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

Abstract

The invention discloses a customization method of a continuous carbon fiber 3D printing health detection sensor and a detection method. The customization method of the health detection sensor comprises the following steps: S1, performing three-dimensional scanning on a body part of a user to construct a three-dimensional model of a detection 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, namely designing the continuous carbon fiber distribution mode in the sensor; and S4, a health detection sensor model is obtained through design of the shape and the continuous carbon fiber distribution mode, the health detection sensor model is input into carbon fiber slicing software to obtain a printing code, and the printing code is imported into continuous carbon fiber 3D printing equipment to achieve printing of the health detection sensor. According to the invention, customized design of the shape and performance of the sensor for human health detection can be realized, so that accurate monitoring of the body part of a specific user is realized, and the accuracy and efficiency of health monitoring are improved.

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 population aging and the improvement of health awareness, the demand for health monitoring has increased significantly, especially the requirements for personalization and precision. The limitations of existing health monitoring devices in shape, size and performance limit their ability to meet the needs of individualized health monitoring. Flexible sensors have become one of the key technologies in the field of medical health monitoring due to their unique mechanical flexibility, wearability and biocompatibility. Although flexible sensors have made significant progress in recent years, they still face many challenges in practical applications.

[0003] First, the shape of flexible sensors cannot be customized: existing health monitoring devices have limitations in shape, size, performance, etc., and cannot meet the needs of individual health monitoring. Traditional flexible sensing health monitoring devices are mostly fixed in shape and size, which is difficult to adapt to the physical characteristics and monitoring needs of different users, resulting in limited accuracy and comfort of monitoring data, and their fit and customization still need to be improved. Due to individual differences in user body parts and sizes, such sensors are difficult to meet the personalized needs of all users. It is difficult to adapt to dynamic movements or activities or occasions where complex curved shapes are required. It lacks flexibility and the ability to be customized, and cannot meet the physiological differences and specific needs of different individuals. Secondly, the performance of traditional flexible sensors, such as sensitivity, cannot be customized: traditional flexible monitoring devices usually adopt standardized designs, and their performance parameters such as sensitivity and response speed are usually preset; the performance of traditional flexible monitoring devices is limited by the materials themselves, such as sensitivity and durability, which are often difficult to adjust, and cannot be personalized according to the specific needs of users, and it is difficult to meet the needs of individual health monitoring. Finally, the existing flexible sensors often require complex processes and high costs in the manufacturing process. The production process usually involves multiple steps and equipment, and the production cycle is relatively long and the cost is high.

[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 printed health detection sensors to solve the difficulties existing in the prior art. Summary of the invention

[0005] The purpose of the present invention is to provide a customization method and a detection method of a continuous carbon fiber 3D printed health detection sensor, which has the characteristics of customizable shape and customizable performance, and can realize 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. Based on the shape and health detection requirements, the performance of the health detection sensor is designed, that is, the distribution mode of the continuous carbon fibers in the sensor is designed;

[0011] S4. A health detection sensor model is obtained by designing the shape and distribution of continuous carbon fiber. 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 a 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 fiber and a flexible substrate.

[0012] Preferably, in S1, constructing a three-dimensional model of the detection part by three-dimensionally scanning 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 continuous carbon fiber distribution mode of the health detection sensor is designed specifically including:

[0015] Selection of specifications, distribution positions, distribution directions, and distribution densities of continuous carbon fibers; among them, selection of 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 distribution positions of continuous carbon fibers: according to the distribution analysis of muscle groups, ensure that the continuous carbon fibers accurately cover the target muscle groups and record all changes in muscle tension; selection of distribution directions of continuous carbon fibers: the laying direction of continuous carbon fibers is designed according to the contraction and stretching state of the muscles and the direction of the muscle fibers; selection of distribution density of continuous carbon fibers: the density of 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 is, the higher the sensitivity of the health detection sensor is; 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 is.

[0018] Preferably, in S4, the health detection sensor model is input into the carbon fiber slicing software to obtain the 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, 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 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.

[0020] A health detection device, the health monitoring device includes a health detection sensor customized by any of the above customization methods, 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 through a wire to transmit an electrical signal; the integrated chip receives the electrical signal and processes it, and then transmits it wirelessly.

[0021] A health detection method is applied to a health detection sensor customized by any of the above customization methods, 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 amplitude of the signal; adjust the signal to a standard range; 2) Feature extraction: Extract key features such as peak, 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 the pre-established muscle tension prediction data model, and the parameters m and b of the linear regression model are calculated using the least squares method. 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 evaluation 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, a method for customizing a continuous carbon fiber 3D printed health detection sensor as described above is implemented.

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

[0028] The present invention proposes a continuous carbon fiber 3D printed intelligent sensing sensor for health detection. The sensor has the characteristics of customizable shape and performance to meet the needs of personalized health monitoring; the flexible sensor is printed using continuous carbon fiber 3D printing equipment, and 3D printing technology is used to complete the manufacturing in a short time, and reduce material waste, so it has advantages in production efficiency and cost; the sensor of the present invention can realize accurate monitoring of specific user body parts, thereby 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 drawings required for use in the embodiments will be briefly introduced below. 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 creative labor.

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

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

[0032] Figure 2 It 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work 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 to 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; wherein 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 through a wire 5 to transmit an electrical signal; the integrated chip 6 receives the electrical signal and 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. Based on the shape and health detection requirements, the performance of the health detection sensor is designed, that is, the distribution mode of the continuous carbon fibers in the sensor is designed;

[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. 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 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] For the parts of the user that need health monitoring (such as the neck, hands, ankles, etc.), use a 3D scanner to scan, obtain the complex shape and feature data of the monitored parts, and build a 3D model of the health monitoring part. The model can be accurate to the external contour and muscle area contour of the monitored part, 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 part, the overall shape of the flexible sensor is designed and customized. By analyzing the model, the shape and size of the sensor are determined to ensure that the sensor can fully fit the user's monitoring part. At the same time, it should be ensured that the sensor can adapt to the shape and movement characteristics of different parts. For example, for monitoring the neck, the sensor needs to be able to flexibly adapt to the bending and rotation of the neck; for monitoring the limbs, 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 when used.

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

[0047] Based on the constructed flexible sensor shape model, the continuous carbon fiber distribution mode is designed according to the specific needs of user part health monitoring. It mainly includes the specification selection, distribution position, direction, density, etc. of continuous carbon fiber 3. Specifically, the specification of continuous carbon fiber 3: according to the size and tension level of the muscle group of the monitored part, select 1K-3K continuous carbon fiber as mechanical sensing, for example, 1K thinner continuous carbon fiber can provide more flexible fit and higher sensitivity, while thick 3K continuous carbon fiber can provide a larger test range and strength; continuous carbon fiber 3 distribution position: according to the distribution analysis of muscle groups, ensure that the continuous carbon fiber can cover the target muscle group to ensure that all changes in muscle tension can be captured. (1. Identify the muscle groups that play a key role in neck movement and stability, such as trapezius, sternocleidomastoid, platysma, etc. 2. Place the continuous carbon fiber sensor on the key muscle groups to ensure that the sensor can cover most of the area of ​​these muscle groups. 3. The selection of the continuous carbon fiber distribution direction is consistent with the direction of the muscle fiber: the direction of the health detection sensor is consistent with the direction of the muscle fiber to maximize the sensor's response to changes in muscle tension.)

[0048] Example: Monitoring the Trapezius Muscle

[0049] The trapezius muscle is located in the neck and back and is divided into the upper and middle parts, with the direction of the muscle fibers in each part being slightly different. The fibers of the upper trapezius muscle mainly run from the cervical spine to the scapula; the fibers of the middle trapezius muscle run from the thoracic spine to the scapula. Sensor distribution direction selection: Upper trapezius muscle: Continuous carbon fibers should be arranged along the direction from the cervical spine to the scapula to capture the tension changes of the upper trapezius muscle when the head is tilted and rotated. Middle trapezius muscle: Continuous carbon fibers should be arranged along the direction from the thoracic spine to the scapula to capture the tension changes of the middle trapezius muscle when the shoulder is lifted and rotated.

[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: According to the size and range of motion of the muscle group, the required density of continuous carbon fiber is determined (high density range: the interval between continuous carbon fiber filaments is less than 1mm, suitable for occasions requiring high sensitivity; medium density range: the interval between continuous carbon fiber filaments is 1-3mm, suitable for occasions requiring a balance between sensitivity and flexibility; low density range: the interval 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 is, 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 placement 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 sensitivity of the sensor 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 comprises 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 continuous printing of carbon fiber, 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 collection: When the patient performs muscle activity, the continuous carbon fiber inside the sensor is subjected to tension, causing the resistance value of the continuous carbon fiber to change. The resistance change relative change rate is converted into a voltage or current signal through the circuit, and then analog-to-digital conversion is performed to convert the analog signal into a digital signal for subsequent processing.

[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 amplitude of the signal for subsequent processing; adjust the signal to a standard range for comparison and analysis. 2) Feature extraction: extract key features from the preprocessed signal, such as peak value, waveform, frequency, etc. Feature extraction can use time domain analysis (such as mean, variance), frequency domain analysis (such as fast Fourier transform FFT) or time-frequency analysis (such as wavelet transform).

[0059] 3. Data comparison and analysis:

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

[0061] Compare and analyze the predicted muscle tension data values ​​with the normal healthy muscle tension data values ​​(calculate the difference between the patient data and the normal value or historical data, including the mean, standard deviation, percentage difference, etc.; use statistical methods (such as t-test, ANOVA, non-parametric test, etc.) to determine whether the difference is statistically significant), and 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 states 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 states. 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, which helps doctors quickly understand the patient's muscle health status. Based on the data analysis results, doctors can develop personalized treatment plans for patients. This may include physical therapy, medication, surgery or rehabilitation training. Based on the assessment results of muscle function, treatment goals are 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 that muscle tension has improved, the intensity or frequency of certain treatment measures may need to be reduced. The continuous carbon fiber smart sensor can be used to monitor the progress of rehabilitation.

[0065] Doctors can use the data to evaluate the effectiveness of rehabilitation training and adjust the training plan as needed. Doctors can use the data analysis results to guide patients and help them understand their muscle health and treatment progress. This helps improve patients' treatment compliance and rehabilitation effects. For patients who need long-term rehabilitation, doctors can use continuous carbon fiber smart sensors for long-term tracking to ensure continuous rehabilitation effects.

[0066] In addition, the parameters of heating treatment, including temperature, heating time and heating area, can be customized according to the doctor's guidance and the patient's specific conditions, such as the size, location and treatment needs of muscle groups. For example, continuous carbon fibers are arranged in the patient's muscle tension and sore areas, and the thermal conductivity of continuous carbon fibers is used for heating treatment to stimulate the 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 the 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 adapt to different monitoring environments and needs. Design a multi-level alarm system to issue different levels of alarms, such as mild, moderate and severe warnings, depending on the severity of muscle dysfunction.

[0069] A health detection device, the health monitoring device comprises a health detection sensor customized by any one of the above customization methods, as well as an upper fixture 1, a health detection sensor, a lower fixture 4, and an integrated chip 6; wherein, an upper fixture 1 and a lower fixture 4 are respectively arranged on both sides of the health detection sensor, for fixing the health detection sensor; the upper fixture 1 and the lower fixture 4 are manufactured by 3D printing flexible thermoplastic materials, and 3D printing can customize complex fixture structures, realize personalized design and rapid production of the fixture; the health detection sensor is then connected to the integrated chip 6 through a wire 5 to transmit an electrical signal; the integrated chip 6 receives the electrical signal and processes it, and then transmits it wirelessly;

[0070] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and 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 a personalized continuous carbon fiber intelligent sensing flexible sensor model, so that the sensor model can fully contact the neck and the contact parts are reasonably distributed, so that the continuous carbon fiber intelligent sensing flexible sensor can better adapt to the shape and function of the neck and improve 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 key muscles to be monitored may include the trapezius, deltoid, pectoralis, latissimus dorsi, etc. of the shoulders. The activity and strength output of these muscles are crucial to improving posture.) Lay continuous carbon fiber according to different user needs (physiological problems) Continuous carbon fiber is arranged in appropriate positions, and the laying of continuous carbon fiber should be customized according to the direction and function of the muscle. 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 stretching direction of the muscle 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 are laid in the stretching direction of the stretched muscle to monitor changes in muscle length during stretching. This monitoring helps assess the stretching ability of the muscle and determine whether the muscle needs to be strengthened with 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, the continuous carbon fiber is arranged in the appropriate position, and the thermal conductivity of the continuous carbon fiber is used for heating treatment 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 conclusion, the continuous carbon fiber intelligent sensing flexible sensor can provide important quantitative data in the rehabilitation monitoring of neck muscles, helping doctors and therapists to more accurately assess the patient's muscle function, develop personalized rehabilitation plans, and monitor the rehabilitation effect. With the continuous advancement of technology, 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 change rate of resistance of the continuous carbon fiber intelligent sensing flexible sensor increases with the increase of external load, and the change of resistance value of continuous carbon fiber can reflect the size of external load in real time. By laying continuous carbon fibers of different quantities or shapes at different positions, the position and deformation of external force can be identified, thereby realizing intelligent perception and monitoring of human muscle activity, joint activity, range of motion, rehabilitation degree, etc. In addition, the continuous carbon fiber intelligent sensing flexible sensor can measure the magnitude and direction of the force generated by the muscle, thereby evaluating the strength, endurance and coordination of the muscle. The continuous carbon fiber flexible sensor, using the force resistance characteristics of carbon fiber, can sense the magnitude and direction of the force applied to the sensor in real time, and convert the force information into changes in resistance value. Through the analysis of these data, the health status and state of the muscles can be understood, 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 the intelligent terminal through the Bluetooth transmitter, which is expected to be used for online health assessment, motor function assessment, rehabilitation training guidance, etc. Convert physical quantities into electrical signals and pass them to the data acquisition module. The data acquisition module is responsible for receiving the electrical signal transmitted by the sensor and converting it into a digital signal for subsequent processing and analysis. The processor is responsible for real-time processing and analysis of the collected data, such as data filtering, calibration, correction and other operations to ensure the accuracy and reliability of the monitoring results. The processor can be a microcontroller, a single-chip microcomputer or an embedded processor. The communication module is used to transmit the processed data to the monitoring platform or cloud storage system to achieve remote monitoring and management of the data. The communication module can use wireless communication technologies such as Wi-Fi, Bluetooth, LoRa, etc., or wired communication technologies such as Ethernet, RS-485, etc. to provide a stable power supply for the circuit to ensure the normal operation of the circuit. Therefore, the continuous carbon fiber flexible sensor can realize real-time monitoring of the wearer's neck muscle tension according to the change of continuous carbon fiber resistance, and can reflect the intensity and degree of various movements of the user's neck lateral flexion, extension, and backward in real time.

[0079] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for 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 some parts of the embodiments.

[0080] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood 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. Based on the shape and health detection requirements, the performance of the health detection sensor is designed, that is, the distribution mode of the continuous carbon fibers in the sensor is designed; 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 a 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.

2. A 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 three-dimensionally scanning 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 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 specifications, distribution positions, distribution directions, and distribution densities of continuous carbon fibers; among them, selection of 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 distribution positions of continuous carbon fibers: according to the distribution analysis of muscle groups, ensure that the continuous carbon fibers accurately cover the target muscle groups and record all changes in muscle tension; selection of distribution directions of continuous carbon fibers: the laying direction of continuous carbon fibers is designed according to the contraction and stretching state of the muscles and the direction of the muscle fibers; selection of distribution density of continuous carbon fibers: the density of continuous carbon fibers is determined according to the size and range of motion of the muscle groups.

4. The method for customizing a continuous carbon fiber 3D printed health detection sensor according to claim 3, characterized in that: The S3 also includes adjusting the sensitivity of the health detection sensor: By setting the density and direction of the continuous carbon fiber, the sensitivity of the health detection sensor can be adjusted; when the direction of the continuous carbon fiber remains unchanged, the greater the density of the continuous carbon fiber is set, the higher the sensitivity of the health detection sensor is; 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.

5. 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, which specifically includes: 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; 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.

6. A health detection device, characterized in that: The health monitoring device includes a health detection sensor customized by the customization method described in any one of claims 1-5, 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 through a wire to transmit an electrical signal; the integrated chip receives the electrical signal and processes it, and then transmits it wirelessly.

7. A health detection method, applied to a health detection sensor customized by the customization method described in any one of claims 1 to 5, 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 amplitude of the signal, and adjust the signal to a standard range; 2) Feature extraction: Extract key features such as peak, 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 the pre-established muscle tension prediction data model, and the parameters m and b of the linear regression model are calculated using the least squares method. 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 evaluation of muscle tension, range of motion, endurance and coordination parameters.

8. 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 5 is implemented.

Images