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

Keywords

references

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