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A cross-modal epidermal sensor enables single-channel fusion of biopotential and biomechanical signals

Nature Sensors (2026)Cite this article

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Abstract

Disease monitoring typically requires the acquisition of multiple physiological signals with different modalities, yet existing epidermal electronics rely on separate sensors for each modality, increasing device footprint, bandwidth and power consumption. Here we report a wearable electronic system that fuses multimodal physiological signals into a single cross-modal biosignal (X-Sig). Leveraging a cross-layered device architecture and in-sensor signal fusion strategy, the X-Sig sensor concurrently acquires biopotential signals (electrocardiography and electromyography) and biomechanical signals (force myography and radial pulse) through a single channel. This approach enables continuous monitoring of haemodynamic parameters with high accuracy, including heart rate, pulse arrival time, and diastolic and systolic blood pressure. In machine-learning-based gesture recognition, the sensor substantially reduced the decoding error rate compared with conventional electromyography. By fusing complementary modalities at the sensor level, the X-Sig sensor provides a versatile platform for designing bandwidth-efficient and low-power wearable electronics.

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Fig. 1: Design concept and principle of epidermal cross-modal biosignal (X-Sig) sensor.
Fig. 2: CARD electrodes based on heterogeneous configuration design and PU molecular engineering.
Fig. 3: Cross-layered integration and signal fusion of X-Sig sensor.
Fig. 4: Single-signal monitoring of complex haemodynamic parameters with X-Sig sensor.
Fig. 5: X-Sig sensor for accurate gesture recognition.

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Data availability

The main data supporting the results in this study are available within the Article and its Supplementary Information. Source data are provided with this paper. Other data are available from the corresponding authors upon request.

Code availability

Code for implementing and experimenting with the proposed methodology is available via GitHub at https://github.com/ap152/X-Sig-sensor.

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Acknowledgements

This study was supported by Sichuan Science and Technology Program (grant no. 2024YFFK0133), National University of Singapore Presidential Young Professorship Award (grant no. 22-4974-A0003), MTC Programmatic ‘BLISS’ (grant no. M24M9b0013), Wellcome Leap’s Dynamic Resilience Program jointly funded by Temasek Trust, MOE AcRF Tier 1 grant (grant no. 22-5402-A0001-0), A*STAR Manufacturing, Trade and Connectivity (MTC) MedTech Programmatic Seed grant (grant no. M24N9b0121), National Natural Science Foundation of China (grant no. 52203272), and Fundamental Research Funds for the Central Universities of China, Medical Interdisciplinary Research Key Project of Sichuan University (grant no. 2022).

Author information

Author notes

  1. These authors contributed equally: Xiaodong Wu, Cheng Zhu, Lifei Zheng, Yangyang Song, Jinchao Wang.

Authors and Affiliations

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

    Xiaodong Wu & Yuxin Liu

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

    Xiaodong Wu & Yuxin Liu

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

    Xiaodong Wu & Yuxin Liu

  4. School of Mechanical Engineering, Sichuan University, Chengdu, China

    Xiaodong Wu, Cheng Zhu, Lifei Zheng, Yangyang Song, Jinchao Wang, Zhentao Yao & Zhuqing Wang

  5. School of Engineering Science, Royal Institute of Technology, Stockholm, Sweden

    Xuyi Zhang

  6. State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, China

    Yangyang Han

  7. NHC Key Laboratory of Chronobiology, Sichuan University, Chengdu, China

    Zhou Jiang

  8. College of Electrical Engineering, Sichuan University, Chengdu, China

    Zhimeng Liu

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Contributions

X.W. conceptualized the work. X.W., Z.L. and Y.L. developed the methodology. J.W., C.Z., L.Z., Y.S. and X.W. performed experiments. J.W., C.Z., L.Z., Y.S., X.Z., Z.Y., Z.L. and X.W. visualized data. Y.H., Z.W. and Z.J. provide supporting resources, invaluable expertise and insights. X.W., Z.L. and Y.L. acquired funding. X.W., Z.L. and Y.L. supervised the project. J.W., C.Z., L.Z., X.W., Z.L. and Y.L. wrote the paper. X.W., Z.L. and Y.L. edited the paper.

Corresponding authors

Correspondence to Xiaodong Wu, Zhimeng Liu or Yuxin Liu.

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Competing interests

X.W., C.Z., L.Z. and Y.H. are inventors on a pending patent application in China (202410442845.X) pertaining to this research. The other authors declare no competing interests.

Peer review

Peer review information

Nature Sensors thanks Mengdi Han, Jinxing Li and Xing Sheng for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Notes 1–10, Supplementary Figs. 1–51, Supplementary Tables 1–3, captions for Supplementary Videos 1–3 and Supplementary References 1–34.

Reporting Summary (download PDF )

Supplementary Video 1 (download MP4 )

Water-assisted peeling process of adhesive CARD electrodes from the skin surface.

Supplementary Video 2 (download MP4 )

Continuous monitoring of ECG signal with X-Sig sensor under different skin deformations.

Supplementary Video 3 (download MP4 )

Continuous monitoring of HR, SBP and DBP with X-Sig sensor.

Supplementary Data (download XLSX )

Source data for Supplementary Figs. 12b, 29a, 37b and 38–40.

Source data

Source Data Fig. 2 (download XLSX )

Source data for Fig. 2c–j.

Source Data Fig. 3 (download XLSX )

Source data for Fig. 3g–m,o.

Source Data Fig. 4 (download XLSX )

Source data for Fig. 4g–j,l.

Source Data Fig. 5 (download XLSX )

Source data for Fig. 5e–g.

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Wu, X., Zhu, C., Zheng, L. et al. A cross-modal epidermal sensor enables single-channel fusion of biopotential and biomechanical signals. Nat. Sens. (2026). https://doi.org/10.1038/s44460-026-00044-0

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