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A battery-free wireless epidermal sensor network for continuous systolic blood pressure monitoring

Nature Electronics (2026)Cite this article

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Abstract

Continuous and precise monitoring of physiological signals via epidermal sensors could be of use in the development of personalized healthcare. However, the practical deployment of such sensors is hindered by the need for bulky batteries and limitations in data transfer. Here we report a battery-free epidermal network that is wirelessly interconnected through a wearable metamaterial and can provide continuous, high-fidelity biosensing. The network separates the power transfer (13.56 MHz) and data communication (2.4 GHz) channels through a dual-mode metamaterial textile, providing efficient wireless power transfer and low-latency data communication. We use a smartphone as a hub to wirelessly deliver power to and acquire biological signals from multiple networked epidermal sensors mediated by the metamaterial, which is integrated in clothing. The network can continuously monitor systolic blood pressure, including in dynamic environments such as during exercise.

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Fig. 1: Battery-free epidermal sensor networks with dual-mode metamaterial textiles.
Fig. 2: Dual-mode metamaterial textile design and characterization.
Fig. 3: Design and characterization of battery-free epidermal sensors.
Fig. 4: Validation of the epidermal sensor network.
Fig. 5: Continuous multimodal measurements during daily activities including commute, sleep and exercise.

Data availability

Source data are provided with this paper. All other relevant data that support the findings of this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

X.T. acknowledges funding support from the National Natural Science Foundation of China (grant number 62501361), the Guangdong Basic and Applied Basic Research Foundation (grant number 2025A1515010122) and the Shenzhen Science and Technology Program (grant number JCYJ20250604180342057). X.T. also acknowledges funding support from the Tsinghua SIGS. P.G. acknowledges support from the Moore Foundation (019797-00001) and the Technology and Research Initiative Fund (TRIF). S.A.K. thanks the Agency for Science, Technology and Research (A*STAR) for its support of our research through the SINGA fellowship program. K.A.K. acknowledges support from a NIH Infection and Inflammation as Drivers of Aging (IIDA) training grant (1T32AG058503-04). J.S.H. acknowledges funding support from the NUS iHealthtech Institute.

Author information

Author notes

  1. These authors contributed equally: Selman A. Kurt, Kevin Albert Kasper, Qinghao Xu.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Selman A. Kurt, Xiao Xiao & John S. Ho

  2. Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, USA

    Kevin Albert Kasper, Tucker Stuart & Philipp Gutruf

  3. Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Qinghao Xu, Jian Song, Wenbo Ding & Xi Tian

  4. Center of Double Helix, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Qinghao Xu & Xi Tian

  5. IoT Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

    Xin Yang

  6. College of Computer Science, Zhejiang University of Technology, Hangzhou, China

    Qihang Zeng

  7. School of Microelectronics, University of Science and Technology of China, Hefei, China

    Zhipeng Li

  8. Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore

    Yuxin Liu & John S. Ho

  9. Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore

    Yuxin Liu

  10. The N.1 Institute for Health, National University of Singapore, Singapore, Singapore

    Yuxin Liu & John S. Ho

  11. Department of Electrical and Computer Engineering, Bio5 Institute and Neuroscience GIDP, The University of Arizona, Tucson, AZ, USA

    Philipp Gutruf

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Contributions

S.A.K., K.A.K., Q.X., X.T., P.G. and J.S.H. conceived and planned the research. S.A.K. and X.T. designed the dual-mode metamaterial textile. K.A.K., S.A.K. and P.G. designed and fabricated the battery-free epidermal sensors and dry electrodes. S.A.K. and Q.X. performed the electromagnetic simulations and characterized the wireless system. S.A.K., K.A.K. and Q.X. conducted the benchtop characterization and signal monitoring experiments. X.Y., X.X., Q.Z., T.S., Z.L., Y.L., J.S. and W.D. contributed to the experimental setup and study design. S.A.K., K.A.K., Q.X., X.T., P.G. and J.S.H. wrote the paper with input from all authors. X.T., P.G. and J.S.H. supervised the entire project.

Corresponding authors

Correspondence to Xi Tian, Philipp Gutruf or John S. Ho.

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Nature Electronics thanks Toni Björninen, Hyoyoung Jeong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–25 and Table 1.

Reporting Summary (download PDF )

Supplementary Video 1 (download MP4 )

Continuous BP monitoring during strenuous exercise.

Supplementary Video 2 (download MP4 )

Washing protocol.

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Statistical source data.

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Statistical source data.

Source Data Fig. 4 (download XLSX )

Statistical source data.

Source Data Fig. 5 (download XLSX )

Statistical source data.

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Kurt, S.A., Kasper, K.A., Xu, Q. et al. A battery-free wireless epidermal sensor network for continuous systolic blood pressure monitoring. Nat Electron (2026). https://doi.org/10.1038/s41928-026-01597-1

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