Customization method and detection method of continuous carbon fiber 3D printing health detection sensor

Abstract

The present invention discloses a customization method and detection method for a continuous carbon fiber 3D-printed health detection sensor. The customization method for the health detection sensor includes the following steps: S1, constructing a three-dimensional model of the detection part by performing a three-dimensional scan on a user's body part; S2, designing the overall shape of the health detection sensor based on the constructed three-dimensional model of the detection part; S3, designing the performance of the health detection sensor based on the shape and health detection requirements, that is, designing the continuous carbon fiber distribution method in the sensor; S4, obtaining a health detection sensor model by designing the shape and continuous carbon fiber distribution method, inputting the health detection sensor model into carbon fiber slicing software to obtain a printing code, and importing the printing code into a continuous carbon fiber 3D printing device to print the health detection sensor. The present invention can realize the customized design of the shape and performance of sensors used for human health detection, thereby realizing accurate monitoring of specific user body parts, thereby improving the accuracy and efficiency of health monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible sensor customization, and in particular to a customization method and a detection method of a continuous carbon fiber 3D printed health detection sensor. Background Art

[0002] With the increasing trend of an aging population and rising health awareness, society's demand for health monitoring has grown significantly, especially in terms of personalization and precision. The limitations of existing health monitoring devices in shape, size, and performance restrict their ability to meet the needs of personalized health monitoring. Flexible sensors, due to their unique mechanical flexibility, wearability, and biocompatibility, have become a key technology in the field of medical health monitoring. Although flexible sensors have made significant progress in recent years, they still face many challenges in practical application.

[0003] First, the shape of flexible sensors cannot be customized: Existing health monitoring devices have limitations in shape, size, and performance, making them unable to meet the needs of personalized health monitoring. Traditional flexible sensing health monitoring devices are mostly fixed in shape and size, making them difficult to adapt to the individual body characteristics and monitoring needs of different users. This results in limited accuracy and comfort in monitoring data, and their fit and customization still need to be improved. Due to individual differences in body parts and sizes, such sensors cannot meet the personalized needs of all users. They are also difficult to adapt to dynamic movements or activities, or situations that require conforming to complex curved surfaces. Their lack of flexibility and customization makes them unable to meet the physiological differences and specific needs of different individuals. Second, the performance and sensitivity of traditional flexible sensors cannot be customized: Traditional flexible monitoring devices typically use standardized designs, and their performance parameters, such as sensitivity and response speed, are often preset. The performance of traditional flexible monitoring devices is limited by the inherent materials, such as sensitivity and durability, which are often difficult to adjust. They cannot be customized to meet the specific needs of users, making them difficult to meet personalized health monitoring needs. Finally, the manufacturing process of existing flexible sensors often requires complex processes and high costs. The production process often involves multiple steps and equipment, resulting in relatively long production cycles and high costs.

[0004] Therefore, it is an urgent problem for those skilled in the art to propose a customization method and a detection method for continuous carbon fiber 3D printing health detection sensors to solve the difficulties existing in the existing technology. Summary of the Invention

[0005] The purpose of the present invention is to provide a customization method and detection method for a continuous carbon fiber 3D printed health detection sensor, which has the characteristics of customizable shape and performance, and can achieve accurate monitoring of specific user body parts, thereby improving the accuracy and efficiency of health monitoring.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for customizing a continuous carbon fiber 3D printed health detection sensor, comprising the following steps:

[0008] S1, constructing a three-dimensional model of the detection part by performing a three-dimensional scan of the user's body parts;

[0009] S2. Design the overall shape of the health detection sensor based on the constructed three-dimensional model of the detection part;

[0010] S3. Designing the performance of the health detection sensor based on the shape and health detection requirements, that is, designing the distribution of the continuous carbon fibers in the sensor;

[0011] S4. A health detection sensor model is obtained by designing the shape and distribution of continuous carbon fibers. The health detection sensor model is input into the carbon fiber slicing software to obtain a printing code, and the printing code is imported into the continuous carbon fiber 3D printing device to realize the printing of the health detection sensor. The health detection sensor is obtained by combining the printed continuous carbon fibers and a flexible substrate.

[0012] Preferably, in S1, constructing a three-dimensional model of the detection part by performing a three-dimensional scan on the user's health detection part specifically includes:

[0013] The user's body parts are scanned with a 3D scanner to obtain the complex shape and feature data of the detected parts and build a 3D model of the detected parts.

[0014] Preferably, in S3, based on the shape and health detection requirements, the design of the continuous carbon fiber distribution mode of the health detection sensor specifically includes:

[0015] Selection of the specifications, distribution position, distribution direction, and distribution density of continuous carbon fibers; among them, selection of the specifications of continuous carbon fibers: according to the size and tension level of the muscle group at the detection site, select 1K-3K continuous carbon fibers as mechanical sensors; selection of the distribution position of continuous carbon fibers: based on the distribution analysis of the muscle groups, ensure that the continuous carbon fibers accurately cover the target muscle groups and record all changes in muscle tension; selection of the distribution direction of continuous carbon fibers: the laying direction of the continuous carbon fibers is designed according to the contraction and stretching state of the muscles and the direction of the muscle fibers; selection of the distribution density of continuous carbon fibers: the density of the continuous carbon fibers is determined according to the size and range of motion of the muscle groups.

[0016] Preferably, S3 also includes adjusting the sensitivity of the health detection sensor:

[0017] By setting the density and direction of the continuous carbon fibers, the sensitivity of the health detection sensor can be adjusted. When the direction of the continuous carbon fibers remains unchanged, the greater the density of the continuous carbon fibers, the higher the sensitivity of the health detection sensor. When the density of the continuous carbon fibers remains unchanged, the more the direction of the continuous carbon fibers is consistent with the direction of the muscles, the higher the sensitivity of the health detection sensor.

[0018] Preferably, in S4, the health detection sensor model is input into the carbon fiber slicing software to obtain a printing code, and the printing code is imported into the continuous carbon fiber 3D printing device to print the health detection sensor, which specifically includes:

[0019] The designed health detection sensor model is exported as an STL file, the STL file is input into the carbon fiber slicing software to obtain the printing G code, and the printed G code is imported into the continuous carbon fiber 3D printing device; the continuous carbon fiber 3D printing device contains two nozzles, one nozzle is used to print continuous carbon fiber, and the other nozzle is used to print flexible thermoplastic materials.

[0020] A health monitoring device includes a health monitoring sensor customized by any of the customization methods described above, as well as an upper fixture, a lower fixture, and an integrated chip; wherein the upper fixture and the lower fixture are respectively provided on both sides of the health monitoring sensor for fixing the health monitoring sensor; the health monitoring sensor is then connected to the integrated chip via a wire to transmit an electrical signal; the integrated chip receives the electrical signal, processes it, and then transmits it wirelessly.

[0021] A health detection method is applied to a health detection sensor customized by any of the customization methods above, and the health detection sensor is worn on a detection part of a human body to perform health detection, specifically comprising:

[0022] Data acquisition: The relative rate of change of resistance is converted into a voltage or current signal through the circuit in the integrated chip, and then analog-to-digital conversion is performed to convert the analog signal into a digital signal for subsequent processing;

[0023] Data processing and analysis: 1) Preprocessing: Use low-pass, high-pass, or band-pass filters to remove noise and interference from the signal and enhance the signal amplitude; adjust the signal to a standard range; 2) Feature extraction: Extract key features such as peak value, waveform, and frequency from the preprocessed signal; feature extraction uses time domain analysis, frequency domain analysis, or time-frequency analysis;

[0024] Data comparison and analysis:

[0025] The collected data is input into a pre-established muscle tension prediction data model, and the least squares method is used to calculate the parameters m and b of the linear regression model. The unknown muscle tension is predicted through y = mx + b; the predicted muscle tension data values ​​are compared and analyzed with the normal healthy muscle tension data values ​​to evaluate muscle function, including the assessment of muscle tension, range of motion, endurance and coordination parameters.

[0026] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements any of the above-mentioned methods for customizing a continuous carbon fiber 3D printed health detection sensor.

[0027] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0028] This invention proposes a continuous carbon fiber 3D-printed intelligent sensing sensor for health monitoring. This sensor features customizable shape and performance to meet the needs of personalized health monitoring. The flexible sensor is printed using continuous carbon fiber 3D printing equipment, utilizing 3D printing technology. This allows for rapid manufacturing and reduces material waste, offering advantages in production efficiency and cost. The sensor can precisely monitor specific body parts, improving the accuracy and efficiency of health monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order 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. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 A schematic diagram of a process flow for a customization method of a continuous carbon fiber 3D printed health detection sensor provided by the present invention;

[0031] Among them, a is the flow chart for constructing the three-dimensional model of the detection part; b is the flow chart for designing the sensor shape according to the detection part; c is the flow chart for designing the distribution position of the continuous carbon fiber of the sensor;

[0032] Figure 2 This is a structural diagram of the health detection system of the present invention;

[0033] Among them, 2a is the left view of the health detection system, and 2b is the rear view of the health detection system; 1-upper fixture, 2-flexible substrate, 3-continuous carbon fiber, 4-lower fixture, 5-wire, 6-integrated chip. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] like Figure 1 As shown in Figures a-c, the present invention provides a method for customizing a continuous carbon fiber 3D-printed health detection sensor. The health monitoring system includes an upper fixture 1, a health detection sensor, a lower fixture 4, and an integrated chip 6. The upper fixture 1 and the lower fixture 4 are respectively provided on both sides of the health detection sensor to fix the health detection sensor. The health detection sensor is then connected to the integrated chip 6 via a wire 5 to transmit an electrical signal. The integrated chip 6 receives the electrical signal, processes it, and then transmits it wirelessly. The method for customizing the health detection sensor includes the following steps:

[0037] S1, constructing a three-dimensional model of the detection part by performing a three-dimensional scan of the user's body parts;

[0038] S2. Design the overall shape of the health detection sensor based on the constructed three-dimensional model of the detection part;

[0039] S3. Designing the performance of the health detection sensor based on the shape and health detection requirements, that is, designing the distribution of the continuous carbon fibers in the sensor;

[0040] S4. A health detection sensor model is obtained by designing the shape and distribution of continuous carbon fibers. The health detection sensor model is input into the carbon fiber slicing software to obtain a printing code, and the printing code is imported into the continuous carbon fiber 3D printing device to realize the printing of the health detection sensor. The health detection sensor is obtained by combining the printed continuous carbon fibers 3 and the flexible substrate 2.

[0041] Specifically, the main steps are as follows:

[0042] Step 1: 3D scanning to build a model of the test area

[0043] A 3D scanner is used to scan the parts of the user's body that require health monitoring (such as the neck, hands, ankles, etc.) to obtain the complex shape and feature data of the monitored parts, and to build a 3D model of the health monitoring parts. The model can be accurate to the external contours and muscle area contours of the monitored parts, providing accurate data support for the personalized customization of the shape and performance of the flexible sensor.

[0044] Step 2: Design the sensor shape according to the monitoring part model

[0045] Based on the constructed three-dimensional model of the monitoring area, the overall shape of the flexible sensor is designed and customized. By analyzing the model, the sensor's shape and dimensions are determined to ensure that it fully conforms to the user's monitoring area. Furthermore, the sensor must be able to adapt to the shape and movement characteristics of different body parts. For example, for neck monitoring, the sensor needs to be able to flexibly adapt to the bending and rotation of the neck; for limb monitoring, the sensor needs to be able to adapt to the bending and extension of the joints, to maximize the sensing performance and comfort of the sensor during use.

[0046] Step 3: Based on the shape and health monitoring requirements, the sensitivity performance of the flexible sensor is designed and customized by designing the distribution of continuous carbon fibers 3.

[0047] Based on the constructed flexible sensor shape model, the continuous carbon fiber distribution pattern is designed based on the specific needs of the user's health monitoring area. This mainly includes the specification selection, distribution location, orientation, and density of the continuous carbon fiber 3. Specifically, the specifications of the continuous carbon fiber 3 are: Based on the size and tension level of the muscle group to be monitored, continuous carbon fibers with a 1K-3K specification are selected for mechanical sensing. For example, the thinner 1K continuous carbon fiber provides a more flexible fit and higher sensitivity, while the thicker 3K continuous carbon fiber provides a larger testing range and strength. The distribution location of the continuous carbon fiber 3 is: Based on the distribution analysis of muscle groups, the continuous carbon fiber is ensured to cover the target muscle groups to ensure that all changes in muscle tension can be captured. (1. Identify muscle groups that play a key role in neck movement and stability, such as the trapezius, sternocleidomastoid, and platysma. 2. Place the continuous carbon fiber sensors over key muscle groups, ensuring that the sensors cover the majority of these muscle groups. 3. Align the distribution direction of the continuous carbon fiber with the direction of the muscle fibers: Align the orientation of the health detection sensor with the direction of the muscle fibers to maximize the sensor's response to changes in muscle tension.)

[0048] Example: Monitoring the Trapezius Muscle

[0049] The trapezius muscle, located in the neck and back, is divided into upper and middle sections, each with slightly different muscle fiber orientations. The upper trapezius fibers primarily run from the cervical spine to the scapula; the middle trapezius fibers run from the thoracic spine to the scapula. Sensor distribution orientation selection: For the upper trapezius, continuous carbon fibers should be arranged along the direction from the cervical spine to the scapula to capture changes in tension in the upper trapezius during head tilt and rotation. For the middle trapezius, continuous carbon fibers should be arranged along the direction from the thoracic spine to the scapula to capture changes in tension in the middle trapezius during shoulder elevation and rotation.

[0050] Sensor layout: When designing the layout of the sensor, ensure that the sensor covers the entire muscle group of the trapezius muscle while maintaining consistency with the direction of the muscle fibers. Continuous carbon fiber distribution direction: The laying direction of the continuous carbon fiber should be designed according to the contraction and stretching state of the muscle and the direction of the muscle fibers to improve the sensitivity of the sensor. Continuous carbon fiber distribution density: The required density of continuous carbon fiber is determined according to the size and range of motion of the muscle group (high density range: the spacing between continuous carbon fiber filaments is less than 1mm, suitable for occasions requiring high sensitivity; medium density range: the spacing between continuous carbon fiber filaments is 1-3mm, suitable for occasions requiring a balance between sensitivity and flexibility; low density range: the spacing between continuous carbon fiber filaments is greater than 3mm, suitable for occasions requiring high flexibility and durability.).

[0051] In addition, the sensitivity of the flexible sensor can also be adjusted: 1. When the direction remains unchanged, the greater the density of the continuous carbon fiber, the higher the sensitivity; 1. When the density remains unchanged, the more consistent the direction of the continuous carbon fiber and the muscle, the higher the sensitivity.

[0052] Adjust the sensitivity of the sensor according to the expected tension range of the patient's muscle group. For areas with large changes in muscle tension, higher sensitivity may be required, which can be achieved by increasing the number of carbon fiber layers or adjusting the laying position of the carbon fiber, such as Figure 2 a, in order to improve the sensitivity of the sensor to changes in muscle tension. It can also be achieved by choosing a suitable carbon fiber laying angle, such as Figure 2 b. The sensor’s sensitivity to forces in a specific direction can be optimized. If the sensor needs to be more sensitive to forces perpendicular to the fiber direction, the carbon fibers can be laid at an angle close to 90 degrees.

[0053] Step 4: 3D Printing of the Sensor

[0054] The human body part health detection sensor model with designed shape and performance is exported as an STL file, and imported into the carbon fiber slicing software for slicing to obtain the printing G code; the G code is imported into the continuous carbon fiber 3D printing device, wherein the continuous carbon fiber 3D printing device includes two nozzles, one nozzle is used to print continuous carbon fiber, and the other nozzle is used to print flexible thermoplastic material (the matrix material of the present invention can be thermoplastic polyurethane, silicone, flexible PLA material, etc.); the designed human body part health detection sensor can use the slicing software to plan the path of the continuous carbon fiber, maximize the continuity of carbon fiber printing, meet the functional customization of the sensor, and improve the mechanical properties of the sensor.

[0055] Step 5: Sensor health detection data collection and analysis;

[0056] The printed sensors can be used for human health monitoring and to collect data on mechanical activities such as muscle stretching, compression, and bending during human movement.

[0057] 1. Data Acquisition: When a patient engages in muscle movement, the continuous carbon fibers within the sensor are subjected to tension, causing a change in resistance. A circuit converts this resistance change (the relative rate of change) into a voltage or current signal. This is then converted to a digital signal for subsequent processing through analog-to-digital conversion.

[0058] 2. Data Processing and Analysis: 1) Preprocessing: Remove noise and interference from the signal, such as using low-pass, high-pass, or band-pass filters; enhance the signal's amplitude for subsequent processing; and adjust the signal to a standard range for easy comparison and analysis. 2) Feature Extraction: Extract key features from the preprocessed signal, such as peak value, waveform, and frequency. Feature extraction can be performed using time-domain analysis (e.g., mean, variance), frequency-domain analysis (e.g., Fast Fourier Transform (FFT), or time-frequency analysis (e.g., wavelet transform).

[0059] 3. Data comparison and analysis:

[0060] Based on the established muscle tension prediction data model, the least squares method was used to calculate the parameters m and b of the linear regression model, and the unknown muscle tension was predicted by y=mx+b.

[0061] The predicted muscle tension data values ​​are compared and analyzed with the normal healthy muscle tension data values ​​(the difference between the patient data and the normal value or historical data is calculated, including the mean, standard deviation, percentage difference, etc.; statistical methods (such as t-test, ANOVA, non-parametric test, etc.) are used to determine whether the difference is statistically significant) to evaluate muscle function, including the evaluation of parameters such as muscle tension, range of motion, endurance, and coordination. Through analysis, abnormal patterns of muscle function and potential pathological conditions can be identified. And ensure that the data is consistent with known medical knowledge and clinical practice. Muscle tension data should match the physiological function of the muscle and known pathological conditions. Combine the data results with the patient's clinical symptoms and signs to provide a comprehensive clinical interpretation.

[0062] Step 6: Based on the data analysis results, provide feedback to doctors and guide treatment and rehabilitation.

[0063] The analysis results are presented to doctors in the form of charts, trend lines, and reports, helping them quickly understand their patients' muscle health. Based on the data analysis results, doctors can develop personalized treatment plans for their patients. This may include physical therapy, medication, surgery, or rehabilitation training. Based on the muscle function assessment results, treatment goals can be set, such as increasing muscle strength, improving muscle endurance, or restoring muscle coordination.

[0064] During treatment, doctors can adjust treatment plans based on the real-time data provided by the continuous carbon fiber tension sensor. For example, if the data shows improved muscle tone, the intensity or frequency of certain treatments may need to be reduced. The continuous carbon fiber smart sensor can be used to monitor rehabilitation progress.

[0065] Doctors can use this data to evaluate the effectiveness of rehabilitation training and adjust training plans as needed. They can use data analysis results to provide guidance to patients, helping them understand their muscle health and treatment progress. This helps improve treatment compliance and rehabilitation outcomes. For patients requiring long-term rehabilitation, doctors can use continuous carbon fiber smart sensors for long-term tracking to ensure sustained rehabilitation results.

[0066] Furthermore, heating therapy parameters, including temperature, heating time, and heating area, can be customized based on the doctor's guidance and the patient's specific condition, such as muscle size, location, and treatment needs. For example, continuous carbon fibers can be placed in areas of muscle tension and soreness, utilizing their thermal conductivity to stimulate acupuncture points on the back of the neck and relax the neck muscles. This can relieve muscle tension and stiffness, help dilate blood vessels, and improve blood circulation, which is very beneficial for promoting muscle recovery and reducing inflammatory responses.

[0067] Also includes setting up an alarm system based on force resistance characteristics:

[0068] One or more thresholds are set based on the normal functional range of a muscle group. When the muscle tension or activity level detected by the sensor exceeds these thresholds, the alarm system will be triggered. Users and doctors can adjust the alarm thresholds and notification methods as needed to suit different monitoring environments and needs. A multi-level alarm system can be designed to issue different levels of alerts, such as mild, moderate, and severe warnings, depending on the severity of the muscle dysfunction.

[0069] A health monitoring device includes a health monitoring sensor customized by any of the customization methods described above, an upper fixture 1, a health monitoring sensor, a lower fixture 4, and an integrated chip 6. The upper fixture 1 and the lower fixture 4 are provided on either side of the health monitoring sensor, respectively, for securing the health monitoring sensor. The upper fixture 1 and the lower fixture 4 are manufactured using 3D printing of flexible thermoplastic materials. 3D printing allows for customization of complex fixture structures, enabling personalized design and rapid fabrication of fixtures. The health monitoring sensor is then connected to the integrated chip 6 via a wire 5 to transmit electrical signals. The integrated chip 6 receives and processes the electrical signals, and then wirelessly transmits the signals.

[0070] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a method for customizing a continuous carbon fiber 3D printed health detection sensor as described above is implemented.

[0071] In a specific embodiment, taking human neck health monitoring as an example:

[0072] Step 1: Use precise scanning to obtain the neck contour, establish a three-dimensional model of the neck, and analyze the personalized continuous carbon fiber intelligent perception flexible sensor model, so that the sensor model can fully contact the neck, and the contact points are reasonably distributed, so that the continuous carbon fiber intelligent perception flexible sensor can better adapt to the shape and function of the neck, improving the user's adaptation speed and comfort.

[0073] Step 2: Designers, users and doctors negotiate to clarify which muscle groups are the focus of monitoring. (For example, for high and low shoulder problems, the muscles that need to be monitored may include the trapezius, deltoid, pectoral and latissimus dorsi muscles of the shoulders. The activity and strength output of these muscles are crucial to improving posture.) According to different user needs (physiological problems), continuous carbon fiber is laid and arranged in appropriate positions. The laying of continuous carbon fiber should be customized according to the direction and function of the muscles. For example, the area around the neck, in order to measure parameters such as tension, torsion and pressure, the arrangement of carbon fibers is designed to be parallel, cross or other geometric shapes to ensure that resistance changes can be effectively detected under loads in different directions.

[0074] For stretched muscles, continuous carbon fiber should be laid in the direction of the muscle's stretching to monitor changes in muscle length. When a muscle is stretched, it may lose its normal elasticity, resulting in an inability to effectively contract or stretch during activity. Continuous carbon fiber sensors laid in the direction of the stretched muscle can monitor changes in muscle length during stretching. This monitoring helps assess a muscle's stretching capacity and determine whether it requires strength training or specific stretching exercises.

[0075] Step 2: The patient originally had a series of physiological problems such as forward head tilt, uneven shoulders, etc. Combined with relevant medical knowledge and doctor's guidance, continuous carbon fibers were placed in appropriate positions, and heating treatment was performed using the thermal conductivity of continuous carbon fibers to stimulate the acupuncture points on the back of the neck and relax the neck muscles, which can relieve muscle tension and stiffness in various parts of the body.

[0076] In summary, the continuous carbon fiber intelligent sensing flexible sensor can provide important quantitative data in neck muscle rehabilitation monitoring, helping doctors and therapists more accurately assess patients' muscle function, develop personalized rehabilitation plans, and monitor rehabilitation outcomes. With continued technological advancements, this monitoring method is expected to be more widely used in rehabilitation medicine in the future.

[0077] Step 3: Printing and manufacturing.

[0078] Step 4: Intelligent Processing System. The relative rate of change in resistance of the continuous carbon fiber intelligent sensing flexible sensor increases with increasing external load. This change in resistance can reflect the magnitude of the external load in real time. By laying continuous carbon fibers in varying quantities or shapes at different locations, the location and deformation of external forces can be identified, enabling intelligent sensing and monitoring of muscle activity, joint mobility, range of motion, and recovery progress. Furthermore, the continuous carbon fiber intelligent sensing flexible sensor can measure the magnitude and direction of force generated by muscles, thereby assessing muscle strength, endurance, and coordination. Leveraging the mechanical resistance properties of carbon fiber, the continuous carbon fiber flexible sensor can sense the magnitude and direction of force applied to the sensor in real time and convert this force information into changes in resistance. Analysis of this data can provide insights into muscle health and status, providing quantifiable, objective, accurate, and reliable support for clinical treatment. Finally, the data collected by the continuous carbon fiber intelligent sensing flexible sensor is transmitted to a smart terminal via a Bluetooth transmitter, potentially enabling online health assessments, motor function evaluations, rehabilitation training guidance, and more. Physical quantities are converted into electrical signals and transmitted to the data acquisition module. The data acquisition module receives the electrical signals transmitted by the sensors and converts them into digital signals for subsequent processing and analysis. The processor processes and analyzes the collected data in real time, performing operations such as filtering, calibration, and correction to ensure the accuracy and reliability of the monitoring results. The processor can be a microcontroller, single-chip microcomputer, or embedded processor. The communication module transmits the processed data to a monitoring platform or cloud storage system, enabling remote data monitoring and management. The communication module can utilize wireless communication technologies such as Wi-Fi, Bluetooth, and LoRa, or wired communication technologies such as Ethernet and RS-485 to provide a stable power supply to the circuits and ensure proper operation. Therefore, the continuous carbon fiber flexible sensor monitors the wearer's neck muscle tension in real time based on the changes in the resistance of the continuous carbon fiber, providing real-time information on the intensity and degree of various neck movements, such as lateral flexion, extension, and backward bending.

[0079] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0080] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for customizing a continuous carbon fiber 3D printed health detection sensor, characterized in that: The following steps are involved: S1, constructing a three-dimensional model of the detection part by performing a three-dimensional scan of the user's body parts; S2. Designing the overall shape of the health detection sensor based on the constructed three-dimensional model of the detection part; S3. Designing the performance of the health detection sensor based on the shape and health detection requirements, that is, designing the distribution of the continuous carbon fibers in the sensor; S4. A health detection sensor model is obtained by designing the shape and distribution of continuous carbon fibers. The health detection sensor model is input into carbon fiber slicing software to obtain a printing code. The printing code is then imported into a continuous carbon fiber 3D printing device to print the health detection sensor. The health detection sensor is obtained by combining the printed continuous carbon fibers and a flexible substrate. In S3, based on the shape and health detection requirements, the continuous carbon fiber distribution mode of the health detection sensor is designed, specifically including: Selection of the specifications, distribution location, distribution direction, and distribution density of the continuous carbon fiber; among them, the selection of the specifications of the continuous carbon fiber: based on the size and tension level of the muscle group at the detection site, select 1K-3K continuous carbon fiber as a mechanical sensor; the selection of the distribution location of the continuous carbon fiber: based on the distribution analysis of the muscle group, ensure that the continuous carbon fiber accurately covers the target muscle group and records all changes in muscle tension; the selection of the distribution direction of the continuous carbon fiber: the laying direction of the continuous carbon fiber is designed according to the contraction and stretching state of the muscle and the direction of the muscle fibers; the selection of the distribution density of the continuous carbon fiber: the density of the continuous carbon fiber is determined according to the size and range of motion of the muscle group; The S3 also includes adjusting the sensitivity of the health detection sensor: By setting the density and direction of the continuous carbon fibers, the sensitivity of the health detection sensor can be adjusted. When the direction of the continuous carbon fibers remains unchanged, the greater the density of the continuous carbon fibers, the higher the sensitivity of the health detection sensor. When the density of the continuous carbon fibers remains unchanged, the more the direction of the continuous carbon fibers is consistent with the direction of the muscles, the higher the sensitivity of the health detection sensor.

2. The method for customizing a continuous carbon fiber 3D printed health detection sensor according to claim 1, characterized in that: In S1, constructing a three-dimensional model of the detection part by performing a three-dimensional scan on the user's health detection part specifically includes: The user's body parts are scanned with a 3D scanner to obtain the complex shape and feature data of the detected parts and build a 3D model of the detected parts.

3. The method for customizing a continuous carbon fiber 3D printed health detection sensor according to claim 1, characterized in that: In S4, the health detection sensor model is input into the carbon fiber slicing software to obtain a printing code, and the printing code is imported into the continuous carbon fiber 3D printing device to print the health detection sensor. Specifically, the steps include: The designed health detection sensor model is exported as an STL file, the STL file is input into the carbon fiber slicing software to obtain the printing G code, and the printing G code is imported into the continuous carbon fiber 3D printing device; the continuous carbon fiber 3D printing device includes two nozzles, one nozzle is used to print continuous carbon fiber, and the other nozzle is used to print flexible thermoplastic material.

4. A health detection device, characterized in that: The health detection device includes a health detection sensor customized by the customization method described in any one of claims 1-3, as well as an upper fixer, a lower fixer, and an integrated chip; wherein, an upper fixer and a lower fixer are respectively provided on both sides of the health detection sensor to fix the health detection sensor; the health detection sensor is then connected to the integrated chip via a wire to transmit electrical signals; the integrated chip receives the electrical signals and processes them, and then transmits them wirelessly.

5. A health detection method, applied to a health detection sensor customized by the customization method according to any one of claims 1 to 3, characterized in that: Wear the health detection sensor to the detection part of the human body to perform health detection, including: Data acquisition: The relative rate of change of resistance is converted into a voltage or current signal through the circuit in the integrated chip, and then analog-to-digital conversion is performed to convert the analog signal into a digital signal for subsequent processing; Data processing and analysis: 1) Preprocessing: Use low-pass, high-pass, or band-pass filters to remove noise and interference from the signal, enhance the signal amplitude, and adjust the signal to a standard range; 2) Feature extraction: Extract key features such as peak value, waveform, and frequency from the preprocessed signal; feature extraction uses time domain analysis, frequency domain analysis, or time-frequency analysis; Data comparison and analysis: The collected data is input into a pre-established muscle tension prediction data model, and the least squares method is used to calculate the parameters m and b of the linear regression model. The unknown muscle tension is predicted through y=mx+b; the predicted muscle tension data values ​​are compared and analyzed with the normal healthy muscle tension data values ​​to evaluate muscle function, including the assessment of muscle tension, range of motion, endurance and coordination parameters.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for customizing a continuous carbon fiber 3D printed health detection sensor as described in any one of claims 1 to 3 is implemented.

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