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Nano Research 2023, 16(7): 10120-10130 https://doi.org/10.1007/s12274-023-5490-8
Research Article | Issue | Published: 13 March 2023

A vector hybrid triboelectric sensor (HTS) for motion identification via machine learning

Show Author's Information Nannan Zhou1,  Hongrui Ao1( ),  Xiaoming Chen2,  Shan Gao1,  Hongyuan Jiang1
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Keywords:
machine learning, hierarchical structure, vector triboelectric sensor, surface nanocrystalline, state detection, sensing properties
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Nanogenerators
Cite this article:
Zhou N, Ao H, Chen X, et al. A vector hybrid triboelectric sensor (HTS) for motion identification via machine learning. Nano Research, 2023, 16(7): 10120-10130. https://doi.org/10.1007/s12274-023-5490-8
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Abstract Full text Electronic supplementary material About this article

Rapidly responding and cost-effective sensors played a crucial role in industrial detection. However, the lack of versatile strategies for identifying and classifying operating states on various practical behaviors has limited the rapid development of monitoring technology. This study developed a vector hybrid triboelectric sensor (HTS) with surface nanocrystalline containing triboelectric vibration and rotation units (triboelectric vibration unit (TVU), triboelectric rotation unit (TRU)) capable of detecting the vibrational and rotary states of the device. The synchronous detection of two sensing signals can be achieved due to the hierarchical structure as the basic unit of the HTS, which contributed to reducing the volume and spatial distribution of the HTS. Based on the voltage/current/charge (U–I–Q) signal amplitudes and phase features generated by the TVU, the vibration frequency and orientation of the device can be identified by using a double-layer neural network (D-LNN), in which the accuracy reaches 96.5% and 95.5% respectively. Additionally, by combining logistic regression, D-LNN, and linear regression, the accuracy of the TRU for rotary classification exceeds 93.5% in practical application. In this study, the great potential application of the HTS combined with the machine learning methods was successfully explored and exhibited and it might speed up the development of industrial detection in the near future.


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Electronic supplementary material
About this article

A vector hybrid triboelectric sensor (HTS) for motion identification via machine learning

Show Author's information Nannan Zhou1, Hongrui Ao1( ), Xiaoming Chen2, Shan Gao1, Hongyuan Jiang1
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China

Abstract

Rapidly responding and cost-effective sensors played a crucial role in industrial detection. However, the lack of versatile strategies for identifying and classifying operating states on various practical behaviors has limited the rapid development of monitoring technology. This study developed a vector hybrid triboelectric sensor (HTS) with surface nanocrystalline containing triboelectric vibration and rotation units (triboelectric vibration unit (TVU), triboelectric rotation unit (TRU)) capable of detecting the vibrational and rotary states of the device. The synchronous detection of two sensing signals can be achieved due to the hierarchical structure as the basic unit of the HTS, which contributed to reducing the volume and spatial distribution of the HTS. Based on the voltage/current/charge (U–I–Q) signal amplitudes and phase features generated by the TVU, the vibration frequency and orientation of the device can be identified by using a double-layer neural network (D-LNN), in which the accuracy reaches 96.5% and 95.5% respectively. Additionally, by combining logistic regression, D-LNN, and linear regression, the accuracy of the TRU for rotary classification exceeds 93.5% in practical application. In this study, the great potential application of the HTS combined with the machine learning methods was successfully explored and exhibited and it might speed up the development of industrial detection in the near future.

Keywords: machine learning, hierarchical structure, vector triboelectric sensor, surface nanocrystalline, state detection, sensing properties

References(53)

[1]

Zheng, N.; Xue, J. H.; Jie, Y.; Cao, X.; Wang, Z. L. Wearable and humidity-resistant biomaterials-based triboelectric nanogenerator for high entropy energy harvesting and self-powered sensing. Nano Res. 2022, 15, 6213–6219.
DOI Google Scholar
[2]

Hou, W. C.; Tang, X. L.; Fang, L., Zheng, Q. W.; Chen, X. Y.; Zheng, L. Self-driven real-time angle vector sensor as security dialer based on bi-directional backstop triboelectric nanogenerator. Nano Energy 2022, 99, 107430.
DOI Google Scholar
[3]

Zhu, J. X.; Ji, S. L.; Yu, J. C.; Shao, H. R.; Wen, H. Y.; Zhang, H.; Xia, Z. J.; Zhang, Z. S.; Lee, C. Machine learning-augmented wearable triboelectric human–machine interface in motion identification and virtual reality. Nano Energy 2022, 103, 107766.
DOI Google Scholar
[4]

Jiang, Y.; Zhang, Y. F.; Ning, C.; Ji, Q. Q.; Peng, X.; Dong, K.; Wang, Z. L. Ultrathin eardrum-inspired self-powered acoustic sensor for vocal synchronization recognition with the assistance of machine learning. Small 2022, 18, 2106960.
DOI Google Scholar
[5]

Luo, Y.; Wang, Z. H.; Wang, J. Y.; Xiao, X.; Li, Q.; Ding, W. B.; Fu, H. Y. Triboelectric bending sensor based smart glove towards intuitive multi-dimensional human–machine interfaces. Nano Energy 2021, 89, 106330.
DOI Google Scholar
[6]

Fang, H.; Guo, J. J.; Wu, H. Wearable triboelectric devices for haptic perception and VR/AR applications. Nano Energy 2022, 96, 107112.
DOI Google Scholar
[7]

Jiang, Y.; An, J.; Liang, F.; Zuo, G. Y.; Yi, J.; Ning, C.; Zhang, H.; Dong, K.; Wang, Z. L. Knitted self-powered sensing textiles for machine learning-assisted sitting posture monitoring and correction. Nano Res. 2022, 15, 8389–8397.
DOI Google Scholar
[8]

Han, G.; Wu, B.; Pu, Y. L. Correction to: A triboelectric nanogenerator based on waste plastic bags for flexible vertical interconnection system. Microsyst. Technol. 2020, 26, 3901.
DOI Google Scholar
[9]

Fang, Y. S.; Zou, Y. J.; Xu, J.; Chen, G. R.; Zhou, Y. H.; Deng, W. L.; Zhao, X.; Roustaei, M.; Hsiai, T. K.; Chen, J. Ambulatory cardiovascular monitoring via a machine-learning-assisted textile triboelectric sensor. Adv. Mater. 2021, 33, 2104178.
DOI Google Scholar
[10]

Ouyang, H.; Li, Z.; Gu, M.; Hu, Y. R.; Xu, L. L.; Jiang, D. J.; Cheng, S. Y.; Zou, Y.; Deng, Y.; Shi, B. J. et al. A bioresorbable dynamic pressure sensor for cardiovascular postoperative care. Adv. Mater. 2021, 33, 2102302.
DOI Google Scholar
[11]

Ma, Z. T.; Chen, H.; Song, X. F.; Chen, B. H.; Li, Q.; Li, Y. L. Z.; Liu, H. Y.; Jia, K. C.; Huang, S. H.; Sun, L. Z. et al. Porous-structure engineered spacer for high-throughput and rapid growth of high-quality graphene films. Nano Res. 2022, 15, 9741–9746.
DOI Google Scholar
[12]

Li, M.; Jiang, T. Y.; Ren, Y. K.; Jiang, H. Y. Constant direct current triboelectric nanogenerator based on soft-contact mode for self-powered cathodic protection. Nano Energy 2022, 103, 107777.
DOI Google Scholar
[13]

Cao, R.; Zhou, T.; Wang, B.; Yin, Y. Y.; Yuan, Z. Q.; Li, C. J.; Wang, Z. L. Rotating-sleeve triboelectric-electromagnetic hybrid nanogenerator for high efficiency of harvesting mechanical energy. ACS Nano 2017, 11, 8370–8378.
DOI Google Scholar
[14]

Wang, P. H.; Pan, L.; Wang, J. Y.; Xu, M. Y.; Dai, G. Z.; Zou, H. Y.; Dong, K.; Wang, Z. L. An ultra-low-friction triboelectric-electromagnetic hybrid nanogenerator for rotation energy harvesting and self-powered wind speed sensor. ACS Nano 2018, 12, 9433–9440.
DOI Google Scholar
[15]

Zhu, J. X.; Sun, Z. D.; Xu, J. K.; Walczak, R. D.; Dziuban, J. A.; Lee, C. Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis. Sci. Bull. 2021, 66, 1176–1185.
DOI Google Scholar
[16]

Tao, H. C.; Gibert, J. Multifunctional mechanical metamaterials with embedded triboelectric nanogenerators. Adv. Funct. Mater. 2020, 30, 2001720.
DOI Google Scholar
[17]

Wang, C.; Li, X. M.; Wang, L.; Liu, G. F.; Nie, B. B.; Qiu, Y. F.; Fan, B.; Yan, C.; Chen, X. L.; Tian, H. M. et al. Metal micropatterning by triboelectric spark discharge. Adv. Funct. Mater. 2022, 32, 2109265.
DOI Google Scholar
[18]

Bu, T. Z.; Xiao, T. X.; Yang, Z. W.; Liu, G. X.; Fu, X. P.; Nie, J. H.; Guo, T.; Pang, Y. K.; Zhao, J. Q.; Xi, F. B. et al. Stretchable triboelectric-photonic smart skin for tactile and gesture sensing. Adv. Mater. 2018, 30, 1800066.
DOI Google Scholar
[19]

Wang, Y. N.; Shim, E.; He, N. F.; Pourdeyhimi, B.; Gao, W. Modeling the triboelectric behaviors of elastomeric nonwoven fabrics. Adv. Mater. 2022, 34, 2106429.
DOI Google Scholar
[20]

Zhang, Z. L.; Bai, Y.; Xu, L.; Zhao, M.; Shi, M. W.; Wang, Z. L.; Lu, X. M. Triboelectric nanogenerators with simultaneous outputs in both single-electrode mode and freestanding-triboelectric-layer mode. Nano Energy 2019, 66, 104169.
DOI Google Scholar
[21]

Zhao, T. M.; Fu, Y. M.; Sun, C. X.; Zhao, X. S.; Jiao, C. X.; Du, A.; Wang, Q.; Mao, Y. P.; Liu, B. D. Wearable biosensors for real-time sweat analysis and body motion capture based on stretchable fiber-based triboelectric nanogenerators. Biosens. Bioelectron. 2022, 205, 114115.
DOI Google Scholar
[22]

Alagumalai, A.; Mahian, O.; Vimal, K. E. K.; Yang, L.; Xiao, X.; Saeidi, S.; Zhang, P.; Saboori, T.; Wongwises, S.; Wang, Z. L. et al. A contextual framework development toward triboelectric nanogenerator commercialization. Nano Energy 2022, 101, 107572.
DOI Google Scholar
[23]

Jennah, S.; Belouaggadia, N.; Ezzine, M.; Lbibb, R. Study of the performance of a thermoelectric generator transformation of the thermal energy conveyed by a heat transfer fluid into electrical energy. Curr. Appl. Phys. 2022, 43, 97–105.
DOI Google Scholar
[24]

Cai, Y.; Hong, B. H.; Wu, W. X.; Wang, W. W.; Zhao, F. Y. Active cooling performance of a PCM-based thermoelectric device: Dynamic characteristics and parametric investigations. Energy 2022, 254, 124356.
DOI Google Scholar
[25]

He, X. Y.; Gu, J. T.; Hao, Y. N.; Zheng, M. R.; Wang, L. M.; Yu, J. Y.; Qin, X. H. Continuous manufacture of stretchable and integratable thermoelectric nanofiber yarn for human body energy harvesting and self-powered motion detection. Chem. Eng. J. 2022, 450, 137937.
DOI Google Scholar
[26]

Liu, N.; Zhang, X. Y.; Dou, Y. Y.; Yan, X. F.; Yu, L. M. Design of carbon aerogels with variable surface morphology for electromagnetic wave absorption. Carbon 2022, 200, 271–280.
DOI Google Scholar
[27]

Lázaro, R. P. S.; Fuentes-Aguilar, R. Q.; Chairez, I. Parametric characterization of approximation functions for the axial and lateral force between electromagnets and spherical permanent magnets. Measurement 2022, 202, 111841.
DOI Google Scholar
[28]

Shan, Z.; Cheng, S. Y.; Wu, F.; Pan, X. H.; Li, W. J.; Dong, W.; Xie, A. M.; Zhang, G. Electrically conductive two-dimensional metal-organic frameworks for superior electromagnetic wave absorption. Chem. Eng. J. 2022, 446, 137409.
DOI Google Scholar
[29]

Qian, Y. J.; Zhang, J. J.; Yu, Y. X.; Jiang, Q. S.; Yu, X. B.; Cheng, Z. Q. Preparation and release of novel frog egg-like microspheres by coaxial electrostatic spray technique. Mater. Lett. 2022, 328, 133089.
DOI Google Scholar
[30]

Zhao, Z. H.; Liu, D.; Li, Y. H.; Wang, Z. L.; Wang, J. Direct-current triboelectric nanogenerator based on electrostatic breakdown effect. Nano Energy 2022, 102, 107745.
DOI Google Scholar
[31]

Jakubik, W.; Powroźnik, P.; Wrotniak, J.; Krzywiecki, M. Theoretical analysis of acoustoelectrical sensitivity in SAW gas sensors with single and bi-layer structures. Sens. Actuators B:Chem. 2016, 236, 1069–1074.
DOI Google Scholar
[32]

Wang, N.; Wang, C.; Zhang, X. H. The quantitative measurement method for the radiation energy of the acoustoelectric converted wave field of building electricity. Microelectron. J. 2021, 108, 104989.
DOI Google Scholar
[33]

Kanokpaka, P.; Chang, L. Y.; Wang, B. C.; Huang, T. H.; Shih, M. J.; Hung, W. S.; Lai, J. Y.; Ho, K. C.; Yeh, M. H. Self-powered molecular imprinted polymers-based triboelectric sensor for noninvasive monitoring lactate levels in human sweat. Nano Energy 2022, 100, 107464.
DOI Google Scholar
[34]

Zhu, J. X.; Zhu, M. L.; Shi, Q. F.; Wen, F.; Liu, L.; Dong, B. W.; Haroun, A.; Yang, Y. Q.; Vachon, P.; Guo, X. G. et al. Progress in TENG technology—A journey from energy harvesting to nanoenergy and nanosystem. EcoMat 2020, 2, e12058.
Google Scholar
[35]

Hossain, G.; Rahman, M.; Hossain, I. Z.; Khan, A. Wearable socks with single electrode triboelectric textile sensors for monitoring footsteps. Sens. Actuators A:Phys. 2022, 333, 113316.
DOI Google Scholar
[36]

Hou, X. Y.; Zhu, M. L.; Sun, L. N.; Ding, T. X.; Huang, Z. Y.; Shi, Y. T.; Su, Y. L.; Li, L.; Chen, T.; Lee, C. Scalable self-attaching/assembling robotic cluster (S2A2RC) system enabled by triboelectric sensors for in-orbit spacecraft application. Nano Energy 2022, 93, 106894.
DOI Google Scholar
[37]

Ko, H. J.; Kwon, D. S.; Bae, K.; Kim, J. Self-suspended shell-based triboelectric nanogenerator for omnidirectional wind-energy harvesting. Nano Energy 2022, 96, 107062.
DOI Google Scholar
[38]

Wang, X.; Wang, Z. L.; Yang, Y. Hybridized nanogenerator for simultaneously scavenging mechanical and thermal energies by electromagnetic-triboelectric-thermoelectric effects. Nano Energy 2016, 26, 164–171.
DOI Google Scholar
[39]

Zhong, Y. M.; Guo, Y. C.; Wei, X. X.; Rui, P. S.; Du, H. J.; Wang, P. H. Multi-cylinder-based hybridized electromagnetic-triboelectric nanogenerator harvesting multiple fluid energy for self-powered pipeline leakage monitoring and anticorrosion protection. Nano Energy 2021, 89, 106467.
DOI Google Scholar
[40]

Xie, Z. J.; Wang, Y.; Wu, R. S.; Yin, J. H.; Yu, D.; Liu, J. Q.; Cheng, T. H. A high-speed and long-life triboelectric sensor with charge supplement for monitoring the speed and skidding of rolling bearing. Nano Energy 2022, 92, 106747.
DOI Google Scholar
[41]

Xie, Z. J.; Dong, J. W.; Li, Y. K.; Gu, L.; Song, B. Y.; Cheng, T. H.; Wang, Z. L. Triboelectric rotational speed sensor integrated into a bearing: A solid step to industrial application. Extreme Mech. Lett. 2020, 34, 100595.
DOI Google Scholar
[42]

Le, C. D.; Nguyen, T. H.; Vu, D. L.; Vo, C. P.; Ahn, K. K. A rotational switched-mode water-based triboelectric nanogenerator for mechanical energy harvesting and vehicle monitoring. Mater. Today Sustain. 2022, 19, 100158.
DOI Google Scholar
[43]

Fang, Y.; Tang, T. Y.; Li, Y. F.; Hou, C.; Wen, F.; Yang, Z.; Chen, T.;. Sun, L. N.; Liu, H. C.; Lee, C. A high-performance triboelectric-electromagnetic hybrid wind energy harvester based on rotational tapered rollers aiming at outdoor IoT applications. iScience 2021, 24, 102300.
DOI Google Scholar
[44]

Yun, J.; Jayababu, N.; Kim, D. Self-powered transparent and flexible touchpad based on triboelectricity towards artificial intelligence. Nano Energy 2020, 78, 105325.
DOI Google Scholar
[45]

Xie, L. J.; Chen, X. P.; Wen, Z.; Yang, Y. Q.; Shi, J. H.; Chen, C.; Peng, M. F.; Liu, Y. N.; Sun, X. H. Spiral steel wire based fiber-shaped stretchable and tailorable triboelectric nanogenerator for wearable power source and active gesture sensor. Nano-Micro Lett. 2019, 11, 39.
DOI Google Scholar
[46]

Zhou, Y. K.; Shen, M. L.; Cui, X.; Shao, Y. C.; Li, L. J.; Zhang, Y. Triboelectric nanogenerator based self-powered sensor for artificial intelligence. Nano Energy 2021, 84, 105887.
DOI Google Scholar
[47]

Zhang, W. Q.; Deng, L. F.; Yang, L.; Yang, P.; Diao, D. F.; Wang, P. F.; Wang, Z. L. Multilanguage-handwriting self-powered recognition based on triboelectric nanogenerator enabled machine learning. Nano Energy 2020, 77, 105174.
DOI Google Scholar
[48]

Liu, J. Y.; Wen, Z.; Lei, H.; Gao, Z. Q.; Sun, X. H. A liquid–solid interface-based triboelectric tactile sensor with ultrahigh sensitivity of 21. 48 kPa−1. Nano-Micro Lett. 2022, 14, 88.
DOI Google Scholar
[49]

Luo, Y.; Xiao, X.; Chen, J.; Li, Q.; Fu, H. Y. Machine-learning-assisted recognition on bioinspired soft sensor arrays. ACS Nano 2022, 16, 6734–6743.
DOI Google Scholar
[50]

Xu, W.; Li, X. H.; Brugger, J.; Liu, X. Study of the enhanced electricity output of a sliding droplet-based triboelectric nanogenerator for droplet sensor design. Nano Energy 2022, 98, 107166.
DOI Google Scholar
[51]

Zhang, H. D.; Cheng, Q.; Lu, X.; Wang, W. H.; Wang, Z. L.; Sun, C. W. Detection of driving actions on steering wheel using triboelectric nanogenerator via machine learning. Nano Energy 2021, 79, 105455.
DOI Google Scholar
[52]

Zhao, L. M.; Zheng, Q.; Ouyang, H.; Li, H.; Yan, L.; Shi, B. J.; Li, Z. A size-unlimited surface microstructure modification method for achieving high performance triboelectric nanogenerator. Nano Energy 2016, 28, 172–178.
DOI Google Scholar
[53]

Yin, X.; Liu, D.; Zhou, L. L.; Li, X. Y.; Xu, G. Q.; Liu, L.; Li, S. X.; Zhang, C. G.; Wang, J.; Wang, Z. L. A motion vector sensor via direct-current triboelectric nanogenerator. Adv. Funct. Mater. 2020, 30, 2002547.
DOI Google Scholar
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Publication history
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Acknowledgements

Publication history

Received: 06 November 2022
Revised: 23 December 2022
Accepted: 08 January 2023
Published: 13 March 2023
Issue date: July 2023

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgements

We acknowledge the helpful discussion with Mr. Chuanbing Zhang.

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