Self-powered Posture Recognition Based on Thermoelectric Hydrogel

Yu-Hao Zhang; Guo-Shun Cui; Saeed Ahmed Khan; Lang-Hua Shi; Hao-Zhe Zhang; Kun Yang; Xuan-Sen Zhao; Hu-Lin Zhang

doi:10.1007/s10118-025-3440-5

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Self-powered Posture Recognition Based on Thermoelectric Hydrogel

Special Topic: Functional Gels | Updated：2026-05-30

- Self-powered Posture Recognition Based on Thermoelectric Hydrogel
- Self-powered Posture Recognition Based on Thermoelectric Hydrogel
- Chinese Journal of Polymer Science 2026年44卷第6期页码：1780-1789
- Affiliations：
  
  a.College of Integrated Circuits, Taiyuan University of Technology, Taiyuan 030024, China
  b.Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan
  c.College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
  d.College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China
- Author bio：
  
  yangkun01@tyut.edu.cn (K.Y.)
  zhanghulin@tyut.edu.cn (H.L.Z.)
- Funds：
  
  This work was financially supported by the Fundamental Research Project of Shanxi Province (No. 202403021211229), the National Natural Science Foundation of China (No. 52475600), and Special Project of Science and Technology Cooperation and Exchange of Shanxi Province (No. 202304041101021).;The authors declare no interest conflict.Electronic supplementary information (ESI) is available free of charge in the online version of this article at http://doi.org/10.1007/s10118-025-3440-5.Data will be made available upon request.
- DOI：10.1007/s10118-025-3440-5
  中图分类号：
- 收稿：2025-07-30，
  
  录用：2025-08-17，
  
  网络首发：2025-12-17，
  
  纸质出版：2026-06-05
- Accepted：
Scan QR Code
Zhang, Y. H.; Cui, G. S.; Khan, S. A.; Shi, L. H.; Zhang, H. Z.; Yang, K.; Zhao, X. S.; Zhang, H. L. Self-powered posture recognition based on thermoelectric hydrogel. Chinese J. Polym. Sci. 2026, 44, 1780–1789

Yu-Hao Zhang, Guo-Shun Cui, Saeed Ahmed Khan, et al. Self-powered Posture Recognition Based on Thermoelectric Hydrogel[J]. Chinese Journal of Polymer Science, 2026, 44(6): 1780-1789.
Zhang, Y. H.; Cui, G. S.; Khan, S. A.; Shi, L. H.; Zhang, H. Z.; Yang, K.; Zhao, X. S.; Zhang, H. L. Self-powered posture recognition based on thermoelectric hydrogel. Chinese J. Polym. Sci. 2026, 44, 1780–1789 DOI： 10.1007/s10118-025-3440-5.

Yu-Hao Zhang, Guo-Shun Cui, Saeed Ahmed Khan, et al. Self-powered Posture Recognition Based on Thermoelectric Hydrogel[J]. Chinese Journal of Polymer Science, 2026, 44(6): 1780-1789. DOI： 10.1007/s10118-025-3440-5.

摘要

This study presents a flexible hydrogel-based posture recognition system capable of accurately identifying nine distinct human postures. By integrating thermoelectric sensing with artificial intelligence algorithms

the system achieved a recognition accuracy of 96.82%. The real-time dynamic visualization of posture data was enabled using a custom-developed visualization platform.

Abstract

Posture recognition technology plays a crucial role in health monitoring

motion analysis

and other related fields. However

traditional recognition devices are limited by a lack of comfort

stability

and environmental adaptability

which significantly restrict their performance and range of applications. In response

this study proposes a self-powered

strain-driven

intelligent posture recognition method based on a thermoelectric hydrogel. This approach enables high-precision posture recognition through simultaneous acquisition of dynamic strain signals from multiple joints. The proposed poly(vinyl alcohol) (PVA)/starch bi-network hydrogel

synthesized in a binary H

O/glycerol solvent system

exhibited outstanding flexibility and tensile strength

allowing it to conform closely to joint movements and ensure wearing comfort. Simultaneously

the hydrogel generated stable electrical signals

via

the redox reaction of [Fe(CN)

]

3–/4–

supporting continuous monitoring and data collection. Moreover

its self-powered nature allows effective joint strain signal acquisition

even in open environments. Using a machine learning algorithm that incorporates signal processing and analysis

the proposed method achieved a posture recognition accuracy of 96.82%. This study presents a novel technological route for posture recognition in wearable devices

demonstrating its strong application potential in real-time feedback for

sports rehabilitation and performance analysis of athletes.

关键词

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references

Wu, X.; Bai, S.; O’Sullivan, L. Editorial for the special issue on wearable robots and intelligent device. Biomim. Intell. Robot. 2023 , 3 , 100102..

Li, X.; Zhou, Z.; Wu, J.; Xiong, Y. Human posture detection method based on wearable devices. J. Healthc. Eng. 2021 , 2021 , 1−8..

Roggio, F.; Ravalli, S.; Maugeri, G.; Bianco, A.; Palma, A.; Di Rosa, M.; Musumeci, G. Technological advancements in the analysis of human motion and posture management through digital devices. World J Orthop. 2021 , 12 , 467−484..

Jiang, T.; Fang, K.; Zhao, H.; Chen, G.; Wang, Y. A multi-view structured light measurement method based on pose estimation using deep learning. IEEE Access 2022 , 10 , 127718−127727..

Lan, G.; Wu, Y.; Hu, F.; Hao, Q. Vision-based human pose estimation via deep learning: a survey. IEEE Trans. Human-Mach. Syst. 2023 , 53 , 253−268..

[Xu, L.; Zhao, L.; Sun, X.; Wang, D.; Li, G.; Yan, K. A comprehensive framework for occluded human pose estimation. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; IEEE: Seoul, Korea, Republic of, 2024 , pp. 3405–3409..

[Zhang, F.; Li, Z.; Zhu, X.; Chen, L. Robust low-light human pose estimation through illumination-texture modulation. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; IEEE: Hyderabad, India, 2025 , pp. 1–5..

Gu, K.; Su, B. A study of human pose estimation in low-light environments using YOLOv8 model. ACE 2024 , 32 , 136−142..

Zhao, J. A review of wearable IMU (inertial-measurement-unit)-based pose estimation and drift reduction technologies. J. Phys.: Conf. Ser. 2018 , 1087 , 042003..

Yu, Q.; Zhang, P.; Chen, Y. Human motion state recognition based on flexible, wearable capacitive pressure sensors. Micromachines (Basel) 2021 , 12 , 1219..

Liu, J.; Zhang, J.; Liu, J.; Sun, W.; Li, W.; Shen, H.; Wang, L.; Li, G. Advances in fiber-based wearable sensors for personal digital health monitoring. Materials 2023 , 16 , 7428..

Zhang, Q.; Niu, X.; Shi, C. Impact assessment of various IMU error sources on the relative accuracy of the GNSS/INS systems. IEEE Sensors J. 2020 , 20 , 5026−5038..

Chen, W.; Yu, C.; Tu, C.; Lyu, Z.; Tang, J.; Ou, S.; Fu, Y.; Xue, Z. A survey on hand pose estimation with wearable sensors and computer-vision-based methods. Sensors 2020 , 20 , 1074..

Palermo, E.; Rossi, S.; Patanè, F.; Cappa, P. Experimental evaluation of indoor magnetic distortion effects on gait analysis performed with wearable inertial sensors. Physiol. Meas. 2014 , 35 , 399−415..

Brasier, N.; Wang, J.; Gao, W.; Sempionatto, J. R.; Dincer, C.; Ates, H. C.; Güder, F.; Olenik, S.; Schauwecker, I.; Schaffarczyk, D.; Vayena, E.; Ritz, N.; Weisser, M.; Mtenga, S.; Ghaffari, R.; Rogers, J. A.; Goldhahn, J. Applied body-fluid analysis by wearable devices. Nature 2024 , 636 , 57−68..

Hu, J.; Liu, Z.; Yang, P.; Qin, S.; Li, N.; Ji, W.; Wang, Z. L.; Chen, X. Charge transfer behavior of triboelectric polymers and triboelectric sensors operated under ultrahigh pressure. SusMat 2024 , 4 , e250..

Yang, P.; Liu, Z.; Qin, S.; Hu, J.; Yuan, S.; Wang, Z. L.; Chen, X. A wearable triboelectric impedance tomography system for noninvasive and dynamic imaging of biological tissues. Sci. Adv. 2024 , 10 , eadr9139..

Bai, C.; Wang, Z.; Yang, S.; Cui, X.; Li, X.; Yin, Y.; Zhang, M.; Wang, T.; Sang, S.; Zhang, W.; Zhang, H. Wearable electronics based on the gel thermogalvanic electrolyte for self-powered human health monitoring. ACS Appl. Mater. Interfaces 2021 , 13 , 37316−37322..

Bai, C.; Li, X.; Cui, X.; Yang, X.; Zhang, X.; Yang, K.; Wang, T.; Zhang, H. Transparent stretchable thermogalvanic PVA/gelation hydrogel electrolyte for harnessing solar energy enabled by a binary solvent strategy. Nano Energy 2022 , 100 , 107449..

Fang, R.; Li, X.; Khan, S. A.; Wang, Z.; Cui, X.; Zhang, H.; Lin, Z.-H. Anhydrous thermogalvanic gel for simultaneous waste heat recovery and thermal management of electronics. ACS Appl. Polym. Mater. 2023 , 5 , 4628−4635..

Tian, C.; Bai, C.; Wang, T.; Yan, Z.; Zhang, Z.; Zhuo, K.; Zhang, H. Thermogalvanic hydrogel elect rolyte for harvesting biothermal energy enabled by a novel redox couple of SO 4/3 2- ions. Nano Energy 2023 , 106 , 108077..

Liu, Y.; Zhang, Q.; Odunmbaku, G. O.; He, Y.; Zheng, Y.; Chen, S.; Zhou, Y.; Li, J.; Li, M.; Sun, K. Solvent effect on the seebeck coefficient of Fe 2+ /Fe 3+ hydrogel thermogalvanic cells. J. Mater. Chem. A 2022 , 10 , 19690−19698..

Han, C.-G.; Qian, X.; Li, Q.; Deng, B.; Zhu, Y.; Han, Z.; Zhang, W.; Wang, W.; Feng, S.-P.; Chen, G.; Liu, W. Giant thermopower of ionic gelatin near room temperature. Science 2020 , 368 , 1091−1098..

Jin, L.; Greene, G. W.; MacFarlane, D. R.; Pringle, J. M. Redox-active quasi-solid-state electrolytes for thermal energy harvesting. ACS Energy Lett. 2016 , 1 , 654−658..

Yu, B.; Duan, J.; Cong, H.; Xie, W.; Liu, R.; Zhuang, X.; Wang, H.; Qi, B.; Xu, M.; Wang, Z. L.; Zhou, J. Thermosensitive crystallization–boosted liquid thermocells for low-grade heat harvesting. Science 2020 , 370 , 342−346..

Yang, H.; Li, N.; Yang, K.; Sun, L.; Zhang, H.; Zhang, Z.; Cui, X. Coupling thermogalvanic and piezoresistive effects in a robust hydrogel for deep-learning-assisted self-powered sign language and object recognition. Chem. Eng. J. 2024 , 488 , 150816..

Jose, J.; Al-Harthi, M. A. Citric acid crosslinking of poly(vinyl alcohol)/starch/graphene nanocomposites for superior properties. Iran Polym. J. 2017 , 26 , 579−587..

Liang, J.; Ludescher, R. D. Effects of glycerol on the molecular mobility and hydrogen bond network in starch matrix. Carbohyd Polym. 2015 , 115 , 401−407..

Kovtun, G.; Casas, D.; Cuberes, T. Influence of glycerol on the surface morphology and crystallinity of polyvinyl alcohol films. Polymers 2024 , 16 , 2421..

Ma, X.; Yu, J.; He, K. Thermoplastic starch plasticized by glycerol as solid polymer electrolytes. Macromol. Mater. Eng. 2006 , 291 , 1407−1413..

Phattarateera, S.; Xin, L.; Amphong, C.; Limsamran, V.; Threepopnatkul, P. Comparative studies of starch blend s on the properties of PVA films. Carbohydr. Polym. Tech. 2023 , 6 , 100340..

[Development of starch-polyvinyl alcohol (PVA) biodegradable film: effect of cross-linking agent and antimicrobials on film characteristics. J. Appl. Packag. Res . 2017 , 9 , 1..

[Li, N.; Wang, Z.; Niu, Y.; Li, Y.; Wen, S.; Zhang, H.; Lin, Z. Bionic multimodal augmented somatosensory receptor enabled by thermogalvanic hydrogel. Adv. Sci . 2025 , e05873 ..

Li, Y.; Li, N.; Zhang, X.; Zhang, J.; Sun, L.; Huang, Z.; Zhang, H. Deep-learning-enabled breathable thermogalvanic hydrogel array for self-powered mental monitoring. J. Mater. Chem. A 2025 , 13 , 10531−10539..

Cazón, P.; Velazquez, G.; Vázquez, M. Characterization of mechanical and barrier properties of bacterial cellulose, glycerol and polyvinyl alcohol (PVOH) composite films with eco-friendly UV-protective properties. Food Hydrocolloid 2020 , 99 , 105323..

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