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Advances in flexible triboelectric pressure sensor for physiological signal monitoring

Tianxiao Peng1, §, Lanyue Shen1, §, Zhenqiu Gao1, §, Zhengdi Sima3, Azamat Yeshmukhametov2, Gulnur Kalimuldina2, Yina Liu3, and Zhen Wen2,

+ Author Affiliations

 Corresponding author: Yina Liu, yina.liu@xjtlu.edu.cn; Zhen Wen, wenzhen2011@suda.edu.cn

DOI: 10.1088/1674-4926/26050029CSTR: 32376.14.1674-4926.26050029

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Abstract: Real-time physiological signal monitoring transforms health management from 'passive treatment' to 'active prevention' and from 'vague perception' to 'precise control'. Triboelectric pressure sensing technology exhibits inherent advantages including high sensitivity, diverse material selection, and superior dynamic performance. It is highly compatible with flexible substrates, thus demonstrating enormous application potential in the monitoring of human physiological signals as well as human-computer interaction. In this review, we present the types and the advantages and disadvantages of pressure sensors. In addition, the basic principles and preparation methods of triboelectric pressure sensors are introduced, and their research progress in the field of human signal detection is summarized, including their applications in pulse signal monitoring, sleep monitoring, and gait detection. Finally, the constraints on the practical application of triboelectric pressure sensors are discussed. This review is expected to provide readers with some general strategies and novel ideas for conducting in-depth research on sensors with better performance for physiological signal recognition.

Keywords: pressure sensor, triboelectric nanogenerator, flexible wearable electronics, physiological signal monitoring



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Fig. 1.  (Color online) The working mechanism of four types of pressure sensors[26]. Copyright 2022, Royal Society of Chemistry.

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Fig. 2.  Theoretical model of dielectric contact mode triboelectric sensor.

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Fig. 3.  (Color online) The mechanism of the vertical contact-separation mode triboelectric sensor.

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Fig. 4.  (Color online) Examples of contact separation mode triboelectric sensor. (a) Contact separation triboelectric pressure sensor based on insulation material[72]. Copyright 2024, Wiley-VCH. (b) Stacked series contact separation triboelectric pressure sensor[73]. Copyright 2023, Elsevier. (c) Spiral fiber contact separation triboelectric pressure sensor[74]. Copyright 2022, American Chemical Society.

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Fig. 5.  (Color online) The mechanism of the sliding mode triboelectric sensor.

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Fig. 6.  (Color online) Examples of sliding mode triboelectric sensor. (a) Schematic diagram of a triboelectric sensor capable of simultaneously detecting phase force and shear force. (b) The signal difference on the electrode when the device detects forces in different directions. (c) Schematic diagram of simultaneous testing of normal and shear forces on the device. (d) Display of physical images of the device[75]. Copyright 2018, Wiley-VCH.

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Fig. 7.  The mechanism of a single-electrode mode triboelectric sensor. (Color online)

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Fig. 8.  (Color online) Examples of single electrode mode triboelectric sensor. (a) Schematic diagram of a single electrode triboelectric sensor in an electronic tongue[85]. Copyright 2024, Springer Nature. (b) Schematic diagram of the “electronic tongue” device for identifying different liquids. (c) The operating principle of a single electrode triboelectric sensor based on ion PDMS[86]. Copyright 2022, Wiley-VCH. (d) Physical display of device position recognition under normal and tensile conditions. (e) When the device is stretched, the voltage signal at both ends of the sensor is measured when touching the positions of TP # 1 to TP # 7.

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Fig. 9.  (Color online) The mechanism of a free-standing triboelectric-layer mode triboelectric sensor.

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Fig. 10.  (Color online) Examples of independent layer mode triboelectric sensor. (a) Triboelectric pressure sensor based on independent layer mode of turntable structure[87]. Copyright 2023, Wiley-VCH. (b) Quality inspection of ferromagnetic metal particles during rotation. (c) Triboelectric pressure sensor based on the independent layer mode of forked electrodes[88]. Copyright 2023, Elsevier. (d) Typical output waveform after releasing the liquid column after two presses. There are three stages: (i) the first stage, (ii) the intermediate stage, and (iii) the return stage.

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Fig. 11.  (Color online) Template-assisted method for triboelectric sensor manufacturing. (a) Fabrication process of the silicone rubber replica[96]. Copyright 2021, Elsevier. (b) Optical images of silicone rubber at different tilt angles. (c) Force analysis of tactile sensors. (d) Simulated stress-strain results of sensors with 30° and 90° under the same pressure. (e) Hot pressing sandpaper on the tribo-layer to obtain microstructures[98]. Copyright 2017, Elsevier. (f) Schematic diagram of the triboelectric sensor and SEM image of sandpaper. (g) Corresponding sensitivity curve of the sensor. (h) Schematic of the design and fabrication of a GTPS with a gradient height micro cone array[101]. Copyright 2023, Elsevier. (i) Gradient response of the height gradient during force application. (j) Performance comparison between different triboelectric pressure sensors.

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Fig. 12.  (Color online) Material synthesis method for triboelectric sensor manufacturing. (a) Schematic of the fabrication process and molecular structure of the poly-DADMAC/nylon-11 nanofiber mat[110]. Copyright 2021, Elsevier. (b) Variation in the peak–peak voltage of the TENG with different concentrations of poly-DADMAC. (c) PAGCA with abundant hydrogen bonding[114]. Copyright 2024, Elsevier. (d) PAGCA-based TENG at different temperatures of –30 °C and 30 °C. (e) Output voltages of the original and self-healed PAGCA-based TENGs.

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Fig. 13.  (Color online) Textile technology for triboelectric sensor manufacturing. (a) Schematic diagram of various textile methods, including stitched, woven, and knitted methods[123]. Copyright 2020, Elsevier. (b) Performance of devices prepared by different textile methods. (c) The preparation process of the negative Poisson's ratio yarn[124]. Copyright 2021, RSC Publishing. (d) Physical image of woven fabric with a negative Poisson's ratio. (e) Relationship between Poisson's ratio and the tensile strength of the materials. (f) Performance of the strain sensors with negative Poisson's ratio materials. (g) Schematic diagram of the textile triboelectric sensor for pulse testing[125]. Copyright 2021, Wiley-VCH. (h) Pulse signal measured by the textile triboelectric sensor.

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Fig. 14.  (Color online) 3D printing technology for triboelectric sensor manufacturing. (a) Schematic diagram of the fabrication of the rGO microstructure-based triboelectric pressure sensor (rGO-TPS)[126]. Copyright 2022, Elsevier. (b) Photograph of a cylindrical rGO-TPS with a hollow spacer. (c) Sensing performance of the rGO TPS. (d) A scheme showing the 3D printing of TENG components[127]. Copyright 2023, Elsevier. (e) High-magnification SEM micrographs of the pellet surface. (f) Typical open-circuit voltage waveforms measured at the bottom of the ultrasonic bath tank. (g) An MXene-based pressure sensor (M-PS)[128]. Copyright 2022, Elsevier. (h) Side-view SEM image of an MXene film. (i) Open circuit voltage of the MTENG under various applied forces. (j) Schematic diagram of 3D-pTENG preparation[129]. Copyright 2021, Wiley-VCH. (k) Schematic of the 3D structure of a 3D-pTENG. (l) VOC of a 3D-pTENG under a wide range of pressures.

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Fig. 15.  (Color online) MEMS technology for triboelectric sensor manufacturing. (a) The detailed fabrication process of the microstructured triboelectric ultrasonic device (μTUD)[135]. Copyright 2020, Springer Nature. (b) Optical microscope image and (c) selected area enlarged image of the μTUD units. (d) Enlarged images of the cross-sectional view of the μTUD cavity. (e) Relationship between the peak open-circuit voltage and the incident acoustic pressure for the μTUD. (f) Flowchart of the MEMS-TENG fabrication process. (g) Structural and detailed drawings of the MEMS-TENG. (h) MEMS-TENG output at f = 2 kHz and different shaker inputs[136]. Copyright 2022, Elsevier. (i) Peak-to-peak output voltage of the MEMS-TENG against varying base acceleration[137]. Copyright 2023, Wiley-VCH. (j) Characterization of the MEMS-TENG in harmonic base excitation[138]. Copyright 2023, Elsevier.

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Fig. 16.  (Color online) Pulse sensor based on triboelectric effect. (a) The schematic diagram of the pulse sensor inspired by Paper Cuttings[149]. Copyright 2022, Wiley-VCH. (b) The wireless cardiovascular monitoring system measures pulse waves (scale, 2 cm) under a pre-stress of 2.6 kPa. (c) Pulse waveforms measured under different prestressing pressures. (d) Real time arterial pulse signals measured by sensors during sitting and walking. (e) A cardiovascular monitoring system based on textile triboelectric sensors[125]. Copyright 2021, Wiley-VCH. (f) Structural schematic diagram of textile triboelectric sensor. (g) SEM image of layered CNTs fabric. Scale: 300 µm. (h) Schematic diagram of fabric triboelectric sensor responding to radial artery pulse. (i) The customized app displays cardiovascular status on the mobile interface.

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Fig. 17.  (Color online) Application of triboelectric pressure sensor in sleep monitoring. (a) Schematic design of health monitoring system[150]. Copyright 2018, American Chemical Society. (b) HM-SPS for motion tracking and health monitoring. (c) The digital image of HM-SPS below the subject for health monitoring. (d) The electrical response of HM-SPS to continuous motion. (e) The signal of two complete respiratory cycles. (f) The signal of two heartbeat cycles when holding your breath.

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Fig. 18.  (Color online) Application of triboelectric pressure sensor in gait monitoring. (a) Schematic diagram of foot pressure sensing unit[151]. Copyright 2024, Wiley-VCH. (b) The overall structure diagram of the smart insole. (c) The signal output of intelligent insoles at different gait stages. (d) Continuous distribution of foot pressure during walking for subjects wearing smart insoles. (e) A confusion matrix of 1820 pressure signals.

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Table 1.   Comparison of applications, required materials, advantages and disadvantages of four pressure sensing technologies.

Sensing technology Applications Advantages Disadvantages
Piezoresistive Robot tactile sensing; electronic skin; haptic feedback system High sensitivity allows for the detection of minor pressure changes; excellent linearity facilitates simple signal processing; highly integrated temperature drift; limited long-term stability; weak high-frequency response capability
Capacitive Human-computer interaction touch screen; tactile array sensing Fast response speed; low power consumption; wide detection range.
 Vulnerable to electromagnetic interference; linearity decreases with increasing pressure; sensitive to humidity
Piezoelectric Drone obstacle avoidance tactile sensing; Energy harvesting sensor No external power supply required; excellent high-frequency response; resistant to harsh environments Unable to measure static force; weak output signal; piezoelectric materials are brittle, making processing difficult
Triboelectric Wearable device energy harvesting; intelligence Insole gait analysis No external power supply required; materials are easily obtainable and low cost; high sensitivity Poor signal stability; the internal resistance of the device is extremely high, affecting the output amplitude
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Table 2.   Principles, advantages, disadvantages, and application comparison of four triboelectric sensing technologies.

Characteristic Vertical contact-separation mode Sliding mode Single-electrode mode Free-standing triboelectric-layer mode
Working Principle surface charges drive electron back-and-forth flow Horizontal polarization drives electron flow Charged object approaches/leaves the grounded electrode Charged independent layer moves between two fixed electrodes
Number of electrodes Two electrodes Two electrodes One electrode Two electrodes
(Symmetric, non-adjacent)
Main advantages Simple structure, high open-circuit voltage Continuous output, high power density Simple structure, no wiring needed for the moving part High efficiency,
good cycle stability,
high energy density
Main drawbacks Pulsed output, relies on elastic layer recovery Severe long-term sliding wear, poor reliability Lowest output power Motion trajectory requirements
Typical Applications Pressure and vibration sensing Wind and water flow energy harvesting Wearable devices, touchscreens Rotary, vibration
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Table 3.   Comparison of different manufacturing methods and structural performance.

Fabrication methods Template-assisted method Material synthesis method Textile method 3D printing method MEMS technology
Structural Geometry Pyramid porous electrospun fiber micropillar micropillar
Sensitivity Very High Moderate Moderate Adjustable High
Pressure Range Narrow Very Wide Extremely Wide Adjustable Wide
Mechanical Durability Moderate Low High Very High High
Fabrication Complexity High Low Moderate Very High Very High
Suggested application Scenarios Ultra-low pressure detection High-pressure loading Wearable motion monitoring Customized sensors Array tactile sensor
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    Received: 18 May 2026 Revised: 11 July 2026 Online: Accepted Manuscript: 20 August 2026

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      Article Navigation >  Journal of Semiconductors  > 2026  > Accepted Manuscript
      Tianxiao Peng, Lanyue Shen, Zhenqiu Gao, Zhengdi Sima, Azamat Yeshmukhametov, Gulnur Kalimuldina, Yina Liu, Zhen Wen. Advances in flexible triboelectric pressure sensor for physiological signal monitoring[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26050029 ****T X Peng, L Y Shen, Z Q Gao, Z D S ma, A Yeshmukhametov, G Kalimuldina, Y N Liu, and Z Wen, Advances in flexible triboelectric pressure sensor for physiological signal monitoring[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26050029
      Citation:
      Tianxiao Peng, Lanyue Shen, Zhenqiu Gao, Zhengdi Sima, Azamat Yeshmukhametov, Gulnur Kalimuldina, Yina Liu, Zhen Wen. Advances in flexible triboelectric pressure sensor for physiological signal monitoring[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26050029 ****
      T X Peng, L Y Shen, Z Q Gao, Z D S ma, A Yeshmukhametov, G Kalimuldina, Y N Liu, and Z Wen, Advances in flexible triboelectric pressure sensor for physiological signal monitoring[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26050029
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      Advances in flexible triboelectric pressure sensor for physiological signal monitoring

      DOI: 10.1088/1674-4926/26050029
      CSTR: 32376.14.1674-4926.26050029
      • 1.

        Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory of Advanced Healthcare Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, 215123 P. R. China

      • 2.

        Department of Mechanical and Aerospace Engineering, Department of Robotics Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan

      • 3.

        Department of Applied Mathematics, School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, P. R. China

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      • Tianxiao Peng received his B. S. in Applied Chemistry from Wuhan University of Technology in 2025. Currently, he is a graduate student at the Functional Nano and Soft Matter Institute (FUNSOM) of Soochow University. His research primarily focuses on triboelectric nanogenerators based on self-powered sensors
      • Lanyue Shen received her B. S. in Nanomaterial and Technology at Soochow University in 2022. Now she is a graduate student in Institute of Functional Nano & Soft Materials (FUNSOM) in Soochow University. Her research focuses primarily on self-powered sensor based triboelectric nanogenerator
      • Zhenqiu Gao got his B. S. degree in 2020 and PhD degree in 2025 in Nanomaterial and Technology at Soochow University. His main research interests focus on triboelectric nanogenerator and self-powered sensing system
      • Yina Liu is an associate professor in Department of Applied Mathematics at Xi’an Jiaotong-Liverpool University, China. She received her B. S. degree in Electrical and Electronic Engineering at Xi’an Jiaotong-Liverpool University in 2011 and Ph.D. degree in Applied Mathematics at University of Liverpool in 2015. Her current research interests include intelligent sensing systems and applications in healthcare
      • Zhen Wen is a professor in Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University. He received his B.S. degree in Materials Science and Engineering from China University of Mining and Technology in 2011 and Ph.D. degree in Materials Physics and Chemistry from Zhejiang University in 2016. During 2014~2016, he was supported by the program of China Scholarship Council as a joint Ph.D. student in Georgia Institute of Technology, US. His main research interests focus on triboelectric nanogenerator based energy harvesting and self-powered sensing system
      • Corresponding author: yina.liu@xjtlu.edu.cn; wenzhen2011@suda.edu.cn
      • Received Date: 2026-05-18
      • Revised Date: 2026-07-11
        • Available Online: 2026-08-20

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