Flexible triboelectric sensor pulse monitoring system based on intelligent prediction regression network

Abstract

The present application discloses a flexible triboelectric sensor pulse monitoring system based on an intelligent prediction regression network, which belongs to the technical field of flexible triboelectric sensors. The flexible triboelectric sensor includes a PU film layer, an upper friction layer PVA fiber layer, a rubber partition layer, a lower friction layer electrode layer with a corrugated structure, a modified PET base layer and a Cu metal shielding layer, which are sequentially laminated. The rubber partition layer and the PVA fiber layer are located on the same plane and have a rectangular through hole with the same size as the PVA fiber layer. The PVA fiber layer is located on the inner side of the rectangular through hole. The electrode layer with a corrugated structure and the high-performance friction layer can improve the accuracy, sensitivity and response speed of the flexible triboelectric sensor. It has the characteristics of self-powered, non-invasive monitoring and high stability, and can accurately detect the human pulse wave. The system can process and analyze the output data of the flexible triboelectric sensor to monitor the human 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 pulse monitoring system based on an intelligent prediction regression network. Background Art

[0002] Wearable flexible electronic devices, with their advantages in flexibility and portability, have developed rapidly in areas such as motion monitoring and human-computer interaction. Such devices can be directly attached to the surface of the skin or fixed to the body through force rings or straps to collect various types of body information for determining different body movement states and health conditions. The key component of wearable flexible electronic devices is flexible sensors. Flexible triboelectric sensors are converters that directly convert pulsating mechanical signals into corresponding electrical pulse signals. Because they can obtain the physiological information contained in the pulse signal based on the strength and characteristic peaks of the electrical signal, they have become a popular candidate for key components of wearable flexible electronic devices.

[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 single electrode design and low performance of the friction layer of the above-mentioned sensor result in low precision, 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 field of pulse monitoring. Summary of the Invention

[0005] In view of this, the present application provides a flexible triboelectric sensor pulse monitoring system based on an intelligent predictive regression network, which has a sophisticated electrode structure and a high-performance friction layer, can improve the accuracy, sensitivity and response speed of the flexible triboelectric sensor, and process and analyze the output data of the flexible triboelectric sensor to monitor the user's pulse.

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

[0007] In the first aspect, the present application provides a flexible triboelectric sensor, which includes a PU film layer, a PVA fiber layer, a rubber partition layer, an electrode layer, a modified PET base layer and a Cu metal shielding layer that are sequentially bonded together, wherein the electrode layer has a corrugated structure, the PVA fiber layer is the upper friction layer of the flexible triboelectric sensor, the electrode layer is the lower friction layer of the flexible triboelectric sensor, and is also the output electrode of the flexible triboelectric sensor, the rubber 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 the size of the PVA fiber layer, and the PVA fiber layer 2 is located inside the rectangular through hole.

[0008] In some embodiments, the PVA fiber layer is prepared by electrospinning, and the PVA fiber layer has a length of 1 cm, a width of 1 cm, and a thickness of 50-60 μm.

[0009] In some embodiments, the electrode layer is printed on the modified PET base layer by a microelectronic printer. The structure of the electrode layer has multiple concentric rings, the interval between each two concentric rings is 0.6 mm, the width of the concentric rings is 0.4 mm, and there is a circle concentric with the innermost concentric ring in the middle, the diameter of the circle is 2 mm, the center of the circle is extended outward from one side along the length direction of the PVA fiber layer, and the multiple concentric rings and the circle are connected. The width of the extension line is 1 mm, thereby obtaining an electrode layer 4 with a corrugated structure.

[0010] In some embodiments, the material of the electrode layer is any one of Au, Ag and Cu, and the thickness of the electrode layer is 6-10 μm.

[0011] In some embodiments, the PU film layer has a length of 2 cm and a width of 2 cm;

[0012] The length of the rubber partition layer is 2 cm, the width is 2 cm, and the length of the rectangular through hole is 1 cm, the width is 1 cm;

[0013] The modified PET base layer has two parts, the first part of the modified PET base layer has a length of 2 m and a width of 2 cm, and the second part of the modified PET base layer has a length of 4 cm and a width of 1 cm;

[0014] The Cu metal shielding layer has two parts. The first part of the Cu metal shielding layer has a length of 2 cm and a width of 2 cm. The second part of the Cu metal shielding layer has a length of 4 cm and a width of 1 cm.

[0015] In some embodiments, the flexible triboelectric sensor further includes a first PI waterproof layer and a second PI waterproof layer;

[0016] The first PI waterproof layer is laminated above the extension line of the electrode layer, and the length of the first PI waterproof layer is 4 cm and the width is 1 cm;

[0017] The second PI waterproof layer is laminated to the lower surface of the Cu metal shielding layer, and the Cu metal shielding layer is laminated to the lower surface of the modified PET base layer. The second PI waterproof layer has two parts. The first part of the second PI waterproof layer has a length of 2 cm and a width of 2 cm. The second part of the second PI waterproof layer has a length of 4 cm and a width of 1 cm.

[0018] In some embodiments, the center of the PU film layer, the center of the PVA fiber layer, the center of the circle of the electrode layer, the center of the second part of the modified PET base layer, the center of the second part of the Cu metal shielding layer, and the center of the second part of the second PI waterproof layer are all on the same straight line.

[0019] In some embodiments, the flexible triboelectric sensor has a sensitivity of 6.875 V / kPa and a response time of 7.9 ms.

[0020] In a second aspect, the present application provides a method for preparing the flexible triboelectric sensor as described above, the method comprising the following steps:

[0021] Step 1: irradiating a single side of the ultrasonically cleaned, rinsed, and dried PET film with ultraviolet light for 5 to 10 minutes, and trimming the film to obtain a modified PET substrate layer;

[0022] Step 2: printing an electrode layer on the modified PET substrate layer using a metal nanoparticle ink having a surface tension of 26 to 29 mN / m, and performing a drying process to obtain a modified PET substrate layer with an electrode layer;

[0023] Step 3, electrospinning is performed using a 2-4 g / mL PVA solution, and drying is performed after the electrospinning is completed to obtain a PVA fiber layer, wherein the electrospinning needle tube is 5 mL, the nominal needle diameter is 20G, the ambient humidity is 30-40% RH, the spinning voltage is 15-18 kV, the spinning time is 3-4 h, and the ambient temperature is 25-30°C;

[0024] Step 4: Lay the PU film layer, PVA fiber layer, rubber barrier layer, first PI waterproof layer, electrode layer, modified PET base layer, Cu metal shielding layer and second PI waterproof layer in sequence, and connect the outwardly extending end of the electrode layer and one end of the DuPont wire with conductive silver glue.

[0025] In a third aspect, the present application provides a flexible triboelectric sensor pulse monitoring system based on an intelligent prediction regression network, the flexible triboelectric sensor pulse monitoring system based on an intelligent prediction regression network includes the above-mentioned flexible triboelectric sensor, an amplifying and filtering circuit, a control mainboard, a communication module, a signal processing and analysis module, and a terminal, wherein the flexible triboelectric sensor and the amplifying and filtering circuit are connected in sequence, the control mainboard is connected to the filtering circuit, the communication module, and the terminal, and the signal processing and analysis module is loaded on the terminal;

[0026] The flexible triboelectric sensor is used to collect the user's pulse signal including the user's heart rate, blood pressure, augmentation index and reflex index;

[0027] The amplifying and filtering circuit is configured to amplify the user's pulse signal acquired by the flexible triboelectric sensor to obtain an amplified pulse signal, and filter the amplified pulse signal to obtain a filtered pulse signal;

[0028] The control mainboard is used to convert the filtered pulse signal in the form of an analog signal into a filtered pulse signal in the form of a digital signal to obtain a digital pulse signal, and send the digital pulse signal to the signal processing and analysis module;

[0029] The communication module is used to enable the signal processing and analysis module loaded on the terminal to communicate with the control mainboard to send the digital pulse signal to the signal processing and analysis module;

[0030] The signal processing and analysis module is used to process and analyze the digital pulse signal to determine the user's cardiovascular information;

[0031] The terminal is used to display the user's cardiovascular information.

[0032] The beneficial effects of the technical solution provided by this application include at least:

[0033] The present application provides a flexible triboelectric sensor pulse monitoring system based on an intelligent prediction regression network. The flexible triboelectric sensor includes a PU film layer, a PVA fiber layer, a rubber barrier layer, an electrode layer, a modified PET base layer, and a Cu metal shielding layer that are sequentially laminated. The electrode layer has a corrugated structure. The PVA fiber layer is the upper friction layer of the flexible triboelectric sensor. The electrode layer is the lower friction layer of the flexible triboelectric sensor and is also the output electrode of the flexible triboelectric sensor. The rubber barrier layer and the PVA fiber 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 PVA fiber layer, and the PVA fiber layer is located inside the rectangular through hole. By setting up a sophisticated electrode structure and a high-performance friction layer, the accuracy, sensitivity, and response speed of the flexible triboelectric sensor can be improved. It has the characteristics of self-powered, non-invasive monitoring, and high stability. It can accurately detect the pulse wave under the user's superficial skin. The flexible triboelectric sensor pulse monitoring system based on the intelligent prediction regression network can process and analyze the output data of the flexible triboelectric sensor to monitor the user's pulse. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] 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.

[0035] Figure 1 A schematic structural diagram of a flexible triboelectric sensor provided in an embodiment of the present application;

[0036] Figure 2 A schematic structural diagram of the electrode layer of the flexible triboelectric sensor provided in an embodiment of the present application;

[0037] FIG3( a ) is a schematic diagram of the electrical and mechanical characteristics of a flexible triboelectric sensor provided in an embodiment of the present application;

[0038] FIG3( b ) is a schematic diagram of open circuit voltage curves of the flexible triboelectric sensor provided in an embodiment of the present application at different operating frequencies;

[0039] FIG3( c ) is a schematic diagram of the response and recovery time of the flexible triboelectric sensor provided in an embodiment of the present application;

[0040] FIG3( d ) is a schematic diagram of a curve showing a change in open circuit voltage at different distances between the upper and lower friction layers of a flexible triboelectric sensor provided in an embodiment of the present application;

[0041] FIG3( e ) is a schematic diagram of open circuit voltage curves of the flexible triboelectric sensor provided in an embodiment of the present application under different pressures;

[0042] FIG3( f ) is a schematic diagram of the linearity test results of the flexible triboelectric sensor provided in an embodiment of the present application;

[0043] FIG3( g ) is a schematic diagram of the stability test results of the flexible triboelectric sensor provided in an embodiment of the present application;

[0044] FIG4( a ) is a schematic diagram of a 30-second radial artery pulse signal monitored by the flexible triboelectric sensor provided in an embodiment of the present application:

[0045] FIG4( b ) is a partially enlarged schematic diagram of a 30-second radial artery pulse signal monitored by the flexible triboelectric sensor provided in an embodiment of the present application:

[0046] FIG4( c ) is a diagram showing the superposition of each complete pulse wave in a 30-second radial artery pulse signal monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0047] FIG5( a ) is a schematic diagram of a 30-second radial artery pulse signal monitored by a commercial flexible pulse sensor provided in an embodiment of the present application:

[0048] FIG5( b ) is a partially enlarged schematic diagram of a 30-second radial artery pulse signal monitored by a commercial flexible pulse sensor provided in an embodiment of the present application:

[0049] FIG5( c ) is a diagram showing the superposition of each complete pulse wave in a 30-second radial artery pulse signal monitored by a commercial flexible pulse sensor provided by an embodiment of the present application;

[0050] FIG6 (a) is a diagram showing the stability test results of the flexible triboelectric sensor provided in an embodiment of the present application;

[0051] FIG6( b ) is a schematic diagram of the average heart rate of a 90-second radial artery pulse signal monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0052] FIG6( c ) is a schematic diagram of the average enhancement index of a 90-second radial artery pulse signal monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0053] FIG6( d ) is a schematic diagram of the average time difference of a 90-second radial artery pulse signal monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0054] FIG6( e ) is a schematic diagram of the average reflectance index of a 90-second radial artery pulse signal monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0055] FIG6( f ) is a schematic diagram of the average systolic rise time of a 90-second radial artery pulse signal monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0056] FIG7( a ) is a schematic diagram of an internal pulse signal of an elbow monitored by a flexible triboelectric sensor provided in an embodiment of the present application;

[0057] FIG7( b ) is a schematic diagram of a neck pulse signal monitored by a flexible triboelectric sensor provided in an embodiment of the present application;

[0058] FIG8 (a) is a schematic diagram of a pulse monitoring method using a flexible triboelectric sensor according to an embodiment of the present application;

[0059] FIG8( b ) shows the pressure change of the cuff inflation and the corresponding pulse change curve of the user when the flexible triboelectric sensor provided in an embodiment of the present application monitors the pulse;

[0060] FIG9( a ) is a schematic diagram of a user's pulse signal at 8 a.m. monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0061] FIG9( b ) is a schematic diagram of a pulse signal of a user at 10 a.m. monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0062] FIG9( c ) is a schematic diagram of a user's pulse signal at 12 noon monitored by the flexible triboelectric sensor provided by an embodiment of the present application;

[0063] FIG9( d ) is a schematic diagram of a pulse signal of a user at 2 p.m. monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0064] FIG9( e ) is a schematic diagram of a pulse signal of a user at 4 p.m. monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0065] FIG9( f ) is a schematic diagram of a pulse signal of a user at 6 p.m. monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0066] FIG9( g ) is a schematic diagram of a heart rate curve obtained by monitoring a user's pulse signal using the flexible triboelectric sensor provided in an embodiment of the present application;

[0067] FIG9(h) is a schematic diagram of a systolic rise time curve obtained by monitoring a user's pulse signal using the flexible triboelectric sensor provided in an embodiment of the present application;

[0068] FIG9(i) is a schematic diagram of a time difference curve between P1 and P3 obtained based on a user's pulse signal monitored by the flexible triboelectric sensor provided in an embodiment of the present application;

[0069] Figure 10 A scanning electron microscope (SEM) diagram of a PVA fiber layer in the preparation method of a flexible triboelectric sensor provided in an embodiment of the present application;

[0070] FIG11( a ) is a schematic diagram of the structure of a flexible triboelectric sensor pulse monitoring system based on an intelligent prediction regression network provided in an embodiment of the present application;

[0071] FIG11( b ) is a schematic diagram of the structure of a physiological information prediction regression network of a flexible triboelectric sensor pulse monitoring system based on an intelligent prediction regression network provided in an embodiment of the present application;

[0072] FIG12( a ) is a schematic diagram showing the prediction accuracy of the heart rate (HR) by the physiological information prediction regression network of the flexible triboelectric sensor pulse monitoring system based on the intelligent prediction regression network provided in an embodiment of the present application;

[0073] FIG12( b ) is a schematic diagram showing the prediction accuracy of the time difference PPT between P1 and P3 by the physiological information prediction regression network of the flexible triboelectric sensor pulse monitoring system based on the intelligent prediction regression network provided in an embodiment of the present application;

[0074] FIG12( c ) is a schematic diagram showing the prediction accuracy of the reflectance index RI by the physiological information prediction regression network of the flexible triboelectric sensor pulse monitoring system based on the intelligent prediction regression network provided in an embodiment of the present application;

[0075] FIG12( d ) is a schematic diagram showing the prediction accuracy of the systolic rise time UT by the physiological information prediction regression network of the flexible triboelectric sensor pulse monitoring system based on the intelligent prediction regression network provided in an embodiment of the present application;

[0076] Figure 13 This is a schematic diagram of the user pulse monitoring process of the flexible triboelectric sensor pulse monitoring system based on the intelligent prediction regression network provided in an embodiment of the present application. The reference numerals in the figure represent respectively.

[0077] 1-PU film layer, 2-PVA fiber layer, 3-rubber barrier layer, 4-electrode layer, 5-modified PET base layer, 6-Cu metal shielding layer, 7-first PI waterproof layer, 8-second PI waterproof layer. DETAILED DESCRIPTION

[0078] 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.

[0079] 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.

[0080] like Figure 1As shown, the first aspect of the present application provides a flexible triboelectric sensor, which includes a PU film layer 1, a PVA fiber layer 2, a rubber partition layer 3, a waterproof layer 7, an electrode layer 4, a modified PET base layer 5 and a Cu metal shielding layer 6 that are sequentially bonded. The electrode layer 4 has a corrugated structure. The PVA fiber 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 rubber partition layer 3 and the PVA fiber 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 PVA fiber layer, and the PVA fiber layer is located on the inner side of the rectangular through hole.

[0081] The PU film layer 1 is ultra-thin, flexible and skin-friendly, which makes it easy for the flexible friction sensor to directly fit closely with the user's skin through the PU film, thereby enhancing detection accuracy and improving the user experience.

[0082] The rubber barrier layer 3 surrounding the outside of the PVA fiber layer protects the electrode layer 4 and makes the PVA fiber layer 2 fit closely with the first PI waterproof layer 7 and the electrode layer 4, thereby better exhibiting the friction effect.

[0083] In some embodiments, the PVA fiber layer 2 is prepared by electrospinning, and the PVA fiber layer 2 has a length of 1 cm, a width of 1 cm, and a thickness of 50-60 μm.

[0084] The PVA fiber layer 2 located below the PU film layer 1 is prepared by electrospinning, and has the characteristics of being ultra-thin, homogeneous, and dense. Compared with the PVA film in film form, it has a stronger triboelectric effect.

[0085] The modified PET film has high transparency and excellent mechanical properties, which is beneficial to the triboelectric performance of flexible triboelectric sensors.

[0086] An electrode layer 4 with a corrugated structure is provided on the modified PET film, 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 sophisticated corrugated structure improves the triboelectric performance, facilitates subsequent monitoring of the user's health status, and captures tiny signal fluctuations, such as the user's pulse.

[0087] By providing the Cu metal shielding layer 6 , the flexible triboelectric sensor can be made resistant to electromagnetic interference, thereby ensuring stable operation of the flexible triboelectric sensor.

[0088] like Figure 2As shown, in the above-mentioned flexible triboelectric sensor, the electrode layer 4 can be printed on the modified PET base layer 5 by a microelectronic printer. The structure of the electrode layer 4 is a plurality of concentric rings. The interval between each two concentric rings is 0.6 mm, and the width of the concentric rings is 0.4 mm. There is a circle concentric with the innermost concentric ring in the middle, and the diameter of the circle is 2 mm. The center of the circle is extended outward from one side along the length direction of the PVA fiber layer 2, and the plurality of concentric rings and the circle are connected. The width of the extension line is 1 mm, thereby obtaining an electrode layer 4 with a corrugated structure.

[0089] In some embodiments, as Figure 2 As shown, the number of concentric circles can be four.

[0090] In the above-mentioned flexible triboelectric sensor, the material of the electrode layer 4 is any one of Au, Ag and Cu, and the thickness of the electrode layer 4 can be 6-10 μm.

[0091] It should be noted that Ag material has the best conductivity as the material of the electrode layer 4 and can reduce the loss of weak signals during the sensing process.

[0092] In some embodiments, the PU film layer 1 has a length of 2 cm and a width of 2 cm; the rubber partition layer 3 has a length of 2 cm and a width of 2 cm, and the rectangular through hole has a length of 1 cm and a width of 1 cm; the modified PET base layer 5 has two parts, the first part of the modified PET base layer 5 has a length of 2 cm and a width of 2 cm, and the second part of the modified PET base layer 5 has a length of 4 cm and a width of 1 cm; the Cu metal shielding layer 6 has two parts, the first part of the Cu metal shielding layer 6 has a length of 2 cm and a width of 2 cm, and the second part of the Cu metal shielding layer 6 has a length of 4 cm and a width of 1 cm.

[0093] In some embodiments, the flexible triboelectric sensor also includes a first PI waterproof layer 7 and a second PI waterproof layer 8; the first PI waterproof layer 7 is adhered to the upper part of the extension line of the electrode layer 4, and the length of the first PI waterproof layer 7 is 4 cm and the width is 1 cm; the second PI waterproof layer 8 is adhered to the lower surface of the Cu metal shielding layer 6, and the Cu metal shielding layer 6 is adhered to the lower surface of the modified PET base layer 5, and the second PI waterproof layer 8 has two parts, the first part of the second PI waterproof layer 8 is 2 cm long and 2 cm wide, and the second part of the second PI waterproof layer 8 is 4 cm long and 1 cm wide.

[0094] By providing the first PI waterproof layer 7 and the second PI waterproof layer 8, the flexible triboelectric sensor has a waterproof function. When the user sweats or is exposed to humid or rainy environments, the flexible triboelectric sensor can also perform high-performance detection.

[0095] In some embodiments, the center of the PU film layer 1, the center of the PVA fiber layer 2, the center of the circle of the electrode layer 4, the center of the second part of the modified PET base layer 5, the center of the second part of the Cu metal shielding layer 6, and the center of the second part of the second PI waterproof layer 8 are all on the same straight line.

[0096] In some embodiments, the flexible triboelectric sensor has a sensitivity of 6.875 V / kPa and a response time of 7.9 ms.

[0097] With this arrangement, the flexible triboelectric sensor has a compact structure. It utilizes the friction between the electrode layer 4 with a corrugated structure and the PVA fiber layer 2. Through the coordination of the various layers between the flexible triboelectric sensor, the flexible triboelectric sensor has high precision, high sensitivity and fast response speed, and is self-powered, skin-friendly, waterproof and wear-resistant.

[0098] During testing, the flexible triboelectric sensor was divided into two parts: a PU film layer 1, a PVA fiber layer 2, and a rubber barrier layer 3 as the first part, and an electrode layer 4, a modified PET substrate layer, and a Cu metal shielding layer 6 as the second part. The first part was applied to the second part at a specific frequency and pressure, causing triboelectricity to be generated between the PVA fiber layer 2 (the upper friction layer) and the electrode layer 4 (the lower friction layer). Figure 3(a) illustrates the electrical and mechanical characteristics of a flexible triboelectric sensor according to an embodiment of the present application. For example, using Ag as the material for the electrode layer 4, the atomic-level electron cloud potential well model between the Ag electrode and the PVA fiber layer 2 effectively explains the triboelectricity generation process in the flexible triboelectric sensor. d represents the distance between the electrode layer 4 and the PVA fiber layer 2. As shown in Figure 3(b), the open-circuit voltage of the flexible triboelectric sensor was measured at operating frequencies of 1 Hz, 2 Hz, 3 Hz, 4 Hz, and 5 Hz under a pressure of 100 Pa. The results show that the operating frequency has little effect on the open-circuit voltage of the flexible triboelectric sensor. As shown in Figure 3 (c), under the above conditions, the response and recovery time of the flexible triboelectric sensor are both 7.9ms. Due to the change in the distance between the upper and lower friction layers during slapping friction, the flexible triboelectric sensor will exhibit different output capabilities. As can be seen from Figure 3 (d), when the distance is 10mm, the open circuit voltage of the flexible triboelectric sensor is the largest. As shown in Figure 3 (e), when the operating frequency is set to 5Hz, the open circuit voltage of the flexible triboelectric sensor is tested at pressures of 20Pa, 40Pa, 60Pa, 80Pa and 100Pa respectively. As the pressure increases, the open circuit voltage of the flexible triboelectric sensor gradually increases. Figure 3 (f) proves that the flexible triboelectric sensor has an extremely high linear working range under low-amplitude pulse pressure, and the correlation coefficient R 2As high as 0.99, it also proves that the sensitivity in this pressure range reaches 6.875V / kPa. To prove the long-term working stability of the flexible triboelectric sensor, it was subjected to a 3000-cycle test. As shown in Figure 3(g), the open-circuit voltage and short-circuit current of the flexible triboelectric sensor showed extremely strong stability during the 3000-cycle test.

[0099] In some embodiments, a flexible triboelectric sensor may be used to monitor a user's pulse signal.

[0100] To demonstrate the potential and accuracy of flexible triboelectric sensors for pulse signal monitoring, as shown in Figure 4(a), a 31-year-old subject's radial artery pulse wave was monitored using a flexible triboelectric sensor for 30 seconds. The sensor can capture the subject's pulse. As shown in Figure 4(b), a pulse within this 30-second period exhibits three characteristic peaks: the forward wave (P1), the reflected wave (P2), and the dicrotic wave (P3). These three peaks were accurately detected by the flexible triboelectric sensor. To investigate the consistency between waveforms within the same measurement, each complete pulse wave was superimposed simultaneously. The superposition results are shown in Figure 4(c). The average Pearson correlation coefficient (P) between each cycle is 0.951, demonstrating the high consistency of the waveforms obtained by the flexible triboelectric sensor within each cycle. To demonstrate the effectiveness of the flexible triboelectric sensor, a commercially available flexible pulse sensor (NANO POLIS CP301) was used for comparative testing according to the aforementioned principles. As shown in Figure 5(a), a 31-year-old subject's radial artery pulse wave was monitored using a flexible triboelectric sensor for 30 seconds. The commercial flexible pulse sensor captured the subject's pulse. As shown in Figure 5(b), a pulse within this 30-second period exhibited three characteristic peaks: a forward wave (P1), a reflected wave (P2), and a dicrotic wave (P3). Figure 5(c) shows the results of superimposing each complete pulse wave simultaneously. The average Pearson correlation coefficient (P) between each cycle was 0.961. Comparing the flexible triboelectric sensor with a commercially available flexible pulse sensor reveals that the performance of the flexible triboelectric sensor is very close to that of the commercial sensor and that it is self-powered, requiring no external power supply.

[0101] To test the stability of the flexible triboelectric sensor during continuous pulse monitoring, a 31-year-old subject's radial artery pulse wave was monitored for 90 seconds. As shown in Figure 6(a), the flexible triboelectric sensor demonstrated good stability. The pulse signal yielded heart rate (HR), augmentation index (AIr), time difference between P1 and P3 (PPT), reflectivity index (RI), and systolic rise time (UT), as shown in Figures 6(b) to 6(f). Based on the 90-second pulse signal, the subject's average heart rate was 62 beats / min, average AIr was 0.84, average PPT was 0.53 seconds, average RI was 3.22 m / s, and average UT was 0.14 seconds.

[0102] Because the pulse originates from the periodic beating of the heart, superficial pulses in other parts of the body besides the wrist can also be detected. Figures 7(a) and 7(b) show the pulse waveforms at the elbow and neck, respectively, as monitored using a flexible triboelectric sensor. Clear characteristic peaks can be seen.

[0103] To clarify the impact of compression on the upper radial arteries (ARs) on the anterior arteries of the limbs, a flexible triboelectric sensor and a blood pressure monitor were used to measure pulse waveforms from fully open to fully closed and then back to fully open states in the arm artery. The measurement method is shown in Figure 8(a). During measurement, a cuff was first applied to the upper arm, and the triboelectric sensor was then placed on the radial artery. The blood pressure monitor was used to monitor real-time changes in blood pressure. Before the cuff was inflated, the artery was fully open. After the cuff was inflated, the blood pressure monitor measured the blood pressure. The arm artery gradually closed under the cuff pressure and did not reopen until the measurement was completed. Figure 8(b) shows the pressure changes during cuff inflation and the corresponding pulse curve of the test subject. When the cuff pressure reached 56 mmHg, the pulse peak value changed. When the cuff pressure reached 106 mmHg, the flexible triboelectric sensor could no longer detect the pulse. As the cuff pressure reached its peak and gradually decreased, the pulse gradually recovered. When the cuff pressure decreased to 83 mmHg, the flexible triboelectric sensor could detect a weak pulse. When the cuff pressure is reduced to 63 mmHg, the arm artery is fully open and the flexible triboelectric sensor can detect a perfect pulse signal. Therefore, the flexible triboelectric sensor can sense the changes in arterial pulse in real time.

[0104] Pulse changes over time also reflect the user's health status and contribute significantly to the prevention of cardiovascular disease. Figures 9(a) to 9(f) show the pulse signals of a 23-year-old male subject monitored by a flexible triboelectric sensor. The pulse was measured every two hours from 8:00 AM to 6:00 PM. The amplitude of the subject's pulse signal increases slightly near noon and decreases slightly in the afternoon. This is consistent with Traditional Chinese Medicine theory, which states that the heart meridian flows from 11:00 AM to 1:00 PM, during which the pulse is stronger. Figures 9(g) to 9(i) show the corresponding heart rate (HR), systolic rise time (UI), and the time difference between P1 and P3 (PPT). The subject's heart rate was highest at 8:00 AM, with little difference in heart rate during the rest of the day. The UI reached its maximum at 2:00 PM, and the PPT reached its minimum at 12:00 PM.

[0105] In summary, the present application provides a flexible triboelectric sensor, which can improve the accuracy, sensitivity and response speed of the flexible triboelectric sensor by setting up a sophisticated electrode structure and a high-performance friction layer. It has the characteristics of self-powered, non-invasive monitoring and high stability, and has the potential for long-term monitoring of pulse signals.

[0106] In a second aspect, an embodiment of the present application provides a method for preparing the flexible triboelectric sensor described in the first aspect of the present application, the method comprising the following steps:

[0107] Step 1: irradiate one side of the ultrasonically cleaned, rinsed and dried PET film with ultraviolet light for 5 to 10 minutes, and trim it to obtain a modified PET base layer 5.

[0108] It should be noted that single-sided irradiation with ultraviolet light can modify the PET film. The modified PET base layer 5 obtained in step 1 has high transparency and excellent mechanical properties, which is beneficial to the triboelectric properties of the flexible triboelectric sensor.

[0109] In some embodiments, the specific implementation of step 1 can be: a 5 cm × 10 cm rectangle of a 100 μm thick PET film is rolled up and placed in a beaker, and an appropriate amount of ethanol solution is poured into it; the beaker is sealed with plastic wrap and placed in an ultrasonic cleaning machine, and the surface of the PET film is ultrasonically cleaned for 30 minutes; the cleaned PET film is taken out of the beaker, and then repeatedly rinsed with deionized water for 3 times, and placed in a vacuum drying oven and dried at 60°C for 1 hour; the obtained PET film is spread flat in the chamber of an ultraviolet light cleaning machine, and one side of the PET film is irradiated with ultraviolet light for modification for 5 minutes, and then cut to obtain a modified PET base layer 5.

[0110] Step 2: Print the electrode layer 4 on the modified PET base layer 5 using metal nanoparticle ink with a surface tension of 26-29 mN / m, and perform drying treatment to obtain the modified PET base layer 5 with the electrode layer 4.

[0111] The electrode layer 4 printed with metal nanoparticle ink having a surface tension of 26-29 mN / m has good tensile properties.

[0112] Preferably, in the above preparation method, the metal nanoparticle ink in step 2 may be Ag nanoparticle ink.

[0113] In some embodiments, a specific implementation of step 2 may be as follows: using metal nanoparticle ink with a surface tension of 26-29 mN / m and a DP500 microelectronic printer (model: Sigma-Aldrich 901083-25ML), first printing an electrode layer 4 having multiple rings and a circle on the modified PET substrate layer 5; after completion, placing the modified PET substrate layer 5 having the electrode layer 4 in a 60° C. vacuum drying oven for 1 hour; placing the resulting product in its original position on the DP500 printing table, and printing an electrode from the center of the circle along the length direction of the PVA fiber layer 2 outward from one side, so that the electrode connects the multiple concentric rings and the circle; and then placing the modified PET substrate layer 5 in a 60° C. vacuum drying oven for 1 hour to obtain the modified PET substrate layer 5 having the electrode layer 4.

[0114] In some embodiments, in order to facilitate detection or connection with other devices and instruments, a DuPont wire is connected to the end of the strip electrode of the electrode layer 4 using conductive silver glue, thereby leading to a detection end.

[0115] Step 3: Electrospinning is performed using a 2-4 g / mL PVA solution. After the electrospinning is completed, the PVA fiber layer 2 is dried to obtain the PVA fiber layer 2. The electrospinning needle tube is 5 mL, the nominal needle diameter is 20G, the ambient humidity is 30-40% RH, the spinning voltage is 15-18 kV, the spinning time is 3-4 h, and the ambient temperature is 25-30 ° C.

[0116] By setting the relevant parameters of electrospinning in this way, the obtained PVA fiber layer 2 can be made ultra-thin, homogeneous and dense, and has a stronger triboelectric effect than the PVA film in the form of a film. Figure 10 As shown, the fiber diameter of the PVA fiber layer 2 is 200~500nm.

[0117] In the above preparation method, the specific implementation method of step 3 can be: add 2g of PVA particles to 20mL of deionized water, place the mixture in a water bath at 90°C and magnetically stir for 3h, take out the beaker after completion and wait for the temperature to drop to 36°C and maintain it; take 5mL of the obtained viscous solution into a syringe, the nominal value of the needle caliber is 20G, the ambient humidity is 30%RH, the spinning voltage is 18kV, the spinning time is set to 4h, and the ambient temperature is 25°C; the result is placed in a vacuum drying oven at 60°C for 1h to obtain the PVA fiber layer 2.

[0118] It should be noted that the above-mentioned PVA particles are from Shanghai MacLean Biochemical Technology Co., Ltd.

[0119] Step 4: Lay the PU film layer 1, PVA fiber layer 2, rubber barrier layer 3, first PI waterproof layer 7, electrode layer 4, modified PET base layer 5, Cu metal shielding layer 6 and second PI waterproof layer 8 in sequence, and connect the outwardly extending end of the electrode layer 4 and one end of the DuPont wire with conductive silver glue.

[0120] In the above preparation method, the specific implementation method of step 4 can be: use a rubber isolation layer 3 to isolate the electrode layer 4 on all sides; apply a first PI waterproof layer 7 on the upper surface of the rubber isolation layer 3; adhere the PVA fiber layer 2 to the rubber isolation layer 3, and adhere the PU film layer 1 to the upper surface of the PVA fiber layer 2; adhere the modified PET base layer 5 with the electrode layer 4 to the first PI waterproof layer 7, so that the electrode layer 4 and the first PI waterproof layer 7 are tightly adhered; adhere a Cu metal shielding layer 6 with the same size specifications on the lower surface of the modified PET base layer 5 to resist electromagnetic interference; and adhere a second PI waterproof layer 8 on the lower surface of the Cu metal shielding layer 6.

[0121] In some embodiments, the first PI waterproof layer 7 and the second PI waterproof layer 8 may be waterproof sealants.

[0122] In some embodiments, the Cu metal shielding layer 6 may be a copper foil.

[0123] In summary, the PVA fiber layer 2 obtained by the preparation method of the flexible triboelectric sensor provided in the embodiment of the present application is ultra-thin, homogeneous, dense and has a good triboelectric effect. The prepared flexible triboelectric sensor can capture the user's pulse and reflect the user's physical condition.

[0124] On the third aspect, the embodiment of the present application provides a flexible triboelectric sensor pulse monitoring system based on an intelligent prediction regression network, as shown in FIG11 (a), the flexible triboelectric sensor pulse monitoring system based on an intelligent prediction regression network includes a flexible triboelectric sensor, an amplifying and filtering circuit, a control mainboard, a communication module, a signal processing and analysis module and a terminal, the flexible triboelectric sensor and the amplifying and filtering circuit are connected in sequence, the control mainboard is connected to the filtering circuit, the communication module and the terminal, and the signal processing and analysis module is loaded on the terminal; the flexible triboelectric sensor is used to collect the user's pulse signal including the user's heart rate, blood pressure, enhancement index and reflectivity index; the amplifying and filtering circuit is used to The user's pulse signal obtained by the flexible triboelectric sensor is amplified to obtain an amplified pulse signal, and the amplified pulse signal is filtered to obtain a filtered pulse signal; the control mainboard is used to convert the filtered pulse signal in the form of an analog signal into a filtered pulse signal in the form of a digital signal to obtain a digital pulse signal, and send the digital pulse signal to the signal processing module; the communication module is used to enable the signal processing and analysis module loaded on the terminal to communicate with the control mainboard to send the digital pulse signal to the signal processing and analysis module; the signal processing and analysis module is used to process and analyze the digital pulse signal to determine the user's cardiovascular information; the terminal is used to display the user's cardiovascular information.

[0125] In some embodiments, the amplification and filtering circuit may include an LM358 amplifier to amplify the user pulse signal obtained by the flexible triboelectric sensor to obtain an amplified pulse signal, and may also include an OP07 filter to filter the amplified pulse signal to obtain a filtered pulse signal.

[0126] In some embodiments, the cutoff frequency of the amplification and filtering circuit can be as low as 6 Hz to meet the requirements of amplification and filtering of low-frequency pulse signals.

[0127] It should be noted that the user's pulse signal is a low-frequency signal, and the cutoff frequency of the amplification and filtering circuit has a low-frequency requirement. A frequency as low as 6 Hz can ensure the amplification and filtering of the user's pulse signal.

[0128] In some embodiments, the control mainboard may be an STM32F407 single chip microcomputer.

[0129] In some embodiments, a signal processing and analysis module compiled using Labview can be loaded on the terminal to communicate with the control mainboard.

[0130] In some embodiments, the signal processing and analysis module may include a deep learning CNN model programmed in Python within the Tensorflow system to process and analyze the digital pulse signal to determine the user's cardiovascular information.

[0131] In some embodiments, the signal processing and analysis module includes a characteristic peak search algorithm and a physiological information prediction regression network. When the digital pulse signal is transmitted to the signal processing and analysis module through the communication module, the characteristic peak search algorithm will cut and mark the characteristic peaks of the pulse, and then store these data as an Excel file and transmit them to the physiological information prediction regression network. The network can extract four physiological information from this information: heart rate (HR), time difference (PPT) between P1 and P3, reflectivity index (RI) and systolic rise time (UT).

[0132] Figure 11(b) shows the structure of the physiological information prediction regression network, which consists of six convolutional layers, two fully connected layers, three maximum pooling layers, and a linear regression layer. The specific training process for the physiological information prediction regression network is as follows: First, pulse data from three users is prepared, totaling 300 complete pulse data points. This data is divided into a training set, a validation set, and a test set, with the three data sets accounting for 60%, 20%, and 20%, respectively. The network is trained using the training set. After the 200th round of training, the network's loss function value reaches an extremely low level. The network is then verified and tested using the validation and test sets. When the loss function converges at a satisfactory rate, the final physiological information prediction regression network is obtained.

[0133] Figures 12(a) through 12(d) show the prediction accuracy of the physiological information prediction regression network for HR, PPT, RI, and UT, respectively. 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 0.5, demonstrating the effectiveness of this physiological information prediction regression network in deeply mining cardiovascular information.

[0134] In some embodiments, the communication module can be integrated on the control motherboard.

[0135] Figure 13 A schematic diagram of a user pulse monitoring process of a flexible triboelectric sensor pulse monitoring system based on an intelligent prediction regression network provided in an embodiment of the present application is shown in FIG. Figure 13 As shown, the flexible triboelectric sensor collects the user's pulse signal, which is amplified and filtered by the amplification and filtering circuit and then received by the control motherboard integrated with the communication module and converted into a digital pulse signal. The control motherboard sends the digital pulse signal to the signal processing and analysis module on the terminal, and the terminal finally displays the user's cardiovascular information.

[0136] The flexible triboelectric sensor pulse monitoring system based on the intelligent predictive regression network provided in the embodiment of the present application can monitor the user's pulse and process and analyze the user's pulse data, thereby providing a reliable means for the user's cardiovascular care and effectively realizing the mining and analysis of cardiovascular information.

[0137] 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.

[0138] 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.

[0139] 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. A flexible triboelectric sensor, characterized in that: The flexible triboelectric sensor comprises a PU film layer (1), a PVA fiber layer (2), a rubber partition layer (3), an electrode layer (4), a modified PET base layer (5) and a Cu metal shielding layer (6) which are sequentially laminated. The electrode layer (4) has a corrugated structure. The PVA fiber 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 rubber partition layer (3) and the PVA fiber 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 PVA fiber layer (2). The PVA fiber layer (2) is located inside the rectangular through hole. The PVA fiber layer (2) is prepared by electrospinning, and the PVA fiber layer (2) has a length of 1 cm, a width of 1 cm, and a thickness of 50-60 μm; The electrode layer (4) is printed on the modified PET base layer (5) by a microelectronic printer. The electrode layer (4) has a plurality of concentric rings. The interval between each two concentric rings is 0.6 mm. The width of the concentric rings is 0.4 mm. A circle concentric with the innermost concentric ring is located in the middle. The diameter of the circle is 2 mm. The center of the circle is extended outward from one side along the length direction of the PVA fiber layer (2). The plurality of concentric rings and the circle are connected. The width of the extension line is 1 mm, thereby obtaining an electrode layer (4) with a corrugated structure. The flexible triboelectric sensor also includes a first PI waterproof layer (7). and a second PI waterproof layer (8); the first PI waterproof layer (7) is laminated above the extension line portion of the electrode layer (4), the first PI waterproof layer (7) has a length of 4 cm and a width of 1 cm; the second PI waterproof layer (8) is laminated on the lower surface of the Cu metal shielding layer (6), the Cu metal shielding layer (6) is laminated on the lower surface of the modified PET base layer (5), the second PI waterproof layer (8) has two parts, the first part of the second PI waterproof layer (8) has a length of 2 cm and a width of 2 cm, and the second part of the second PI waterproof layer (8) has a length of 4 cm and a width of 1 cm.

2. The flexible triboelectric sensor according to claim 1, characterized in that The material of the electrode layer (4) is any one of Au, Ag and Cu, and the thickness of the electrode layer (4) is 6-10 μm.

3. The flexible triboelectric sensor according to claim 1, characterized in that The PU film layer (1) has a length of 2 cm and a width of 2 cm; The length of the rubber partition layer (3) is 2 cm, and the width is 2 cm; the length of the rectangular through hole is 1 cm, and the width is 1 cm; The modified PET base layer (5) has two parts, the first part of the modified PET base layer (5) has a length of 2 cm and a width of 2 cm, and the second part of the modified PET base layer (5) has a length of 4 cm and a width of 1 cm; The Cu metal shielding layer (6) has two parts, the first part of the Cu metal shielding layer (6) has a length of 2 cm and a width of 2 cm, and the second part of the Cu metal shielding layer (6) has a length of 4 cm and a width of 1 cm.

4. The flexible triboelectric sensor according to claim 1, characterized in that The center of the PU film layer (1), the center of the PVA fiber layer (2), the center of the circle of the electrode layer (4), the center of the second part of the modified PET base layer (5), the center of the second part of the Cu metal shielding layer (6) and the center of the second part of the second PI waterproof layer (8) are all on the same straight line.

5. The flexible triboelectric sensor according to claim 1, characterized in that: The flexible triboelectric sensor has a sensitivity of 6.875 V / kPa and a response time of 7.9 ms.

6. A method for preparing a flexible triboelectric sensor according to any one of claims 1 to 5, characterized in that: The method comprises the following steps: Step 1, irradiating a single side of the PET film after ultrasonic cleaning, rinsing and drying with ultraviolet light for 5 to 10 minutes, and trimming the film to obtain a modified PET base layer (5); Step 2: printing an electrode layer (4) on the modified PET substrate layer (5) using a metal nanoparticle ink having a surface tension of 26-29 mN / m, and performing a drying process to obtain a modified PET substrate layer (5) with the electrode layer (4); Step 3, electrospinning with a 2-4 g / mL PVA solution, followed by drying to obtain a PVA fiber layer (2), wherein the electrospinning needle is 5 mL, the nominal needle diameter is 20G, the ambient humidity is 30-40% RH, the spinning voltage is 15-18 kV, the spinning time is 3-4 h, and the ambient temperature is 25-30°C; Step 4: Lay the PU film layer (1), the PVA fiber layer (2), the rubber barrier layer (3), the first PI waterproof layer (7), the electrode layer (4), the modified PET base layer (5), the Cu metal shielding layer (6), and the second PI waterproof layer (8) in sequence, and connect the outwardly extending end of the electrode layer (4) and one end of the DuPont wire with conductive silver glue.

7. A flexible triboelectric sensor pulse monitoring system based on an intelligent prediction regression network, characterized in that: The flexible triboelectric sensor pulse monitoring system based on the intelligent prediction regression network comprises the flexible triboelectric sensor according to any one of claims 1 to 5, an amplifying and filtering circuit, a control mainboard, a communication module, a signal processing and analysis module, and a terminal, wherein the flexible triboelectric sensor and the amplifying and filtering circuit are connected in sequence, the control mainboard is connected to the filtering circuit, the communication module, and the terminal, and the signal processing and analysis module is loaded on the terminal; The flexible triboelectric sensor is used to collect the user's pulse signal including the user's heart rate, blood pressure, augmentation index and reflex index; The amplifying and filtering circuit is configured to amplify the user's pulse signal acquired by the flexible triboelectric sensor to obtain an amplified pulse signal, and filter the amplified pulse signal to obtain a filtered pulse signal; The control mainboard is used to convert the filtered pulse signal in the form of an analog signal into a filtered pulse signal in the form of a digital signal to obtain a digital pulse signal, and send the digital pulse signal to the signal processing and analysis module; The communication module is used to enable the signal processing and analysis module loaded on the terminal to communicate with the control mainboard to send the digital pulse signal to the signal processing and analysis module; The signal processing and analysis module is used to process and analyze the digital pulse signal to determine the user's cardiovascular information. The signal processing and analysis module includes a physiological information prediction regression network, which includes six convolutional layers, two fully connected layers, three maximum pooling layers, and a linear regression layer. The terminal is used to display the user's cardiovascular information.

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