Flexible triboelectric sensor for human vital signs detection, preparation method thereof, and intelligent detection method

Abstract

The present application discloses a flexible triboelectric sensor for detecting human vital signs, as well as its preparation method and intelligent detection method, belonging to the technical field of flexible triboelectric sensors. The sensor comprises a translucent silicone film layer, a multi-walled carbon nanotube composite sandpaper-modified silicone layer, a foam barrier layer, a conductive fabric electrode layer, a PET base layer, and a metal shielding layer, which are sequentially laminated. The electrode layer has a fabric structure, the multi-walled carbon nanotube composite sandpaper-modified silicone layer serves as the upper friction layer of the flexible triboelectric sensor, and the electrode layer serves as the lower friction layer of the flexible triboelectric sensor. The sensor has a rectangular through-hole, the size of which is the same as that of the multi-walled carbon nanotube composite sandpaper-modified silicone layer, and the multi-walled carbon nanotube composite sandpaper-modified silicone layer is located inside the rectangular through-hole. The sensor has a flexible electrode layer and a high-performance friction layer, which can improve accuracy, sensitivity, and response speed, and can recognize the user's posture, breathing, gestures, and pulse.

Description

Technical Field

[0001] The present application relates to the technical field of flexible triboelectric sensors, and in particular to a flexible triboelectric sensor for detecting human vital signs, a preparation method thereof, and an intelligent detection method thereof. Background Art

[0002] Wearable flexible electronic devices, owing to their unique advantages in flexibility and portability, have made significant progress in recent years in areas such as motion monitoring and human-computer interaction. These devices can be directly attached to the skin or secured to various body parts via force rings or straps, enabling real-time collection of a wide range of body information. This information can be used not only to determine an individual's motion status but also to effectively assess their health. Flexible sensors are a core component of wearable flexible electronic devices, with flexible triboelectric sensors being particularly important. These sensors can directly convert many mechanical signals generated by the human body into corresponding electrical pulses, offering unique advantages in extracting physiological information. By analyzing the intensity and characteristic peaks of electrical signals, they can deeply understand the human posture and physiological information contained in the signals, making them an indispensable key component in wearable flexible electronic devices. With the continuous advancement of technology, the application scope of these devices is also expanding, and they are expected to play an even more important role in various fields such as healthcare, sports, and health monitoring.

[0003] CN104779832A discloses a method for using a fluorocarbon plasma treatment process to form micro-nano structures on the polymer surface to increase the roughness of the friction material, thereby improving the electrical output performance of the triboelectric sensor. CN104167949A discloses a heating and concave-convex embossing treatment technology to obtain a polymer film layer with a concave-convex structure.

[0004] However, the sensing performance of the friction layer of the above-mentioned sensor is not high, resulting in low accuracy, poor sensitivity, and slow response speed of the flexible triboelectric sensor. In addition, the above-mentioned technical solution lacks processing and analysis of output data, which is not conducive to its application in the fields of human posture perception, breathing and pulse monitoring. Summary of the Invention

[0005] In view of this, the present application provides a flexible triboelectric sensor for detecting human vital signs, a preparation method thereof, and an intelligent detection method. It has a flexible electrode layer and a high-performance friction layer, which can improve the accuracy, sensitivity and response speed of the flexible triboelectric sensor, and can analyze the data collected by the flexible triboelectric sensor and identify the user's posture, breathing, gesture and pulse.

[0006] Specifically, the following technical solutions are included:

[0007] In the first aspect, the present application provides a flexible triboelectric sensor for detecting human vital signs, wherein the flexible triboelectric sensor includes a translucent silicone film layer, a multi-walled carbon nanotube composite sandpaper modified silicone layer, a foam partition layer, a conductive fabric electrode layer, a PET base layer and a metal shielding layer, which are sequentially bonded together. The electrode layer has a fabric structure, the multi-walled carbon nanotube composite sandpaper modified silicone layer is the upper friction layer of the flexible triboelectric sensor, and the electrode layer is the lower friction layer of the flexible triboelectric sensor. The foam partition layer and the electrode layer are located on the same plane and have a rectangular through hole. The size of the rectangular through hole is the same as that of the multi-walled carbon nanotube composite sandpaper modified silicone layer, and the multi-walled carbon nanotube composite sandpaper modified silicone layer is located on the inner side of the rectangular through hole.

[0008] In some embodiments, the multi-walled carbon nanotube composite sandpaper modified silica gel layer has a length of 1 to 3 cm, a width of 1 to 1.5 cm, and a thickness of 60 to 120 μm.

[0009] In some embodiments, the conductive fabric electrode layer is made of a cloth soaked in any one of Au, Ag and Cu conductive metal slurries, the cloth is any one of polyester, cotton and cotton-spandex blends, and the conductive fabric electrode layer has a length of 2~6 cm, a width of 1~2 cm, and a thickness of 80~130 μm.

[0010] In some embodiments, the translucent silicone film layer, the PET base layer, and the metal shielding layer are each 2 to 6 cm long and 1 to 2 cm wide;

[0011] The thickness of the translucent silicone film layer is 60-120 μm;

[0012] The length of the foam partition layer is 2-6 cm, the width is 1-2 cm, and the length of the rectangular through hole is 1-3 cm, the width is 1-1.5 cm;

[0013] The thickness of the PET base layer is 180 μm to 220 μm;

[0014] The thickness of the metal shielding layer is 80 μm to 120 μm.

[0015] In some embodiments, the flexible triboelectric sensor further includes a PVC waterproof layer, which is adhered to the lower surface of the metal shielding layer. The size of the PVC waterproof layer is completely determined by the metal shielding layer, and is generally 2 to 6 cm in length and 1 to 2 cm in width.

[0016] In some embodiments, the center of the translucent silicone film layer, the center of the multi-walled carbon nanotube composite sandpaper modified silicone layer, the center of the electrode layer, the center of the PET base layer, the center of the metal shielding layer and the center of the PVC waterproof layer are all on the same straight line.

[0017] In some embodiments, the flexible triboelectric sensor has a response time of 42 ms.

[0018] In a second aspect, the present application provides a method for preparing a flexible triboelectric sensor for detecting human vital signs, the method comprising the following steps:

[0019] Step 1: drop silica gel with a hardness of 0 degrees on the center of the spin disk of a spin coater, and perform spin coating. The spin coater is set to a rotation speed of three revolutions per second, and the spin coating is performed for 30 to 40 seconds. After drying for 3 to 3.5 hours, the semi-transparent silica gel film layer is obtained by cutting.

[0020] Step 2: Place the fabric electrode substrate under the screen of the screen printer, and apply the conductive metal slurry on the screen with a drop amount of 1 mL each time. Wait until the conductive metal slurry fully penetrates into the fiber gaps of the fabric electrode substrate before applying the next drop. Repeat the drop application 3 to 6 times to obtain the electrode layer.

[0021] Step 3: 0.2 g to 0.4 g of multi-walled carbon nanotubes are added to 20 g to 40 g of silica gel with a hardness of 20 degrees and stirred to obtain a silica gel solution mixed with the multi-walled carbon nanotubes; one side of sandpaper with a mesh size of 1200 to 3600 is attached to the center of the upper surface of the rotating disk of the spin coater, and 3 to 4 mL of the silica gel solution mixed with the multi-walled carbon nanotubes is drop-coated on the upper surface of the sandpaper. The rotation speed of the spin coater is then set to 3 to 5 revolutions per second, and the spin coating is performed for 30 to 40 seconds; finally, the rotating disk is removed, and the silica gel layer modified with the multi-walled carbon nanotube composite sandpaper is obtained after drying for 4 to 4.5 hours.

[0022] Step 4: Lay the translucent silicone film layer, the multi-walled carbon nanotube composite sandpaper-modified silicone layer, the foam partition layer, the conductive fabric electrode layer, the PET base layer, the metal shielding layer, and the PVC waterproof layer in sequence, and connect one side of the conductive fabric electrode layer and one end of the DuPont wire with conductive silver glue to obtain a flexible triboelectric sensor.

[0023] In a third aspect, the present application provides an intelligent detection method.

[0024] The intelligent detection method is applied to an intelligent detection system including the flexible triboelectric sensor for detecting human vital signs as described in the first aspect, wherein the intelligent detection system further includes a signal analysis module. The intelligent detection method includes:

[0025] The flexible triboelectric sensor for human vital signs detection collects vital sign signals including the user's main body joint movements, five finger joint movements, breathing movements and pulse heart rate, and sends the vital sign signals to the signal analysis module;

[0026] The signal analysis module receives the vital sign signal and determines the user's posture, gesture, breathing pattern and pulse rate based on the vital sign signal.

[0027] In some embodiments, determining the user's posture, gesture, breathing pattern, and pulse rate based on the vital sign signal includes:

[0028] The preprocessed vital sign signal is input into the recognition network, and the output includes the recognition results of the user's posture, gesture, breathing pattern and pulse rate. The recognition network has an MLP-Mixer architecture, including a one-dimensional convolutional layer, a normalization layer, a mixed spatial information layer, a mixed position feature layer, a global average pooling layer and a fully connected layer.

[0029] The beneficial effects of the technical solution provided by this application include at least:

[0030] The present application provides a flexible triboelectric sensor for detecting human vital signs, a preparation method thereof, and an intelligent detection method. The flexible triboelectric sensor includes a translucent silicone film layer, a multi-walled carbon nanotube composite sandpaper-modified silicone layer, a foam barrier layer, a conductive fabric electrode layer, a PET substrate layer, and a metal shielding layer, which are sequentially laminated. The electrode layer has a fabric structure, the multi-walled carbon nanotube composite sandpaper-modified silicone layer serves as the upper friction layer of the flexible triboelectric sensor, and the electrode layer serves as the lower friction layer of the flexible triboelectric sensor and also serves as the output electrode of the flexible triboelectric sensor. The foam barrier layer and the multi-walled carbon nanotube composite sandpaper-modified silicone layer are located on the same plane and have a rectangular through-hole. The size of the rectangular through-hole is the same as that of the multi-walled carbon nanotube composite sandpaper-modified silicone layer, and the multi-walled carbon nanotube composite sandpaper-modified silicone layer is located inside the rectangular through-hole. By providing an electrode structure with fabric micro-convexities and a high-performance friction layer, the accuracy, sensitivity, and response speed of the flexible triboelectric sensor can be improved. The flexible triboelectric sensor has the characteristics of self-powered, non-invasive monitoring, and high stability, and can recognize the user's posture, breathing, gestures, and pulse. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 A schematic diagram of the structure of a flexible triboelectric sensor for detecting human vital signs provided in an embodiment of the present application;

[0033] FIG2( a ) is a schematic diagram of electrical characteristics of a flexible triboelectric sensor for detecting human vital signs provided by an embodiment of the present application;

[0034] FIG2( b ) is a schematic diagram of the wearing position of the flexible triboelectric sensor for human vital signs detection provided by an embodiment of the present application when performing respiratory behavior detection;

[0035] FIG2( c ) is a schematic diagram of the response and recovery time of a flexible triboelectric sensor for detecting human vital signs provided by an embodiment of the present application;

[0036] FIG2( d ) is a schematic diagram of the distribution range of sensor output voltages corresponding to nine types of breathing behaviors detected by the flexible triboelectric sensor for human vital sign detection provided by an embodiment of the present application during a breathing behavior test;

[0037] FIG2( e ) is a schematic diagram of sensor output signal waveforms corresponding to nine types of breathing behaviors detected by the flexible triboelectric sensor for human vital sign detection provided by an embodiment of the present application during a breathing behavior test;

[0038] FIG2( f ) is a schematic diagram of specific joint positions that need to be detected when the flexible triboelectric sensor for human vital signs detection provided by an embodiment of the present application detects major joint movements of the human body;

[0039] FIG2( g ) is a schematic diagram of gestures and corresponding output waveforms of the flexible triboelectric sensor for human vital sign detection provided by an embodiment of the present application when performing different gesture digital tests;

[0040] FIG3 (a) is a schematic diagram of a 10-second radial artery pulse signal detected by the flexible triboelectric sensor for human vital sign detection provided by an embodiment of the present application during a pulse test;

[0041] FIG3( b ) is a schematic diagram of an embodiment of the present application showing eight flexible triboelectric sensors for detecting human vital signs integrated into a shoe-pad-type sensor array;

[0042] FIG3( c ) is a schematic diagram of the pressure distribution felt by the insole-type sensor array composed of flexible triboelectric sensors provided in an embodiment of the present application when a subject is walking normally;

[0043] Figure 4 A flow chart of a method for preparing a triboelectric sensor provided in an embodiment of the present application;

[0044] FIG5( a ) is a schematic diagram of the structure of the intelligent detection method provided in an embodiment of the present application;

[0045] FIG5( b ) is a confusion matrix of the intelligent detection method provided in an embodiment of the present application on a training set when classifying nine respiratory states;

[0046] FIG5( c ) is a confusion matrix of the intelligent detection method provided in an embodiment of the present application on a test set when classifying nine respiratory states;

[0047] FIG5( d ) is a curve showing the accuracy change of the intelligent detection method provided in an embodiment of the present application when performing classification training for nine respiratory states;

[0048] FIG5( e ) is a curve showing the change in the loss function of the intelligent detection method provided in an embodiment of the present application when performing classification training for nine respiratory states;

[0049] FIG6 (a) is a confusion matrix of the intelligent detection method provided in an embodiment of the present application on a training set when classifying 11 joint motion features;

[0050] FIG6 (b) is a confusion matrix of the intelligent detection method provided in an embodiment of the present application on a test set when classifying 11 joint motion features;

[0051] FIG6 (c) is a curve showing the accuracy change of the intelligent detection method provided in an embodiment of the present application when performing classification training on 11 joint motion features;

[0052] FIG6 (d) is a curve showing the change in the loss function of the intelligent detection method provided in an embodiment of the present application when performing classification training on 11 joint motion features;

[0053] FIG7 (a) is a confusion matrix of the intelligent detection method provided in an embodiment of the present application on a training set when classifying ten gesture digits;

[0054] FIG7( b ) is a confusion matrix of the intelligent detection method provided in an embodiment of the present application on a test set when classifying ten gesture digits;

[0055] FIG7 (c) is a curve showing the accuracy change of the intelligent detection method provided by an embodiment of the present application when performing classification training on ten gesture digits;

[0056] FIG7( d ) is a curve showing the change in the loss function of the intelligent detection method provided in an embodiment of the present application when performing classification training on ten gesture digits;

[0057] FIG8 (a) is a schematic diagram showing the prediction accuracy of the heart rate HR by the intelligent detection method provided in an embodiment of the present application;

[0058] FIG8( b ) is a schematic diagram showing the prediction accuracy of the time difference PPT between the first characteristic peak and the third characteristic peak of the pulse wave according to the intelligent detection method provided in an embodiment of the present application;

[0059] FIG8 (c) is a schematic diagram showing the prediction accuracy of the pulse reflex index RI by the intelligent detection method provided in an embodiment of the present application;

[0060] FIG8( d ) is a schematic diagram showing the prediction accuracy of the intelligent detection method provided in an embodiment of the present application for the systolic rise time UT;

[0061] FIG8( e ) is a loss function curve of the intelligent detection method provided in an embodiment of the present application during training for predicting the four characteristic physiological signals of the pulse;

[0062] FIG9 (a) is a confusion matrix of the intelligent detection method provided in an embodiment of the present application on a training set when identifying eight types of human walking postures;

[0063] FIG9( b ) is a confusion matrix of the intelligent detection method provided by an embodiment of the present application on a test set when identifying eight types of human walking postures;

[0064] FIG9( c ) is a curve showing the accuracy change of the intelligent detection method provided in an embodiment of the present application when performing recognition training for eight types of human walking postures;

[0065] FIG9( d ) is a curve showing the change in the loss function of the intelligent detection method provided in an embodiment of the present application when performing recognition training for eight types of human walking postures.

[0066] The reference numerals in the figures represent respectively:

[0067] 1- translucent silicone film layer, 2- multi-walled carbon nanotube composite sandpaper modified silicone layer, 3- foam insulation layer, 4- conductive fabric electrode layer, 5- PET base layer, 6- metal shielding layer, 7- PVC waterproof layer. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0069] In order to make the technical solutions and advantages of the present application clearer, the implementation methods of the present application will be described in further detail below with reference to the accompanying drawings.

[0070] like Figure 1As shown, the first aspect of the present application provides a flexible triboelectric sensor for detecting human vital signs, the flexible triboelectric sensor comprising a translucent silicone film layer 1, a multi-walled carbon nanotube composite sandpaper modified silicone layer 2, a foam partition layer 3, a conductive fabric electrode layer 4, a PET base layer 5 and a metal shielding layer 6 which are sequentially bonded. The electrode layer 4 has a fabric structure, the multi-walled carbon nanotube composite sandpaper modified silicone layer 2 is the upper friction layer of the flexible triboelectric sensor, the electrode layer 4 is the lower friction layer of the flexible triboelectric sensor, and is also the output electrode of the flexible triboelectric sensor, the foam partition layer 3 and the multi-walled carbon nanotube composite sandpaper modified silicone layer are located on the same plane, and have a rectangular through hole. The size of the rectangular through hole is the same as that of the multi-walled carbon nanotube composite sandpaper modified silicone layer, and the multi-walled carbon nanotube composite sandpaper modified silicone layer is located on the inner side of the rectangular through hole.

[0071] The translucent silicone film layer 1 is flexible and skin-friendly, which facilitates the flexible friction sensor to directly fit closely with the user's skin through the translucent silicone film, thereby enhancing detection accuracy and improving the user experience.

[0072] The foam barrier layer 3 surrounding the outer side of the multi-walled carbon nanotube composite sandpaper modified silicone layer protects the electrode layer 4 and makes the multi-walled carbon nanotube composite sandpaper modified silicone layer 2 fit closely with the electrode layer 4, thereby better exhibiting the friction effect.

[0073] In some embodiments, the multi-walled carbon nanotube composite sandpaper modified silica gel layer 2 can be prepared by physical doping, magnetic stirring and spin coating methods. The multi-walled carbon nanotube composite sandpaper modified silica gel layer 2 has a length of 1 to 3 cm, a width of 1 to 1.5 cm, and a thickness of 60 to 120 μm.

[0074] The multi-walled carbon nanotube composite sandpaper-modified silicone layer 2 located below the translucent silicone film layer 1 is prepared by physical doping, magnetic stirring and spin coating methods. It has the characteristics of ultra-thinness, homogeneity and density. Compared with pure silicone film, the multi-walled carbon nanotube composite sandpaper-modified silicone layer 2 has a stronger triboelectric effect.

[0075] The PET substrate layer 5 has high transparency and excellent mechanical properties, which is beneficial to the triboelectric performance of the flexible triboelectric sensor.

[0076] An electrode layer 4 with a fabric structure is provided on the PET base layer 5, which serves as the lower friction layer of the flexible triboelectric sensor and also acts as the output electrode of the flexible triboelectric sensor. The unique surface micro-convex structure of the fabric is utilized to improve the triboelectric performance, facilitating subsequent monitoring of various joint movements and pulse conditions of the user.

[0077] By providing the metal shielding layer 6 , the flexible triboelectric sensor can be made resistant to electromagnetic interference, thereby ensuring stable operation of the flexible triboelectric sensor.

[0078] In some embodiments, the electrode layer 4 is made of fabric soaked in a conductive metal slurry selected from Au, Ag, and Cu. The fabric is selected from polyester, cotton, and a cotton-spandex blend. The conductive fabric electrode layer 4 has a length of 2-6 cm, a width of 1-2 cm, and a thickness of 80-130 μm. It should be noted that fabric soaked in a conductive metal slurry selected from Au, Ag, and Cu has the best conductivity as the material for the electrode layer 4 and can reduce the loss of weak signals during the sensing process.

[0079] In some embodiments, the length of the translucent silicone film layer 1, the PET base layer 5 and the metal shielding layer 6 are all 2~6 cm and the width is 1~2 cm; the thickness of the translucent silicone film layer 1 is 60~120 μm; the length of the foam partition layer 3 is 2~6 cm and the width is 1~2 cm; the length of the rectangular through hole is 1~3 cm and the width is 1~1.5 cm; the thickness of the PET base layer 5 is 180μm~220μm; the thickness of the metal shielding layer 6 is 80μm~120μm.

[0080] In some embodiments, the flexible triboelectric sensor further includes a PVC waterproof layer 7, which is attached to the lower surface of the metal shielding layer 6. The dimensions of the PVC waterproof layer 7 are the same as those of the metal shielding layer 6. The PVC waterproof layer 7 has a length of 2 to 6 cm, a width of 1 to 2 cm, and a thickness of 130 μm to 150 μm.

[0081] By providing the PVC waterproof layer 7, the flexible triboelectric sensor has a waterproof function. When the user is exposed to humid, rainy and other environments, the flexible triboelectric sensor can also perform high-performance detection.

[0082] In some embodiments, the center of the translucent silicone film layer 1, the center of the multi-walled carbon nanotube composite sandpaper modified silicone layer 2, the center of the electrode layer 4, the center of the PET base layer 5, the center of the metal shielding layer 6 and the center of the PVC waterproof layer 7 are all on the same straight line.

[0083] In some embodiments, the fastest response time of the flexible triboelectric sensor is 42 ms.

[0084] With this arrangement, the flexible triboelectric sensor has a compact structure. It utilizes the friction between the electrode layer 4 with a fabric structure and the multi-walled carbon nanotube composite sandpaper-modified silicone layer 2. Through the coordination of the various layers between the flexible triboelectric sensors, the flexible triboelectric sensor has high precision, high sensitivity, and fast response speed. It has the characteristics of self-powered, skin-friendly, waterproof, and wear-resistant.

[0085] During testing, the flexible triboelectric sensor was divided into two parts: a translucent silicone film layer 1, a multi-walled carbon nanotube composite sandpaper-modified silicone layer 2, and a foam barrier layer 3 as the first part, and an electrode layer 4, a PET base layer 5, and a metal shielding layer 6 as the second part. The first part was applied to the second part at a specific frequency and pressure, thereby generating frictional charge between the multi-walled carbon nanotube composite sandpaper-modified silicone layer 2 (the upper friction layer) and the electrode layer 4 (the lower friction layer). Figure 2(a) shows the electrical characteristics of a flexible triboelectric sensor for human vital sign detection, as provided in an embodiment of the present application. For example, using Ag and cotton as the material for the electrode layer 4, the maximum output voltage generated between the electrode layer and the multi-walled carbon nanotube composite sandpaper-modified silicone layer 2 was approximately 175V, and the current was approximately 27μA. Figure 2(b) shows the sensor wearing method and position during a respiratory behavior test. As shown in Figure 2(c), under these conditions, the response and recovery times of the flexible triboelectric sensor were 42ms and 97ms, respectively. Figure 2(d) shows that deep, moderate, and shallow breathing intensities differ significantly across the nine breathing patterns: deep-fast (DF), deep-moderate (DM), deep-slow (DS), moderate-fast (MF), moderate-moderate (MM), moderate-slow (MS), shallow-fast (SF), shallow-moderate (SM), and shallow-slow (SS). Figure 2(e) shows the output waveforms of the flexible triboelectric sensor for these nine breathing patterns. Figure 2(f) illustrates the specific locations detected by the flexible triboelectric sensor for motion detection of major human joints. Figure 2(g) shows a typical signal waveform output by the flexible triboelectric sensor when used for digital gesture detection.

[0086] In some embodiments, the flexible triboelectric sensor can be used to monitor the user's pulse signal. As shown in FIG3 (a), the flexible triboelectric sensor monitors the radial artery pulse wave of a 31-year-old tester for 10 seconds. It can be seen that the flexible triboelectric sensor can capture the pulse beat of the tester.

[0087] In some embodiments, flexible triboelectric sensors, when integrated into an insole-type sensor array, can detect a person's walking status. Figure 3(b) shows a cross-sectional view of the sensor array, which incorporates eight flexible triboelectric sensors. Figure 3(c) illustrates the pressure distribution felt by a 31-year-old subject during normal walking. The flexible triboelectric sensors at different locations clearly differentiate the pressure distribution.

[0088] In summary, the flexible triboelectric sensor for detecting human vital signs provided in the embodiment of the present application has a flexible electrode layer and a high-performance friction layer, which can improve the accuracy, sensitivity and response speed of the flexible triboelectric sensor, and can identify the user's posture, breathing, gestures and pulse.

[0089] Second, as Figure 4 As shown, the embodiment of the present application provides a method for preparing the flexible triboelectric sensor according to the first aspect of the present application, the method comprising the following steps:

[0090] Step 1: Drop silica gel with a hardness of 0 degrees on the center of the spin coater's spin disk for spin coating. Set the spin coater's rotation speed to three revolutions per second for 30 to 40 seconds. Dry for 3 to 3.5 hours and then cut to obtain a translucent silica gel film layer 1.

[0091] The rotation speed of the spin coater has a decisive influence on the thickness of the final silicone film. The translucent silicone film layer 1 obtained in step 1 has excellent mechanical properties, which is beneficial to the triboelectric properties of the flexible triboelectric sensor.

[0092] Step 2: Place the fabric electrode substrate under the screen of the screen printer, and drip the conductive metal slurry on the screen with a dripping amount of 1 mL each time. Wait until the conductive metal slurry fully penetrates into the fiber gaps of the fabric electrode substrate before dripping again. Repeat the dripping 3 to 6 times to obtain the electrode layer 4.

[0093] In some embodiments, cotton cloth is selected as the base fabric of the fabric electrode to achieve better triboelectric performance.

[0094] In some embodiments, the conductive metal paste in (2) can be Ag nanoparticle ink.

[0095] Step 3: add 0.2g~0.4g of multi-walled carbon nanotubes to 20g~40g of silica gel with a hardness of 20 degrees and stir to obtain a silica gel solution mixed with the multi-walled carbon nanotubes; then, attach one side of sandpaper with a mesh size of 1200~3600 to the center of the upper surface of the rotating disk of the spin coater, and drop 3~4mL of the silica gel solution mixed with the multi-walled carbon nanotubes on the upper surface of the sandpaper. Then, set the rotation speed of the spin coater to 3~5 revolutions per second, and spin coat for 30~40s; finally, remove the rotating disk, dry for 4~4.5 hours, and then cut to obtain the multi-walled carbon nanotube composite sandpaper modified silica gel layer 2.

[0096] By setting the relevant parameters for preparing the mixed silica gel in this way, the obtained multi-walled carbon nanotube composite sandpaper-modified silica gel layer 2 can be made homogeneous and dense, and has a stronger triboelectric effect than a pure silica gel film without surface modification.

[0097] Step 4: Lay the translucent silicone film layer 1, the multi-walled carbon nanotube composite sandpaper-modified silicone layer 2, the foam barrier layer 3, the conductive fabric electrode layer 4, the PET base layer 5, the metal shielding layer 6, and the PVC waterproof layer 7 in sequence, and connect one side of the conductive fabric electrode layer 4 and one end of the DuPont wire with conductive silver glue to obtain a flexible triboelectric sensor.

[0098] In some embodiments, the preparation method may specifically include:

[0099] (1) Drop 0-degree silica gel on the center of the spin coater, set the spin coater's rotation speed to three revolutions per second, spin coat for 30 seconds, dry for 3 hours, and then cut to obtain a translucent silica gel film layer 1.

[0100] (2) The fabric electrode substrate is placed under the screen of a screen printer, and a conductive metal slurry is dripped onto the screen at a rate of 1 mL each time. The next dripping is performed after the conductive metal slurry fully penetrates into the fiber gaps of the fabric electrode substrate each time. The dripping is repeated three times to obtain an electrode layer 4.

[0101] In some embodiments, the specific implementation of (2) can be: using metal Ag nanoparticle ink with a surface tension of 26-29 mN / m and a screen printer to prepare a conductive layer on a cotton cloth with a thickness of 70-120 μm, and then placing the conductive layer in a vacuum drying oven at 60°C for 1 hour to obtain the final electrode layer 4.

[0102] In some embodiments, in order to facilitate detection or connection with other devices and instruments, a DuPont wire is connected to one side of the electrode layer 4 using conductive silver glue, thereby leading to a detection end.

[0103] (3) 0.2 g of multi-walled carbon nanotubes was added to 20 g of silica gel with a hardness of 20 degrees and stirred to obtain a silica gel solution mixed with multi-walled carbon nanotubes; then one side of a 1200-mesh sandpaper was attached to the center of the upper surface of the spin coater's rotating disk, and 3 mL of the silica gel solution mixed with multi-walled carbon nanotubes was dripped on the upper surface of the sandpaper. Then, the rotation speed of the spin coater was set to three revolutions per second, and the spin coating was performed for 30 seconds; finally, the spin coater was removed and placed in a ventilated and dry place for 4 hours to obtain a multi-walled carbon nanotube composite sandpaper modified silica gel layer 2.

[0104] (4) The translucent silicone film layer, the multi-walled carbon nanotube composite sandpaper modified silicone layer, the foam insulation layer, the conductive fabric electrode layer, the PET base layer, the metal shielding layer and the PVC waterproof layer are laminated in sequence, and one side of the conductive fabric electrode layer and one end of the DuPont wire are connected with conductive silver glue.

[0105] In the above preparation method, the specific implementation method of (4) can be: the electrode layer 4 is isolated on all sides by a foam isolation layer 3; a layer of waterproof glue is applied on the upper surface of the foam isolation layer 3; the side of the multi-walled carbon nanotube composite sandpaper-modified silicone layer 2 that is not modified by sandpaper is first attached to the center of the translucent silicone film layer 1, and then the translucent silicone film is attached to the foam isolation layer 3; the electrode layer 4 is attached to the center of the PET base layer 5, and then the metal shielding layer 6 is attached to the outside of the PET base; finally, a PVC waterproof layer 7 is attached to the lower surface of the metal shielding layer 6, and one side of the conductive fabric electrode layer 4 and one end of the DuPont line are connected with conductive silver glue to obtain a flexible triboelectric sensor.

[0106] In some embodiments, the metal shielding layer 6 may be brass foil, aluminum foil, or tin foil.

[0107] In summary, the preparation method of the flexible triboelectric sensor for human vital signs detection provided in the embodiment of the present application can enable the flexible triboelectric sensor to have a flexible electrode layer and a high-performance friction layer, which can improve the accuracy, sensitivity and response speed of the flexible triboelectric sensor, thereby enabling the flexible triboelectric sensor to recognize the user's posture, breathing, gestures and pulse.

[0108] In the third aspect, in order to realize the analysis and extraction of the user's motion posture, breathing pattern and pulse physiological information, the embodiment of the present application provides an intelligent detection method. Figure 4 As shown, the method is applied to an intelligent detection system including a flexible triboelectric sensor for detecting human vital signs as in the first aspect, the intelligent detection system further comprising a signal analysis module;

[0109] Flexible triboelectric sensors for human vital sign detection collect vital sign signals including the user's main joint movements, five finger joint movements, breathing movements, and pulse heart rate, and send the vital sign signals to the signal analysis module;

[0110] The signal analysis module receives the vital sign signal and determines the user's posture, gesture, breathing pattern and pulse rate based on the vital sign signal.

[0111] In some embodiments, determining a user's posture, gesture, breathing pattern, and pulse rate based on vital sign signals includes: inputting the preprocessed vital sign signals into a recognition network, and outputting recognition results including the user's posture, gesture, breathing pattern, and pulse rate, see Figure 5 (a), the recognition network has an MLP-Mixer architecture, including a one-dimensional convolutional layer, a normalization layer, a mixed spatial information layer, a mixed position feature layer, a global average pooling layer, and a fully connected layer.

[0112] It should be noted that the recognition network can perform the classification task of the user's breathing pattern, posture and gesture, and can perform the pulse rate recognition task, thereby obtaining recognition results including the user's posture, gesture, breathing pattern and pulse rate.

[0113] In the task of classifying nine breathing patterns (i.e., the user's breathing pattern classification task), each breathing pattern has 200 complete data sets, with one set representing a complete breathing action, for a total of 1800 data sets. When training the recognition network, all data was split into a training set and a test set in a 7:3 ratio. After 50 training epochs, the confusion matrix for the recognition network on the training set, shown in Figure 5(b), achieved 100% accuracy. The confusion matrix for the test set, shown in Figure 5(c), achieved 99.26% accuracy. Figures 5(d) and 5(e) show the accuracy and loss function curves for the nine breathing state classification tasks. As can be seen, after 25 epochs of training, the recognition network achieved high accuracy on both the training and test sets, with similarly low loss functions. This demonstrates the high specificity of the recognition network for this type of classification task. Subsequent modifications to this network were made for the classification of user posture, gesture, and pulse rate, as well as regression prediction tasks.

[0114] In the task of classifying 11 joint motion features (i.e., user posture classification), 200 data sets were collected for each joint's motion, with one set representing a complete motion cycle for that joint, resulting in a total of 2,200 data sets. After appropriately modifying the input channel dimensions of the recognition network previously used for breathing pattern classification, the 11 joint motion features can be classified. All data is still divided into a training set and a test set in a 7:3 ratio. After 50 training epochs, the confusion matrix of the recognition network on the training set, shown in Figure 6(a), achieved an accuracy of 95.7%, and the confusion matrix on the test set, shown in Figure 6(b), achieved an accuracy of 97.69%. Figures 6(c) and 6(d) show the accuracy and loss function curves of the recognition network when training the 11 joint motion features. As can be seen from the figures, after 20 epochs of training, the recognition network achieved high accuracy on both the training and test sets, with a similarly low loss function.

[0115] In the task of classifying 10 digit gestures (i.e., user gesture classification), 200 sets of data were collected for each finger movement corresponding to each digit gesture. One set of data represents a complete cycle of the gesture, resulting in a total of 2,000 sets of data. After appropriately modifying the input channel dimensions of the aforementioned recognition network, classification of the 10 digit gestures was performed. All data was still divided into a training set and a test set in a 7:3 ratio. After 50 training rounds, the confusion matrix of the recognition network on the training set achieved 100% accuracy, as shown in Figure 7(a). The confusion matrix on the test set achieved 99.83% accuracy, as shown in Figure 7(b). Figures 7(c) and 7(d) show the accuracy and loss function curves of the recognition network during training for the 10 digit gesture classification. As can be seen from the figures, after only 8 rounds of training, the network achieved high accuracy on both the training and test sets, while also achieving a very low loss function.

[0116] In the task of extracting physiological information from pulse signals (i.e., identifying a user's pulse rate), a total of 220 pulse data sets were collected from three individuals, each representing a complete pulse cycle. After revising the aforementioned recognition network to a predictive regression model, all data were divided into a training set and a test set in a 7:3 ratio. The network ultimately performed prediction analysis for heart rate (HR), the time difference (PPT) between the progressive wave (P1) and the dicrotic wave (P3), the reflectivity index (RI), and the systolic rise time (UT). Figures 8(a) to 8(d) show the recognition network's prediction accuracy for HR, PPT, RI, and UT. All data points are closely distributed around the straight line X=Y. MAE denotes mean absolute error, and RMSE denotes root mean square error. With the exception of HR, where the error is greater than 1, the errors for the other three parameters are all less than 0.2, demonstrating the effectiveness of the recognition network in deeply mining cardiovascular information. Figure 8(e) shows the loss function curve of the recognition network when it is trained to predict the four characteristic physiological signals of the pulse. It can be seen from the figure that after 5 rounds of training, the loss function of the recognition network on the training set and the test set has reached a very low level.

[0117] When classifying eight different human motion states (i.e., user posture classification tasks), 200 data sets were collected for each motion state, with one set representing a complete motion cycle for that state, resulting in a total of 1,600 data sets. After appropriately modifying the input channel dimensions of the recognition network, classification of the eight motion states was performed. All data was still divided into a training set and a test set in a 7:3 ratio. After 50 training epochs, the confusion matrix of the recognition network on the training set, shown in Figure 9(a), achieved an accuracy of 97.51%. The confusion matrix on the test set, shown in Figure 9(b), achieved an accuracy of 93.44%. Figures 9(c) and 9(d) show the accuracy and loss function curves of the recognition network during training for the eight motion states. As can be seen from the figures, after 20 epochs of training, the recognition network achieved high accuracy on both the training and test sets, while also achieving a very low loss function.

[0118] In summary, the intelligent detection method provided in the embodiment of the present application can enable the flexible triboelectric sensor to identify the user's posture, breathing, gesture and pulse. The flexible triboelectric sensor has the characteristics of self-powered, non-invasive monitoring and high stability, and can improve the recognition accuracy when combined with the recognition network.

[0119] In this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The term "plurality" refers to two or more than two, unless expressly limited otherwise.

[0120] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the present invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only.

[0121] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. Flexible triboelectric sensor for human vital signs detection, characterized by: The flexible triboelectric sensor comprises a translucent silicone film layer (1), a multi-walled carbon nanotube composite sandpaper modified silicone layer (2), a foam isolation layer (3), a conductive fabric electrode layer (4), a PET base layer (5) and a metal shielding layer (6) which are sequentially laminated. The conductive fabric electrode layer (4) has a cross-vertical and cross-horizontal structure of a fabric. The multi-walled carbon nanotube composite sandpaper modified silicone layer (2) is an upper friction layer of the flexible triboelectric sensor. The conductive fabric electrode layer (4) is a lower friction layer of the flexible triboelectric sensor. The foam isolation layer (3) and the multi-walled carbon nanotube composite sandpaper modified silicone layer (2) are located on the same plane and have a rectangular through hole. The size of the rectangular through hole is the same as that of the multi-walled carbon nanotube composite sandpaper modified silicone layer (2). The multi-walled carbon nanotube composite sandpaper modified silicone layer (2) is located inside the rectangular through hole. The multi-walled carbon nanotube composite sandpaper modified silica gel layer (2) has a length of 1 to 3 cm, a width of 1 to 1.5 cm, and a thickness of 60 to 120 μm; The conductive fabric electrode layer (4) is made of a cloth soaked in any one of Au, Ag and Cu conductive metal slurries, and the cloth is any one of polyester, cotton and cotton-spandex blends. The conductive fabric electrode layer (4) has a length of 2 to 6 cm, a width of 1 to 2 cm and a thickness of 80 to 130 μm. The flexible triboelectric sensor further comprises a PVC waterproof layer (7); the PVC waterproof layer (7) is arranged in contact with the lower surface of the metal shielding layer (6), and the size of the PVC waterproof layer (7) is the same as that of the metal shielding layer (6); The center of the translucent silicone film layer (1), the center of the multi-walled carbon nanotube composite sandpaper modified silicone layer (2), the center of the conductive fabric electrode layer (4), the center of the PET base layer (5), the center of the metal shielding layer (6) and the center of the PVC waterproof layer (7) are all on the same straight line.

2. The flexible triboelectric sensor for detecting human vital signs according to claim 1, characterized in that: The translucent silicone film layer (1), the PET base layer (5) and the metal shielding layer (6) are all 2 to 6 cm in length and 1 to 2 cm in width; The thickness of the translucent silicone film layer (1) is 60-120 μm; The length of the foam insulation layer (3) is 2-6 cm, the width is 1-2 cm, the length of the rectangular through hole is 1-3 cm, and the width is 1-1.5 cm; The thickness of the PET base layer (5) is 180 μm to 220 μm; The thickness of the metal shielding layer (6) is 80 μm to 120 μm.

3. The flexible triboelectric sensor for detecting human vital signs according to claim 1, characterized in that: The fastest response time of the flexible triboelectric sensor is 42ms.

4. A method for preparing a flexible triboelectric sensor for detecting human vital signs according to any one of claims 1 to 3, characterized in that: The method comprises the following steps: Step 1, drop silica gel with a hardness of 0 degrees on the center of the spin disk of a spin coater, and perform spin coating. The spin coater's rotation speed is set to three revolutions per second, and the spin coating is performed for 30 to 40 seconds. After drying for 3 to 3.5 hours, the semi-transparent silica gel film layer (1) is obtained by cutting. Step 2: Place the fabric electrode substrate under the screen of a screen printer, and apply a conductive metal slurry on the screen with a drop amount of 1 mL each time. After each drop of the conductive metal slurry fully penetrates into the fiber gaps of the fabric electrode substrate, the next drop is applied. Repeat the drop application 3 to 6 times to obtain a conductive fabric electrode layer (4). Step 3, adding 0.2g~0.4g of multi-walled carbon nanotubes into 20g~40g of silica gel with a hardness of 20 degrees and stirring to obtain a silica gel solution mixed with the multi-walled carbon nanotubes; then sticking one side of sandpaper with a mesh size of 1200~3600 mesh to the center of the upper surface of the rotating disk of the spin coater, dripping 3~4mL of the silica gel solution mixed with the multi-walled carbon nanotubes on the upper surface of the sandpaper, then setting the rotation speed of the spin coater to 3~5 revolutions per second, and spin coating for 30~40s; finally, removing the rotating disk, drying for 4~4.5h, and then cutting to obtain a multi-walled carbon nanotube composite sandpaper modified silica gel layer (2); Step 4: laminate the translucent silicone film layer (1), the multi-walled carbon nanotube composite sandpaper modified silicone layer (2), the foam barrier layer (3), the conductive fabric electrode layer (4), the PET base layer (5), the metal shielding layer (6) and the PVC waterproof layer (7) in sequence, and connect one side of the conductive fabric electrode layer (4) and one end of the DuPont wire with conductive silver glue to obtain a flexible triboelectric sensor.

5. An intelligent detection method, characterized in that: The intelligent detection method is applied to an intelligent detection system including a flexible triboelectric sensor for detecting human vital signs as described in any one of claims 1 to 3, wherein the intelligent detection system further includes a signal analysis module. The intelligent detection method includes: The flexible triboelectric sensor for detecting human vital signs collects vital sign signals including body joint movements, five finger joint movements, breathing movements, and pulse heart rate of the user, and sends the vital sign signals to the signal analysis module; The signal analysis module receives the vital sign signal and determines the user's posture, gesture, breathing pattern and pulse rate based on the vital sign signal.

6. The intelligent detection method according to claim 5, characterized in that: Determining the user's posture, gesture, breathing pattern, and pulse rate based on the vital sign signals includes: The preprocessed vital sign signal is input into the recognition network, and the output includes the recognition results of the user's posture, gesture, breathing pattern and pulse rate. The recognition network has an MLP-Mixer architecture, including a one-dimensional convolutional layer, a normalization layer, a mixed spatial information layer, a mixed position feature layer, a global average pooling layer and a fully connected layer.

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