoring device and the ambient temperature control device, and dynamically adjust the operating parameters of the sleep aid device according to the foot and ankle recovery stage, including mattress hardness, ambient light intensity, and sound wave frequency; The user interaction module is used to provide a visual operation interface, support users to customize foot and ankle recovery goals, pressure adjustment intensity and sleep preference parameters, and provide real-time feedback on system operation status and data logs.

2. The sleep-aiding linkage system based on foot and ankle recovery monitoring according to claim 1 is characterized in that: The ankle data acquisition module includes: A pressure sensor array, embedded in the foot pad in the form of a flexible circuit board, measures the pressure value of the sole of the foot at a fixed sampling interval; The inertial measurement unit, integrated into the ankle strap, captures the three-dimensional motion trajectory of the ankle joint through a three-axis accelerometer and gyroscope to obtain the motion amplitude and frequency; Temperature-impedance composite sensors are distributed in the arch and heel areas to simultaneously measure skin surface temperature changes and impedance values ​​to assess local blood circulation status; The data preprocessing unit performs noise reduction on the original signal, removes non-periodic interference, and eliminates motion artifacts.

3. The sleep-aiding linkage system based on foot and ankle recovery monitoring according to claim 1 is characterized in that: The foot and ankle status analysis module includes: The multi-source data fusion unit is used to align the plantar pressure distribution, ankle joint motion trajectory, foot temperature, and skin impedance data by timestamp, and uses a weighted fusion strategy to generate a comprehensive foot and ankle state feature vector, where the pressure data accounts for 40%, the motion data accounts for 30%, and the temperature and impedance each account for 15%; The ankle recovery model calculation unit has a built-in neural network model trained based on biomechanical characteristics. After inputting the feature vector, it outputs the current fatigue level value. The dynamic threshold judgment unit dynamically adjusts the fatigue threshold range based on the user's historical data. When the fatigue score exceeds 120% of the threshold in three consecutive samplings, the foot and ankle are judged to be in an acute fatigue state. Otherwise, the foot and ankle are judged to be in a stable fatigue state. The recovery phase decision unit compares the fatigue level value with the preset acute phase, transition phase, and stable phase threshold intervals to determine the recovery phase, and triggers the phase switching instruction when the value exceeds the upper limit of the threshold interval for three consecutive sampling periods; The instruction generation unit maps the fatigue state and recovery stage into pressure control instructions, including the airbag pressure target value, adjustment rate and partition priority.

4. The sleep-aiding linkage system based on foot and ankle recovery monitoring according to claim 1 is characterized in that: The foot pressure dynamic control module includes: The pressure regulation execution unit is composed of multiple independently controllable micro-motor-driven airbag arrays. The micro-motor adjusts the airbag inflation volume according to the pressure control instructions of the foot and ankle status analysis module, thereby changing the pressure distribution in different areas of the sole of the foot; The pressure feedback monitoring unit includes pressure sensors distributed on the surface of the airbag, which monitors the adjusted plantar pressure data in real time and feeds the data back to the control strategy generation unit; The control strategy generation unit receives data from the pressure feedback monitoring unit and instructions from the foot and ankle status analysis module, and combines the plantar pressure distribution standard and the user's personalized needs to judge the expected effect of the current pressure adjustment. When the expected effect does not meet the user's needs, it regenerates the pressure adjustment instruction and sends it to the pressure adjustment execution unit.

5. The sleep-aiding linkage system based on foot and ankle recovery monitoring according to claim 1 is characterized in that: The sleep aid device linkage control module includes: The device communication unit uses a unified communication protocol to establish a data exchange channel with the sleep monitoring device and the environmental temperature control device, receiving the user's sleep data collected by the sleep monitoring device and the current operating parameters of the environmental temperature control device, and at the same time transmitting the results of the foot and ankle status analysis module to each device; The parameter matching unit stores the sleep aid device parameter configuration table corresponding to different stages of foot and ankle recovery, matches the recovery stage results obtained by the foot and ankle status analysis module with the configuration table, and determines the device operating parameters such as mattress hardness, ambient light intensity, and sound wave frequency; The linkage coordination unit coordinates the working order of each sleep aid device according to the parameters determined by the parameter matching unit; The instruction execution unit sends control instructions to each sleep aid device according to the working order of the linkage coordination unit, so that the sleep monitoring equipment and environmental temperature control equipment can operate in coordination according to the set parameters.

6. The sleep-aiding linkage system based on foot and ankle recovery monitoring according to claim 1 is characterized in that: The user interaction module includes: An interface display unit displays the real-time data collected by the foot and ankle data acquisition module, the analysis results of the foot and ankle status analysis module, the pressure distribution status of the foot pressure dynamic control module, and the device operating parameters of the sleep aid device linkage control module in a graphical interface; An input processing unit receives parameter information of foot and ankle recovery goals, pressure adjustment intensity, and sleep preferences input by the user on the operation interface; The data feedback unit organizes user operation records and data logs during system operation, including changes in foot and ankle data and the execution status of device control instructions, into charts and provides feedback to users; Set up a storage unit to encrypt and store user-defined parameter information, which will be automatically called the next time the system runs. At the same time, the stored data will be backed up regularly to prevent data loss.

7. The sleep-aiding linkage system based on foot and ankle recovery monitoring according to claim 3 is characterized in that: The implementation process of the neural network model of the ankle recovery model calculation unit is as follows: Input layer: The input layer receives the comprehensive state feature vector of the foot and ankle ,in, And the corresponding four-dimensional data of plantar pressure distribution, ankle joint motion trajectory, foot temperature and skin impedance are normalized to obtain a four-dimensional vector, that is, ,in represents the normalized pressure data, It's sports data. is the temperature data, is the impedance data; The first hidden layer: performs a fully connected feature transformation on the data of the input layer, performs a weighted summation on the input data and introduces a nonlinear transformation to extract representative features. The calculation formula of the fully connected feature transformation is , the weight matrix It is the connection relationship and weight size between the input layer and the first hidden layer. That is, the matrix has 8 rows and 4 columns. The 4 neurons corresponding to the four types of data in the input layer are connected to the 8 neurons preset in the first hidden layer respectively. Each element in the matrix Represents the input layer The neurons in the first hidden layer The weight of each neuron when transmitting a signal, Indicates the sequence number of the first hidden layer neurons, from 1 to 8, Indicates the sequence number of the input layer neurons, from 1 to 4, bias is a constant term set separately for each neuron in the first hidden layer. The first hidden layer corresponds to neurons, is the activation function and , in the calculation process, when When the result is less than 0, the output of the neuron is 0, and the first hidden layer outputs an 8-dimensional vector ; The second hidden layer processes the output of the first hidden layer and performs weighted transformation by introducing the attention mechanism to obtain feature representation. The attention mechanism calculates the attention weight matrix ,in, Is the attention weight matrix, which is used to measure the importance of each feature of the first hidden layer output to the calculation of the second hidden layer neuron output. The elements in the matrix are Represents the first hidden layer The neurons in the second hidden layer The softmax function is used to influence the attention weight of each neuron. The calculation result is converted into a probability distribution form, so that the obtained attention weight matrix Each element in takes a value between 0 and 1, and the sum of all elements is 1, that is, ,in Indicates the effect of the first hidden layer output on the second hidden layer The attention weights of neurons are used to calculate the output of the second hidden layer. ,in is the weight matrix of the second hidden layer, which is responsible for converting the 8-dimensional output of the first hidden layer into the 4-dimensional input of the second hidden layer. Represents the first hidden layer neurons in the second hidden layer The weights of the connections between neurons, is the bias of the second hidden layer, each The second hidden layer corresponds to Neurons, symbol Represents the attention weight matrix and In this way, the attention weight is used to weight the data and obtain the second hidden layer. ; Output layer: Calculate the final ankle fatigue level value based on the features extracted by the hidden layer ,in, is the weight vector and , Indicates that the weight vector Transpose and Perform dot product operation, weight vector Each element in The second hidden layer The contribution weight of each neuron to the final fatigue level output determines the degree of influence of the output of the corresponding neuron in the second hidden layer on the final result. Is the bias value of the output layer, which is used to adjust the calculation results and is a set of real numbers, the calculated The value is mapped to the interval [0, 10] to represent the fatigue state of the ankle.

8. The sleep-aiding linkage system based on foot and ankle recovery monitoring according to claim 3 is characterized in that: The dynamic threshold judgment unit calculates the fatigue level value according to the neural network model and combines it with the dynamically adjusted threshold value to set the initial fatigue threshold value as , record the user's historical fatigue data series ,in Indicates the The fatigue value of the sampling times is used to calculate the mean of historical data and standard deviation , the dynamically adjusted threshold ,in, and is the adjustment coefficient, Used to control the influence of the mean on the threshold. It is used to control the influence of the standard deviation on the threshold, and , , when the fatigue score is All exceeded When the ankle is in an acute fatigue state, it is determined that the ankle is in a stable fatigue state; otherwise, the ankle is determined to be in a stable fatigue state; The recovery stage decision unit will calculate the fatigue level value Compared with the preset acute phase, transition phase, and stable phase threshold intervals, when the fatigue level value exceeds the upper limit of the threshold interval for three consecutive sampling cycles, the stage switching instruction is triggered. The acute phase threshold interval is [0, 3], the transition phase threshold interval is (3, 6], and the stable phase threshold interval is (6, 10].

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