Early warning method and device for spine orthosis, electronic equipment and storage medium

Abstract

The invention discloses an early warning method and device of a spine orthosis, electronic equipment and a storage medium, the method is applied to an early warning system of the spine orthosis, the system comprises a local monitoring end and a remote analysis end, and the early warning method comprises the following steps: when it is detected that a target spine orthosis is completely worn, sending the early warning information to the remote analysis end; the local monitoring end collects initial pressure data corresponding to the target position through the flexible and elastic electronic skin module; the local monitoring end processes the initial pressure data to obtain target pressure data, and sends the target pressure data to a remote analysis end through remote communication; and the remote analysis end determines a data analysis result of the target spinal orthosis according to the target pressure data, and generates an early warning prompt according to the data analysis result. The initial pressure data of the target position is collected through the flexible and elastic electronic skin module, and the early warning prompt is generated according to the pressure data, so that the early warning information of the spine orthosis can be quickly obtained, false alarm and missed alarm are effectively avoided, and accurate monitoring of the treatment effect is ensured.

Description

Technical Field

[0001] This invention relates to the field of spinal orthopedic equipment technology, and in particular to a method, device, electronic equipment and storage medium for early warning of a spinal orthopedic device. Background Technology

[0002] Adolescent scoliosis is a common skeletal developmental abnormality. Orthotic therapy is an important non-surgical treatment method. However, the corrective effect of orthotics depends on the wearing time and tightness. In actual use, patients often fail to wear the orthotics as required due to discomfort or negligence. Parents and doctors cannot know the wearing status of the orthotics in time. Therefore, it is necessary to monitor the wearing status of the orthotics.

[0003] However, existing monitoring methods have limited pressure detection points, which cannot accurately reflect the overall fit of the device. Furthermore, the pressure threshold of the orthodontic device cannot be automatically adjusted according to the patient's treatment progress, resulting in a mismatch between treatment stages and affecting the accurate judgment of the wearing status. Summary of the Invention

[0004] This invention provides a method, device, electronic device, and storage medium for early warning of spinal orthotics. It collects initial pressure data at the target location through a flexible electronic skin module and generates early warning prompts based on the pressure data. This allows for rapid acquisition of early warning information from the spinal orthotics, effectively avoiding false alarms and missed alarms, and ensuring accurate monitoring of treatment effects.

[0005] According to one aspect of the present invention, a method for early warning of a spinal orthosis is provided, wherein the method is applied to an early warning system for a spinal orthosis, the system comprising a local monitoring terminal and a remote analysis terminal, including:

[0006] Once the target spinal orthosis is detected to be worn, the local monitoring unit collects initial pressure data corresponding to the target position through a flexible electronic skin module.

[0007] The local monitoring terminal processes the initial pressure data to obtain the target pressure data, and then sends the target pressure data to the remote analysis terminal via remote communication;

[0008] The remote analysis terminal determines the data analysis results of the target spinal orthosis based on the target pressure data, and generates an early warning prompt based on the data analysis results.

[0009] According to another aspect of the present invention, a warning device for a spinal orthosis is provided, wherein the device is disposed in a warning system for the spinal orthosis, the system comprising a local monitoring terminal and a remote analysis terminal, including:

[0010] The data acquisition module is used to collect initial pressure data corresponding to the target position through the flexible electronic skin module when the target spinal orthosis is detected to be worn.

[0011] The data processing module is used to process the initial pressure data through the local monitoring terminal to obtain the target pressure data, and to send the target pressure data to the remote analysis terminal through remote communication;

[0012] The data analysis module is used to determine the data analysis results of the target spinal orthosis based on the target pressure data through the remote analysis terminal, and to generate early warning prompts based on the data analysis results.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the warning method for the spinal orthosis according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the early warning method of the spinal orthosis according to any embodiment of the present invention.

[0018] The technical solution of this invention involves a local monitoring terminal collecting initial pressure data corresponding to the target location via a flexible electronic skin module after the target spinal orthosis is detected as being worn. The local monitoring terminal processes this initial pressure data to obtain target pressure data and transmits it to a remote analysis terminal via remote communication. The remote analysis terminal determines the data analysis result related to the target spinal orthosis based on the target pressure data and generates an early warning prompt based on the data analysis result. Based on this technical solution, by collecting initial pressure data at the target location via a flexible electronic skin module and generating an early warning prompt based on the pressure data, early warning information about the spinal orthosis can be quickly obtained, effectively avoiding false alarms and missed alarms, and ensuring accurate monitoring of treatment effects.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0021] Figure 1 This is a flowchart of an early warning method for a spinal orthosis provided in an embodiment of the present invention;

[0022] Figure 2 This is a system architecture diagram of the early warning system for the spinal orthosis provided in this embodiment of the invention;

[0023] Figure 3 This is a schematic diagram of the structure of the local monitoring terminal provided in an embodiment of the present invention;

[0024] Figure 4 This is a pressure thermogram provided in an embodiment of the present invention;

[0025] Figure 5 This is a flowchart of an early warning method for a spinal orthosis provided in an embodiment of the present invention;

[0026] Figure 6 This is a schematic diagram of the structure of an early warning device for a spinal orthosis provided in an embodiment of the present invention;

[0027] Figure 7 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Figure 1 This is a flowchart illustrating an early warning method for a spinal orthosis provided in an embodiment of the present invention. This embodiment is applicable to situations where the wearing status of the spinal orthosis is monitored in real time and an alert is issued during the wearing process. The method is applied to an early warning system for spinal orthosis, which includes a local monitoring terminal and a remote analysis terminal. This method can be executed by an early warning device for the spinal orthosis, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method specifically includes the following steps:

[0031] S110. When the target spinal orthosis is detected to be worn, the local monitoring terminal collects the initial pressure data corresponding to the target position through the flexible electronic skin module.

[0032] The target spinal orthosis can be a wearable medical device used to correct the physiological curvature of the spine and help maintain normal spinal posture. The local monitoring terminal can be understood as an embedded data acquisition and processing terminal deployed on the spinal orthosis, capable of monitoring the orthosis's wearing status and related biomechanical data in real time. The flexible electronic skin module can be a pressure sensing component with flexible fit characteristics. The target location can be the area where the orthosis fits against the key stress area of ​​the human spine. The initial pressure data can be understood as the pressure distribution data at the target location.

[0033] The early warning system of the spinal orthosis in this invention is as follows: Figure 2As shown, the early warning system for the spinal orthosis includes a local monitoring terminal and a remote analysis terminal. The local monitoring terminal includes: a multi-channel flexible electronic skin module: used to collect pressure data from multiple areas when the patient wears the orthosis; a power management module: this system is powered by a rechargeable lithium battery, which adjusts the voltage provided by the lithium battery to a suitable voltage through boost and buck modules (LT3471 and TPS7A2033PDBVR, etc.) to provide a stable power output for the system. It features a manual switch button and optimizes battery life through low-power design and dynamic power management mechanisms, allowing the device to operate for extended periods and avoiding frequent charging and battery swapping; and a signal acquisition module: this module, based on the STM32G431 microprocessor, converts the pressure generated by the flexible electronic skin module into a negative voltage via a multiplexer, amplifies it in reverse through an analog operational amplifier circuit, and inputs it to the microprocessor. Its built-in ADC and DMA interfaces perform multi-channel data acquisition, and filter and denoise the data to ensure the accuracy and stability of the acquired data. The processed data is then wirelessly transmitted to the remote analysis terminal via a 4G communication module. The remote analysis terminal can display the data transmitted by the 4G module in the form of a heat map on the user terminal. When the received pressure value exceeds the threshold of the adaptive pressure threshold adjustment algorithm or when the pressure centroid shifts, the monitoring software system will automatically bring up a prompt box to remind the patient that the device is too tight or not properly worn.

[0034] Specifically, upon detecting that the target spinal orthosis has been successfully worn, the local monitoring terminal collects initial pressure data corresponding to the target location via a flexible electronic skin module. For example, the wear detection sensor built into the target spinal orthosis identifies the wearing status in real time. When the sensor detects that the orthosis has been successfully worn, it sends a start command to the local monitoring terminal. Upon receiving the command, the local monitoring terminal controls the flexible electronic skin module to collect pressure values ​​at each sensor point at the target location according to a preset sampling frequency, forming the raw dataset of initial pressure data.

[0035] It should be noted that the flexible electronic skin module in this embodiment of the invention utilizes flexible multi-channel pressure acquisition technology, including a flexible sensor array: the flexible material has bendable and conformable properties, adapting to the back curvature of patients of different body types, avoiding measurement errors caused by rigid sensors failing to make close contact. It achieves multi-point pressure acquisition of key areas of the orthosis, exhibiting good conformability and response sensitivity, as well as multi-channel synchronous sampling: pressure data is acquired in real time through multi-channel parallel sampling and fused by the main control chip to provide complete pressure distribution information.

[0036] It should be noted that the local monitoring terminal of this invention, such as Figure 3As shown, the overall dimensions of the outer shell are 65mm × 55mm × 26mm, making it compact and easy to install on scoliosis orthotics without affecting the patient's normal activities. The shell's compact design provides good protection and ease of maintenance. Figure 3 As shown, the outer shell consists of a top cover (1), a battery cover (2), and a base (3). The base (3) is fixed to a designated position on the outer shell of the scoliosis orthosis by strong adhesive bonding to provide stable support for the internal circuit modules. The core circuit units are all integrated and installed inside the base, with simple wiring and reasonable layout. The top cover (1) and the base (3) are connected by a snap-fit ​​structure. After the internal circuit is installed, the top cover and the base are pressed together to complete the assembly; during disassembly, the snap-fit ​​can be separated by simply pressing the snap-fit, which is convenient for maintenance and replacement of internal components and achieves tool-free quick disassembly and assembly. At the connection between the top cover (1) and the base (3), an opening (4) is reserved for the signal connection between the flexible electronic skin sensor and the main circuit. This opening allows the sensor's FPC flexible cable or signal line to pass through and is sealed with a silicone pad or flexible seal to ensure the reliability of signal transmission and maintain the overall sealing and protection performance of the outer shell. The battery cover (2) is located on one side of the top cover and adopts an independent snap-fit ​​structure design, which can be opened separately. When the battery is depleted, users can easily replace it by simply pressing the clips to open the battery cover without disassembling the entire casing. This design significantly improves maintainability and ease of use. The casing is compact, small in size, and easy to install, providing excellent protection and maintainability, ensuring reliable operation during long-term wear and in mobile environments.

[0037] S120: The local monitoring terminal processes the initial pressure data to obtain the target pressure data, and sends the target pressure data to the remote analysis terminal via remote communication.

[0038] The target pressure data can be standardized pressure data obtained by preprocessing and feature extraction of the initial pressure data. Remote communication can be a communication method for data transmission between the local monitoring terminal and the remote analysis terminal. The remote analysis terminal can be understood as a terminal device with data receiving and analysis functions.

[0039] Specifically, the local monitoring terminal processes the initial pressure data to obtain the target pressure data, and then sends the target pressure data to the remote analysis terminal via remote communication. For example, the local monitoring terminal preprocesses the initial pressure data to obtain the target pressure data. After completing the data processing, the local monitoring terminal establishes a communication connection with the remote analysis terminal through a remote communication protocol, encapsulates the target pressure data, and then sends the encapsulated data to the remote analysis terminal.

[0040] Based on the above technical solution, the local monitoring terminal processes the initial pressure data to obtain the target pressure data, including: collecting a preset number of sample signals each time, removing the maximum and minimum values ​​and calculating the arithmetic mean; inputting the arithmetic mean into the EMA filtering algorithm, setting a preset attenuation factor for smoothing, and obtaining the pressure data.

[0041] The sample signal is the raw pressure sensing signal collected by the flexible electronic skin module, which serves as the basic data source for pressure data processing. The preset quantity is the pre-set number of samples collected in a single session to ensure the effectiveness of data statistics. The maximum value is the largest value in a single sample signal collection, and the minimum value is the smallest value in a single sample signal collection; both are used to remove extreme interference data. The arithmetic mean is the average value of the remaining sample signals after removing extreme values, used to initially reduce random interference. The EMA filtering algorithm is an exponential moving average filtering algorithm used to smooth time-series data to reduce fluctuations. The preset attenuation factor is a parameter used in the EMA filtering algorithm to adjust the weight allocation, used to balance data smoothness and response speed. Smoothing is a process of reducing data fluctuations through filtering algorithms to improve the stability of pressure data. The pressure data is the standardized pressure value obtained after the above processing, used to accurately reflect the pressure state of the orthosis in contact with the human body.

[0042] The sample acquisition process is initiated, and sample signals are acquired according to the preset number. Extreme value filtering is performed on the acquired sample signals, removing the maximum and minimum values. An arithmetic mean is calculated based on the remaining sample signals. The obtained arithmetic mean is input into the EMA filtering algorithm module, with preset attenuation factor parameters configured. The EMA filtering algorithm smooths the arithmetic mean, reducing data fluctuation interference. After processing, pressure data is output. The output data is validated to ensure it meets the requirements for subsequent analysis. After successful validation, the data is stored in the local cache.

[0043] Based on the above technical solution, the pressure data is processed by the signal acquisition module, including: providing excitation voltage to the flexible electronic skin module through a multiplexer and switching the acquisition channel; amplifying the pressure signal in reverse through an analog operational amplifier and then performing hardware filtering through an RC low-pass filter; acquiring the filtered signal through the ADC interface of the microprocessor and processing it with a multi-level software filtering algorithm to obtain the pressure data; and transmitting the pressure data to the storage unit through the DMA interface of the microprocessor.

[0044] Among these, a multiplexer can be a device with signal switching and voltage output functions. The excitation voltage can be understood as the voltage signal driving the flexible electronic skin module. The acquisition channel is the signal path for transmitting pressure signals. An analog operational amplifier can be an analog device used for signal amplification. Inverting amplification can be understood as a method of amplifying the amplitude of the pressure signal and reversing its phase. An RC low-pass filter can be a filter device composed of resistors and capacitors, used to filter out high-frequency interference in the pressure signal. Hardware filtering can be understood as filtering processing implemented through hardware circuits. An ADC interface can be the interface in a microprocessor that implements analog-to-digital signal conversion. A multi-level software filtering algorithm can be understood as an algorithm that combines multiple software filtering methods. A DMA interface can be the interface in a microprocessor that implements direct memory access. A storage unit is a hardware unit used to store data.

[0045] Specifically, the multiplexer is connected to the power supply and outputs an excitation voltage to the flexible electronic skin module according to preset parameters. Simultaneously, it switches the acquisition channel according to preset logic. The pressure signal output from the flexible electronic skin module is connected to an analog operational amplifier, amplified in reverse, and then transmitted to an RC low-pass filter for hardware filtering. The microprocessor acquires the filtered analog pressure signal through an ADC interface, converts it into a digital signal, and inputs it to a multi-stage software filtering algorithm module for processing to generate pressure data. The microprocessor configures the DMA interface parameters, establishes a connection between the DMA channel and the storage unit, and directly transmits the pressure data to the storage unit through the DMA interface. During transmission, the microprocessor releases bus control to ensure transmission efficiency. After transmission is complete, the DMA interface sends a completion signal, and the microprocessor regains control.

[0046] For example, the signal acquisition module includes an STM32G431 microprocessor, a DG9408 multiplexer, and a TLV9154 analog operational amplifier for efficiently acquiring pressure data from a flexible electronic skin sensor. The DG9408 multiplexer's outputs (1-6) are connected to the inputs of the flexible electronic skin via an FPC cable, providing a -3V excitation voltage to the sensor. Simultaneously, its control pins (A0-A2) are connected to three GPIO pins of the STM32G431 microprocessor to control the switching of signal acquisition channels. The TLV9154 analog operational amplifier's negative input is connected to the output of the flexible electronic skin, amplifying the acquired pressure signal in reverse via an FPC cable. The amplified signal is then filtered by an RC low-pass filter and sent to the STM32G431's ADC pin for further processing to avoid high-frequency noise interference. Data Acquisition and Filtering: The STM32G431 microprocessor uses DMA (Direct Memory Access) technology to transfer acquired data to memory, thereby freeing up CPU resources and improving data transfer efficiency. During data acquisition, multi-stage filtering is performed using truncated average filtering and EMA (Exponentially Weighted Moving Average) filtering: Truncated average filtering: 20 data samples are acquired each time, the maximum and minimum values ​​are removed, and the arithmetic mean of the remaining 18 data points is calculated; EMA filtering: The truncated average data is passed to the EMA filter, using an impression factor α=0.3 to balance data smoothness and response speed. In this design, to match the number of sensors on the two flexible electronic skins, two DG9408 analog multiplexers and three TLV9154 analog operational amplifiers are used to ensure simultaneous processing of multiple channel signals and improve the efficiency and accuracy of data acquisition.

[0047] Based on the above technical solution, the target pressure data is sent to the remote analysis terminal via remote communication, including: controlling the start and stop of the 4G communication module through a microprocessor, and turning off the 4G communication module during non-upload periods; encapsulating the processed pressure data into JSON data according to a preset format; uploading the JSON data to the remote analysis terminal via the MQTT protocol; if the upload fails, caching the JSON data and performing a preset number of retries.

[0048] The 4G communication module can be a communication component supporting 4G network wireless transmission, used to realize data interaction between the local monitoring terminal and the remote analysis terminal. Non-upload periods can be understood as pre-defined time periods during which data uploads are not required. The preset format can be a predefined structured data organization specification. JSON data can be standardized data encoded in JSON format. The MQTT protocol can be understood as a lightweight message transmission protocol for IoT scenarios. The cache can be a temporary data storage space built into the local monitoring terminal. The preset number of attempts is the pre-defined maximum number of upload retries. The retry operation can be understood as the process of re-initiating an upload after a data upload failure.

[0049] Specifically, the microprocessor, based on a preset upload time period configuration, outputs a control signal to shut down the 4G communication module during non-upload periods; upon entering the upload period, it sends a start command to activate the 4G communication module, completing network registration and connection. It retrieves processed stress data, defines fields, categorizes and structures the data according to a preset format, and encapsulates it as JSON data. It configures the MQTT protocol connection parameters, establishes a communication link with the remote analysis end, and initiates an upload request with the JSON data as the message body. It monitors upload feedback signals in real time; if no confirmation command is received from the remote analysis end, the upload is considered failed, and the JSON data is written to the local cache space. A retry mechanism is initiated, repeatedly initiating upload requests a preset number of times. Before each retry, the network connection status and cached data integrity are verified, and the upload operation is performed after re-establishing the MQTT connection. If the upload still fails after the preset number of retries, the cached data is preserved, and the upload is automatically triggered again in the next upload cycle.

[0050] For example, the 4G communication module uses the Air780E module, responsible for uploading the collected pressure data to the monitoring system. This module communicates with the STM32G431 microprocessor via serial port pass-through. The TXD pin is connected to the RXD pin of the STM32G431; the RXD pin is connected to the TXD pin of the STM32G431; the EN enable pin is connected to a GPIO port of the STM32G431 to control the module's start and stop. In low-power mode, the 4G communication module is turned off, with power consumption below 0.5mA, effectively reducing energy consumption. The module is only activated upon receiving a command from the STM32G431, ensuring ultra-low power consumption in standby mode. When the 4G communication module receives data from the STM32G431, it first converts the data to JSON format and then uploads it to the cloud for monitoring using the MQTT protocol. Both parents and doctors can view the patient's wearing status and pressure changes in real time via the app, ensuring real-time data monitoring and remote assessment during treatment.

[0051] S130: The remote analysis terminal determines the data analysis results of the target spinal orthosis based on the target pressure data, and generates early warning prompts based on the data analysis results.

[0052] The data analysis results can be assessments of the fit and pressure distribution of the target spinal orthosis obtained by remotely analyzing the target pressure data. Warning messages can be generated based on abnormal data analysis results to inform relevant personnel of any abnormalities during orthosis use.

[0053] Specifically, the remote analysis terminal determines the data analysis results of the target spinal orthosis based on the target pressure data and generates early warning prompts accordingly. For example, it can call a preset data analysis model, input the target pressure data for calculation, analyze the uniformity of pressure distribution between the orthosis and the human body, whether the pressure value is within the appropriate range, and output the data analysis results. The data analysis results are compared with preset normal threshold ranges. If the results are found to exceed the threshold range, it is judged as an abnormal state, triggering the early warning generation process. According to preset early warning level rules, the corresponding early warning type and prompt content are matched to generate standardized early warning prompts. The early warning prompts are associated with the corresponding data analysis results and target pressure data, sent to designated terminals through preset push channels, and simultaneously stored in a remote database to form early warning records.

[0054] Based on the above technical solution, the remote analysis terminal determines the data analysis results of the target spinal orthosis according to the target pressure data, including: obtaining the correction parameters corresponding to the current correction stage, and determining the pressure data threshold corresponding to the current correction stage based on the correction parameters and the target pressure data; and determining the pressure data analysis results based on the pressure data threshold and the target pressure data.

[0055] The correction parameters include minimum pressure value, curve steepness parameter, and inflection point time parameter. The current correction stage can be a specific stage in the spinal correction process, divided according to rehabilitation progress, used to match appropriate correction assessment standards. Correction parameters can be understood as pressure assessment benchmark parameters adapted to different correction stages. The minimum pressure value can be the lowest effective pressure benchmark value that needs to be ensured during the correction process. The curve steepness parameter can be a parameter characterizing the rate of change of the pressure threshold over time, used to adapt to the changing correction intensity requirements at different stages. The inflection point time parameter can be the time node parameter at which the trend of pressure threshold change reverses. The pressure data threshold can be understood as the pressure qualification judgment benchmark corresponding to the current correction stage, used to distinguish between normal and abnormal states of the target pressure data. The pressure data analysis result can be the assessment conclusion after comparing the target pressure data with the corresponding threshold, used to determine whether the current correction pressure is appropriate.

[0056] Specifically, the process involves acquiring the correction parameters corresponding to the current correction stage, and determining the corresponding pressure data threshold based on these parameters and target pressure data. The pressure data analysis results are then determined based on the pressure data threshold and target pressure data. For example, correction parameters corresponding to the current correction stage are retrieved from a remote database, including minimum pressure value, curve steepness parameters, and inflection point time parameters. These correction parameters are input into a preset threshold calculation model, along with the time information corresponding to the target pressure data. The model calculates the pressure data threshold for the current correction stage. The target pressure data is compared with the calculated pressure data threshold for each time period to determine if the target pressure data for each period falls within the threshold range. The degree and duration of pressure data deviation from the threshold are also analyzed. Based on the comparison results, pressure data analysis results are generated, clarifying the current alignment status of the correction pressure, marking abnormality types and time periods, forming a complete analysis conclusion, and storing the corresponding correction parameters and target pressure data.

[0057] For example, the early warning function integrates an adaptive pressure threshold adjustment algorithm and a pressure centroid offset calculation function. The adaptive pressure threshold adjustment algorithm uses the Sigmoid function, and its overall expression is: ;in It is time The pressure value at that time. It is the initial pressure value, that is, the pressure applied at the beginning of treatment. It is the minimum pressure value at the end of treatment, representing the minimum pressure required to maintain correction. It refers to the steepness of the curve, which can be an empirical value set based on historical treatment data to determine the rate of pressure reduction. This is the inflection point of the curve, representing the moment when the spine begins to effectively adapt to the pressure. When a patient first wears the device, the doctor uses X-rays to determine the severity of the patient's scoliosis and provides a conservative treatment timeframe. Based on experience, a k-value for a curve is given, and the first pressure value is collected based on the orthotics with the installed flexible electronic skin. , The pressure value is generally taken as that of a normal person wearing it. As treatment progresses, The pressure value will decrease between these values. It will adjust the pressure threshold after the orthodontic device is worn to avoid generating incorrect prompts due to a fixed threshold.

[0058] Based on the above technical solution, the remote analysis terminal determines the data analysis results of the target spinal orthosis according to the target pressure data, including: determining the sensor pressure value and sensor coordinate position of each sensor in the flexible electronic skin module according to the target pressure data; determining the pressure centroid coordinates based on the sensor pressure value and sensor coordinate position; and determining the coordinate position analysis results based on the pressure centroid coordinates and historical pressure centroid coordinates.

[0059] The sensor pressure value can be the pressure data sensed by a single sensor in the flexible electronic skin module, used to characterize the pressure state at the corresponding contact point. The sensor coordinate position can be understood as the preset spatial coordinates of each sensor in the flexible electronic skin module on the orthosis. The pressure centroid coordinates can be the coordinates of the pressure distribution center calculated based on the pressure values ​​and coordinate positions of all sensors. The historical pressure centroid coordinates can be pressure centroid coordinate data recorded at different time periods, used to compare and analyze the changing trend of the pressure distribution center. The coordinate position analysis result can be understood as the evaluation conclusion after comparing the current pressure centroid coordinates with the historical pressure centroid coordinates.

[0060] Specifically, based on the target pressure data, the sensor pressure value and sensor coordinate position of each sensor in the flexible electronic skin module are determined. The pressure centroid coordinates are then determined based on the sensor pressure values ​​and sensor coordinate positions, and the coordinate position analysis results are derived based on the pressure centroid coordinates and historical pressure centroid coordinates. For example, the sensor configuration information of the flexible electronic skin module is retrieved to determine the identifier and corresponding sensor coordinate position of each sensor. The target pressure data is then matched according to the sensor identifier to determine the sensor pressure value corresponding to each sensor. Based on the sensor pressure values ​​and sensor coordinate positions of all sensors, a centroid calculation algorithm is used to calculate the current pressure centroid coordinates. Historical pressure centroid coordinates within the same correction stage are retrieved from the database and organized into a historical coordinate sequence according to the time dimension. The current pressure centroid coordinates are compared with the historical pressure centroid coordinate sequence to analyze the offset direction, offset range, and change pattern of the coordinate positions, and the coordinate position analysis results are generated based on the comparison results.

[0061] For example, the overall expression for the pressure centroid offset calculation function is as follows: , The coordinates of the pressure centroid are in This represents the pressure value of the sensor in the i-th row and j-th column. It is the horizontal coordinate of the j-th column. This is the numerical coordinate of the i-th row. After the patient wears the orthosis correctly, the monitoring software will record the centroid coordinates of each pressure measurement. If the current centroid coordinates deviate significantly from the previously recorded centroid coordinates, the software will send a "Not properly worn" message to the parents, reminding them to check the patient's fit.

[0062] Based on the above technical solution, early warning prompts are generated according to the data analysis results, including: generating a pressure data early warning prompt when the target pressure data exceeds the pressure data threshold; and / or generating a wearing position offset early warning prompt when the pressure centroid coordinate offset exceeds the preset range.

[0063] The pressure data warning can be a notification generated when the target pressure data exceeds a corresponding threshold. The wearing position offset warning can be a notification generated when the pressure centroid coordinate offset exceeds the allowable range. The preset range can be understood as a pre-defined normal fluctuation range of the pressure centroid coordinate.

[0064] Specifically, the remote analysis terminal retrieves the target pressure data and the corresponding pressure data threshold, as well as the pressure centroid coordinates and a preset range. It compares the target pressure data with the pressure data threshold to determine if any values ​​exceed the threshold; it also compares the pressure centroid coordinates with the preset range to determine if the offset exceeds the allowable boundary. If the target pressure data exceeds the pressure data threshold, a pressure data warning prompt generation process is triggered, specifying the abnormal pressure type and associated data according to a preset format. If the pressure centroid coordinate offset exceeds the preset range, a wearing position offset warning prompt generation process is triggered, indicating the offset direction and degree. If both abnormalities exist simultaneously, two types of warning prompts are generated concurrently. The generated warning prompts are associated with the corresponding target pressure data, pressure centroid coordinates, and data analysis results, stored in a remote database to form a warning record, and simultaneously sent to a designated terminal through a preset push channel.

[0065] Based on the above technical solution, after generating an early warning prompt based on the data analysis results, the process includes: identifying the parent monitoring terminal and doctor monitoring terminal corresponding to the target spinal orthotics; generating warning information based on the early warning prompt and sending the warning information to the parent monitoring terminal; identifying abnormal pressure data associated with the early warning information and sending the abnormal pressure data and warning information to the doctor monitoring terminal.

[0066] The parent monitoring terminal can be a device used by parents to receive alerts about abnormal orthodontic use. The doctor monitoring terminal can be understood as a device used by doctors to view abnormal data and warning information related to the orthodontic device. Warning information can be simplified and generated based on early warning prompts, adapted to parents' understanding. Abnormal stress data can be the core stress data that triggers the early warning prompts.

[0067] Specifically, the process involves identifying the parent monitoring terminal and doctor monitoring terminal corresponding to the target spinal orthosis; generating warning information based on early warning prompts and sending the warning information to the parent monitoring terminal; identifying abnormal pressure data associated with the warning information and sending the abnormal pressure data and warning information to the doctor monitoring terminal. For example, by retrieving the binding information of the target spinal orthosis, the corresponding parent monitoring terminal identifier and doctor monitoring terminal identifier are matched from the database. Based on the type and core information of the warning prompt, the warning information is generated by simplifying the expression according to the preset parent terminal information specifications. The warning information is pushed to the matched parent monitoring terminal through a preset communication channel, and the push time and status are recorded synchronously. The generation record of the warning prompt is correlated, and the corresponding abnormal pressure data is extracted from the target pressure data and structured. The structured abnormal pressure data and warning information are packaged and sent to the matched doctor monitoring terminal through a dedicated communication link on the doctor's end.

[0068] It should be noted that, based on the above technical solution, the remote analysis terminal in the technical solution of this invention will also generate a pressure heat map based on the pressure data, such as... Figure 4 As shown, after receiving pressure data uploaded by the 4G module, the pressure values ​​of each sensing unit in the two flexible electronic skins are analyzed in real time. The collected pressure data matrix is ​​mapped into a 6×6 array, with a total of 36 points, onto the coordinate grid of the heatmap. Since there are two electronic skins, the monitoring software displays two heatmaps. The pressure value of each sensing point corresponds to the color depth on the heatmap, forming a two-dimensional visualization of the pressure distribution. The heatmap module in the monitoring software has a real-time refresh mechanism: whenever a new pressure data packet is received, the software automatically updates the displayed heatmap data. When the patient's orthosis is worn correctly and fits well, the color distribution on both sides of the thermal map is even, appearing as a light green or yellowish-green overall, indicating balanced pressure distribution. If the pressure on one side is too high (e.g., concentrated red areas), it is determined to be too tight, and the software will automatically display a prompt message: "Too tight, please adjust the orthosis position." If the pressure on one side is too low or there is no signal (increased blue areas), it is determined to be loose or not fully fitted, and the software will automatically prompt: "Not properly fitted, please readjust." During prolonged wear, the color change trend of the thermal map reflects the change in the force on the orthosis over time, providing doctors with a basis for assessing wearing habits and orthotic effects. While generating real-time thermal maps, it also automatically records each uploaded pressure data and generated thermal map snapshot. Doctors can view the patient's pressure change trends at different times based on historical records, analyze the evolution of the force-bearing area, and assist in judging the progress of orthotic treatment. Doctors can view the complete thermal map and historical trends on the doctor's end for remote adjustment of the treatment plan, while the parent's end only displays the real-time thermal map and wearing prompts to help parents quickly determine whether the orthosis is properly fitted. Figure 4 The heat map shown indicates that the target spinal orthosis is being worn too tightly.

[0069] The technical solution of this invention involves a local monitoring terminal collecting initial pressure data corresponding to the target position via a flexible electronic skin module when the target spinal orthosis is detected as fully worn. The local monitoring terminal processes this initial pressure data to obtain target pressure data and transmits it to a remote analysis terminal via remote communication. The remote analysis terminal determines the data analysis results for the target spinal orthosis based on the target pressure data and generates an early warning prompt based on the data analysis results. Based on this technical solution, the pressure threshold is dynamically adjusted according to changes in the bending angle and the orthotic effect during treatment. Simultaneously, the communication module uploads data in real time, supporting remote monitoring and feedback. This avoids the false alarm problem caused by fixed thresholds in traditional methods and mitigates the risk of skin damage or ineffective wear of the orthosis by the patient.

[0070] In one possible implementation of the present invention Figure 5 A flowchart of an early warning method for a spinal orthosis provided in an embodiment of the present invention is shown below. Figure 5 As shown, the step of the local monitoring terminal in this embodiment collecting initial pressure data corresponding to the target location through the flexible electronic skin module further includes:

[0071] S510. Install at least two flexible electronic skin modules at the target positions corresponding to the target spinal orthotics.

[0072] The target locations include the area corresponding to the spinal protrusion and the corresponding area on the opposite side of the armpit. The target locations are specific areas where the orthosis fits against key pressure areas of the body. The area corresponding to the spinal protrusion can be the area on the inside of the orthosis corresponding to the protruding part of the spine, used to monitor the pressure on the protruding part. The corresponding area on the opposite side of the armpit can be the area on the orthosis that fits against the opposite side of the armpit, used to monitor the auxiliary force applied to that area.

[0073] Specifically, at least two flexible electronic skin modules are installed at the target locations corresponding to the target spinal orthosis. For example, by retrieving the structural parameters of the target spinal orthosis, the specific installation points of the area corresponding to the spinal protrusion and the area opposite the armpit are located. Flexible adhesive materials are then used to fix the flexible electronic skin modules at the two target locations, thus deploying the two flexible electronic skin modules.

[0074] The S520 synchronously acquires pressure signals corresponding to the target area through the sensor array of the flexible electronic skin module.

[0075] The sensor array can be a sensing structure composed of multiple pressure sensors arranged in a preset pattern within the flexible electronic skin module. The target area can be understood as the area corresponding to the spinal protrusion where the flexible electronic skin module is installed and the corresponding area on the opposite side of the armpit. The pressure signal is a physical electrical signal generated after the sensor array senses the pressure in the target area.

[0076] Specifically, the sensor array of the flexible electronic skin module synchronously acquires pressure signals corresponding to the target area. For example, the acquisition parameters of the sensor array are configured, and the synchronous acquisition trigger conditions and sampling frequency are determined. A preset excitation voltage is output to the sensor array via a multiplexer to activate all sensors in the array. A synchronous acquisition command is triggered, and each sensor in the array simultaneously senses the pressure at its corresponding point, generating a pressure signal. The pressure signals from each sensor are synchronously transmitted to an analog operational amplifier through a preset acquisition channel, with the signal transmission synchronization maintained by a multiplexer during transmission. The operating status of the sensor array is monitored in real time to ensure synchronous response from each sensor and zero signal transmission delay. Trigger timestamps are recorded during acquisition, and the pressure signals are correlated with the point coordinates of the sensor array and the acquisition time to form raw pressure signal data.

[0077] For example, two flexible electronic skins are used to collect pressure data from the patient's body in real time. The flexible electronic skins employ a stretchable structure design, giving traditional flexible circuit boards fabric-level softness and deformability. The sensing layer is 0.18 mm thick and integrates 36 high-sensitivity pressure sensor units within a 3×3 cm² area, possessing a dynamic response time of less than 10 μs, enabling precise capture of high-frequency dynamic pressure changes. The first sensor is located in the spinal protrusion area on the patient's back to monitor the force on the orthopedic support area; the second sensor is located in the axillary region opposite the spinal protrusion to record the distribution of support force on the opposite side. The signal input and output terminals of both flexible electronic skins are connected to channels 1-6 of the DG9408 multiplexer (MUX) in the signal acquisition module via a standard flexible circuit board interface, achieving synchronous sampling and data fusion. The multi-point pressure signals collected by the two electronic skins can be analyzed in parallel simultaneously to generate a whole-body force distribution map. While ensuring wearing comfort, the system utilizes large-area, multi-zone pressure data to improve the accuracy and stability of the overall analysis, providing doctors with more comprehensive orthopedic data support.

[0078] Considering that patients with severe scoliosis typically wear their orthotics for more than 20 hours daily, a single 3.7V 1200mAh rechargeable lithium-ion battery (size AA) is selected as the main power source to ensure long battery life. When the battery is depleted, the user only needs to open the casing to replace it with a fully charged spare battery; there is no need to connect a charger, thus avoiding disruption to the treatment process due to charging delays. The battery is installed in the power module via a battery holder: the anode of the battery holder is connected to one end of the self-locking manual switch button; the cathode of the battery holder is connected to the negative output terminal of the power module; the other end of the self-locking button is connected to the positive output terminal of the power module to control the power supply of the entire device. The power management module integrates a two-stage voltage conversion circuit: an LT3471 module converts the battery output to ±5V, providing symmetrical dual power supplies for the DG9408 multiplexer and the TLV9154 operational amplifier circuit, ensuring high linearity and low noise performance of the signal amplifier; a TPS7A2033PDBVR module stabilizes the battery voltage to 3.3V DC, providing the main power for the STM32G431 signal acquisition module and peripheral circuits. To further improve energy efficiency, this invention designs an intelligent low-power control mechanism: the LT3471 module can automatically turn on or off according to the control instructions of the STM32G431 microprocessor to prevent energy waste in non-operating states; the STM32G431 microprocessor supports the STOP2 low-power mode and the RTC (Real-Time Clock) timed wake-up mechanism. In sleep mode, the current is only in the μA range, significantly reducing power consumption. The RTC automatically wakes up every 5 minutes to perform data acquisition, analysis, and 4G module data upload operations, and then re-enters sleep mode.

[0079] S530. Based on the pressure range of the pressure signal, the corresponding fitting model is used to convert the pressure signal into initial pressure data.

[0080] The pressure range is a pre-divided segmented interval based on the amplitude range of the pressure signal, used to match and adapt fitting models to different pressure characteristics; the fitting model is a mathematical model used to establish the correspondence between the pressure signal and the pressure data, used to achieve accurate quantization conversion of the pressure signal; the initial pressure data is the raw pressure quantization data obtained after conversion by the fitting model, used as the basic data source for subsequent pressure data processing.

[0081] Specifically, the system retrieves preset pressure range division rules, performs amplitude detection and range matching on the collected pressure signals, and determines the pressure range to which each pressure signal belongs. Based on the preset mapping relationship between pressure ranges and fitting models, the corresponding fitting model for that range is called, and model parameters are loaded. The pressure signal is then input into the matched fitting model, and computational processing is performed to establish a quantized correspondence between the pressure signal amplitude and the pressure data, outputting the initial pressure data.

[0082] Based on the above technical solution, the pressure signal is converted into initial pressure data using a corresponding fitting model according to the pressure range of the pressure signal. This includes: when the pressure signal is in the low pressure range, a linear fitting model is used to convert the pressure signal into initial pressure data; when the pressure signal is in the high pressure range, a quadratic polynomial fitting model is used to convert the pressure signal into initial pressure data.

[0083] The low-pressure range can be a pre-defined low-amplitude segmented range based on the pressure signal amplitude. The high-pressure range can be understood as a pre-defined high-amplitude segmented range based on the pressure signal amplitude. The linear fitting model can be a mathematical model that pre-establishes a linear correspondence between the pressure signal and the initial pressure data. The quadratic polynomial fitting model can be a mathematical model that pre-establishes a quadratic nonlinear correspondence between the pressure signal and the initial pressure data. The pressure signal can be understood as the raw pressure electrical signal acquired by the flexible electronic skin module. The initial pressure data can be the raw pressure quantization data obtained after transformation by the fitting model.

[0084] Specifically, a preset pressure range division threshold is retrieved, and the amplitude of the collected pressure signals is detected to determine the pressure range to which each pressure signal belongs. If it is determined to be a low-pressure range, a pre-stored linear fitting model and corresponding parameters are invoked, the pressure signal is input into the model for calculation, linear quantization conversion is completed, and initial pressure data is output. If it is determined to be a high-pressure range, a pre-stored quadratic polynomial fitting model and corresponding parameters are invoked, the pressure signal is input into the model for calculation, non-linear quantization conversion is completed, and initial pressure data is output.

[0085] For example, during the calibration process, a precision air compressor was used as the standard pressure source to apply a uniform and controllable air pressure signal within the range of 0–80 kPa to the flexible electronic skin. The output voltage values ​​of the sensing unit under different pressures were sampled and recorded, and data fitting and correction were performed. The flexible electronic skin exhibited high sensitivity and good linearity in the low-pressure range (0–10 kPa). A linear fitting was performed on the collected output voltage and applied pressure data to establish a linear calibration equation: ;in, This is the pressure value. The sensor output voltage, and These are the linear fitting coefficients. This calibration method enables a high-precision mapping between pressure and output signal, ensuring measurement accuracy within the normal operating pressure range of the orthodont.

[0086] When the pressure exceeds 10 kPa, the sensitivity of the flexible electronic skin begins to decrease, and a slight nonlinearity appears in the relationship between the output signal and the pressure. To improve the fitting accuracy, this invention uses a quadratic polynomial fitting model for calibration in this range: By repeatedly testing and performing least-squares fitting at different pressure points, the optimal fitting parameters are obtained to ensure high measurement stability and accuracy even in high-pressure areas. Each flexible electronic skin undergoes the above calibration process independently, and the resulting calibration coefficients are stored in the non-volatile memory of the STM32G431 microprocessor. During operation, the microprocessor automatically calls the corresponding calibration function according to the current pressure range to achieve real-time data correction and linear compensation.

[0087] The technical solution of this invention, through a multi-channel flexible electronic skin module, can monitor pressure changes at multiple key pressure points of the patient while wearing the orthosis in real time and accurately. Compared with traditional single-sensor or fixed pressure threshold designs, it can adapt to different wearing states in real time, effectively avoiding false alarms and missed alarms, and ensuring accurate monitoring of treatment effects. Through adaptive algorithms, it can dynamically adjust the pressure thresholds of each sensor and make intelligent judgments for different stages of the patient's orthodontic progress. Through a 4G communication module, this system can upload data in real time in any area with a 4G signal, without relying on Wi-Fi or Bluetooth networks, enabling the system to transmit data in various environments and providing comprehensive remote monitoring and real-time feedback functions. Doctors and parents can check the patient's wearing status at any time, effectively improving the ability to remotely assess and intervene during treatment. Adopting a low-power design and dynamic power management mechanism, the battery life is extended without affecting performance. Combined with a rechargeable lithium battery and STOP2 low-power mode, users do not need to charge frequently, greatly improving the convenience of use and adaptability to long-term wear. The shell design is simple and compact, with modular disassembly and battery replacement functions, allowing users to easily replace the battery and avoiding the hassle of frequent maintenance. Furthermore, the pressure data is visualized using a heatmap, allowing doctors and parents to quickly identify abnormal wearing conditions, thus improving the ease and real-time nature of the operation. Through real-time monitoring, remote feedback, and personalized threshold adjustment, this invention significantly improves the comfort and safety of patients wearing orthotics, while effectively reducing discomfort and improper wearing issues during treatment, further enhancing treatment effectiveness and patient compliance.

[0088] Figure 6 This is a schematic diagram of a pre-warning device for a spinal orthosis provided in an embodiment of the present invention. The device is installed in a pre-warning system for the spinal orthosis, which includes a local monitoring terminal and a remote analysis terminal, such as... Figure 6 As shown, the device includes: a data acquisition module 610, a data processing module 620, and a data analysis module 630.

[0089] The data acquisition module 610 is used to collect initial pressure data corresponding to the target position through the flexible electronic skin module when the target spinal orthosis is detected to be worn.

[0090] The data processing module 620 is used to process the initial pressure data through a local monitoring terminal to obtain target pressure data, and to send the target pressure data to a remote analysis terminal through remote communication;

[0091] The data analysis module 630 is used to determine the data analysis results of the target spinal orthosis based on the target pressure data through the remote analysis terminal, and generate an early warning prompt based on the data analysis results.

[0092] Based on the above technical solution, the data acquisition module is used to install at least two flexible electronic skin modules respectively at target positions corresponding to the target spinal orthodon, wherein the target positions include the area corresponding to the spinal protrusion and the area corresponding to the opposite side of the armpit; the sensor array of the flexible electronic skin module synchronously acquires pressure signals corresponding to the target areas; and the pressure signals are converted into the initial pressure data by using a corresponding fitting model according to the pressure range of the pressure signals.

[0093] Based on the above technical solution, the data acquisition module is used to convert the pressure signal into the initial pressure data by using a linear fitting model when the pressure signal is in the low pressure range; and to convert the pressure signal into the initial pressure data by using a quadratic polynomial fitting model when the pressure signal is in the high pressure range.

[0094] Based on the above technical solution, the data analysis module is used to acquire correction parameters corresponding to the current correction stage, and determine the pressure data threshold corresponding to the current correction stage according to the correction parameters and the target pressure data. The correction parameters include minimum pressure value, curve steepness parameter and inflection point time parameter. The pressure data analysis result is determined based on the pressure data threshold and the target pressure data.

[0095] Based on the above technical solution, the data analysis module is used to determine the sensor pressure value and sensor coordinate position of each sensor in the flexible electronic skin module according to the target pressure data; determine the pressure centroid coordinates based on the sensor pressure value and the sensor coordinate position; and determine the coordinate position analysis result based on the pressure centroid coordinates and historical pressure centroid coordinates.

[0096] Based on the above technical solution, the data analysis module is used to generate a pressure data warning when the target pressure data exceeds the pressure data threshold; and / or to generate a wearing position offset warning when the pressure centroid coordinate offset exceeds a preset range.

[0097] Based on the above technical solution, the data analysis module is used to determine the parent monitoring terminal and doctor monitoring terminal corresponding to the target spinal orthodontic device; generate warning information based on the warning prompt and send the warning information to the parent monitoring terminal; determine abnormal pressure data associated with the warning information and send the abnormal pressure data and the warning information to the doctor monitoring terminal.

[0098] The technical solution of this invention involves a local monitoring terminal collecting initial pressure data corresponding to the target position via a flexible electronic skin module when the target spinal orthosis is detected as fully worn. The local monitoring terminal processes this initial pressure data to obtain target pressure data and transmits it to a remote analysis terminal via remote communication. The remote analysis terminal determines the data analysis results for the target spinal orthosis based on the target pressure data and generates an early warning prompt based on the data analysis results. Based on this technical solution, the pressure threshold is dynamically adjusted according to changes in the bending angle and the orthotic effect during treatment. Simultaneously, the communication module uploads data in real time, supporting remote monitoring and feedback. This avoids the false alarm problem caused by fixed thresholds in traditional methods and mitigates the risk of skin damage or ineffective wear of the orthosis by the patient.

[0099] The early warning device for spinal orthotics provided in this embodiment of the invention can execute the early warning method for spinal orthotics provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0100] Figure 7 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0101] like Figure 7As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0102] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0103] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the early warning method for spinal orthotics.

[0104] In some embodiments, the pre-warning method for spinal orthotics can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the pre-warning method for spinal orthotics described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the pre-warning method for spinal orthotics by any other suitable means (e.g., by means of firmware).

[0105] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0109] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0110] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0111] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0112] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for early warning of a spinal orthosis, characterized in that, The method is applied to an early warning system for spinal orthotics, the system comprising a local monitoring terminal and a remote analysis terminal, including: Once the target spinal orthosis is detected to be worn, the local monitoring unit collects initial pressure data corresponding to the target position through a flexible electronic skin module. The local monitoring terminal processes the initial pressure data to obtain the target pressure data, and then sends the target pressure data to the remote analysis terminal via remote communication; The remote analysis terminal determines the data analysis results of the target spinal orthosis based on the target pressure data, and generates an early warning prompt based on the data analysis results.

2. The method according to claim 1, characterized in that, The local monitoring terminal collects initial pressure data corresponding to the target location through a flexible electronic skin module, including: At least two flexible electronic skin modules are respectively installed at target positions corresponding to the target spinal orthotics, wherein the target positions include the area corresponding to the spinal protrusion and the area corresponding to the opposite side of the armpit; The sensor array of the flexible electronic skin module synchronously acquires pressure signals corresponding to the target area; The pressure signal is converted into the initial pressure data using a corresponding fitting model based on the pressure range of the pressure signal.

3. The method according to claim 2, characterized in that, The step of converting the pressure signal into the initial pressure data using a corresponding fitting model based on the pressure range of the pressure signal includes: When the pressure signal is in the low pressure range, a linear fitting model is used to convert the pressure signal into the initial pressure data; When the pressure signal is in the high pressure range, a quadratic polynomial fitting model is used to convert the pressure signal into the initial pressure data.

4. The method according to claim 1, characterized in that, The remote analysis terminal determines the data analysis results of the target spinal orthosis based on the target pressure data, including: Obtain the correction parameters corresponding to the current correction stage, and determine the pressure data threshold corresponding to the current correction stage based on the correction parameters and the target pressure data, wherein the correction parameters include minimum pressure value, curve steepness parameter and inflection point time parameter; The pressure data analysis results are determined based on the pressure data threshold and the target pressure data.

5. The method according to claim 1, characterized in that, The remote analysis terminal determines the data analysis results of the target spinal orthosis based on the target pressure data, including: The sensor pressure value and sensor coordinate position of each sensor in the flexible electronic skin module are determined based on the target pressure data. The pressure centroid coordinates are determined based on the sensor pressure value and the sensor coordinate position, and the coordinate position analysis results are determined based on the pressure centroid coordinates and historical pressure centroid coordinates.

6. The method according to claim 1, characterized in that, The step of generating an early warning notification based on the data analysis results includes: If the target pressure data exceeds the pressure data threshold, a pressure data warning prompt will be generated; and / or, If the pressure centroid coordinates deviate beyond the preset range, a warning message about the wear position deviation will be generated.

7. The method according to claim 1, characterized in that, After generating an early warning notification based on the data analysis results, the process includes: Determine the parent monitoring terminal and doctor monitoring terminal corresponding to the target spinal orthosis; Based on the aforementioned warning prompt, an alert message is generated and sent to the parent monitoring terminal; Identify the abnormal pressure data associated with the warning information, and send the abnormal pressure data and the warning information to the doctor's monitoring terminal.

8. A warning device for a spinal orthosis, characterized in that, The device is installed in the early warning system of the spinal orthosis, the system including a local monitoring terminal and a remote analysis terminal, including: The data acquisition module is used to collect initial pressure data corresponding to the target position through the flexible electronic skin module when the target spinal orthosis is detected to be worn. The data processing module is used to process the initial pressure data through the local monitoring terminal to obtain the target pressure data, and to send the target pressure data to the remote analysis terminal through remote communication; The data analysis module is used to determine the data analysis results of the target spinal orthosis based on the target pressure data through the remote analysis terminal, and to generate early warning prompts based on the data analysis results.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which enables the at least one processor to perform the warning method for the spinal orthosis according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the warning method for the spinal orthosis according to any one of claims 1-7.

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