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A self-filtering liquid acoustic sensor for voice recognition

Nature Electronics volume 7, pages 924–932 (2024)Cite this article

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Abstract

Wearable acoustic sensors can be used for voice recognition. However, the capabilities of such devices, which are typically based on solid materials, are often restricted by ambient noise, motion artefacts and low conformability to the skin. Here we report a liquid acoustic sensor for voice recognition. The approach is based on a three-dimensional oriented and ramified magnetic network structure of neodymium–iron–boron magnetic nanoparticles suspended in a carrier fluid, which behaves like a permanent magnet. The sensor can discriminate small pressures (0.9 Pa), has a high signal-to-noise ratio (69.1 dB) and provides self-filtering capabilities that can remove low-frequency biomechanical motion artefact (less than 30 Hz). We use the liquid acoustic sensor—together with a machine learning algorithm—to create a wearable voice recognition system that offers a recognition accuracy of 99% in a noisy environment.

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Fig. 1: A self-filtering liquid acoustic sensor.
Fig. 2: Formation process of PFMs.
Fig. 3: Fabrication of liquid acoustic sensors.
Fig. 4: Characterization of liquid acoustic sensors.
Fig. 5: Wearable voice recognition system based on the liquid acoustic sensor.

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Data availability

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

Code availability

Computational simulation code and speech recognition code are available from the corresponding author upon reasonable request.

References

  1. Park, J. et al. Frequency-selective acoustic and haptic smart skin for dual-mode dynamic/static human-machine interface. Sci. Adv. 8, eabj9220 (2022).

    Article  Google Scholar 

  2. Ma, K. et al. A wave-confining metasphere beamforming acoustic sensor for superior human-machine voice interaction. Sci. Adv. 8, eadc9230 (2022).

    Article  Google Scholar 

  3. Liu, H. et al. An epidermal sEMG tattoo-like patch as a new human–machine interface for patients with loss of voice. Microsyst. Nanoeng. 6, 16 (2020).

    Article  Google Scholar 

  4. Cohen, P. R. & Oviatt, S. L. The role of voice input for human-machine communication. Proc. Natl. Acad. Sci. USA 92, 9921–9927 (1995).

    Article  Google Scholar 

  5. Yang, Q. et al. Mixed-modality speech recognition and interaction using a wearable artificial throat. Nat. Mach. Intell. 5, 169–180 (2023).

    Article  Google Scholar 

  6. Gong, S. et al. Hierarchically resistive skins as specific and multimetric on-throat wearable biosensors. Nat. Nanotechnol. 18, 889–897 (2023).

    Article  Google Scholar 

  7. Che, Z. et al. Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system. Nat. Commun. 15, 1873 (2024).

    Article  Google Scholar 

  8. Mathew, L. R., Priya, G. & Gopakumar, K. Piezoelectric throat microphone based voice analysis. In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) 1603–1608 (IEEE, 2021).

  9. Yan, W. et al. Single fibre enables acoustic fabrics via nanometre-scale vibrations. Nature 603, 616–623 (2022).

    Article  Google Scholar 

  10. Gao, Y. et al. Hydrogel microphones for stealthy underwater listening. Nat. Commun. 7, 12316 (2016).

    Article  Google Scholar 

  11. Crut, A., Maioli, P., Del Fatti, N. & Vallée, F. Acoustic vibrations of metal nano-objects: time-domain investigations. Phys. Rep. 549, 1–43 (2015).

    Article  MathSciNet  Google Scholar 

  12. Zhou, Q., Zheng, J., Onishi, S., Crommie, M. F. & Zettl, A. K. Graphene electrostatic microphone and ultrasonic radio. Proc. Natl. Acad. Sci. USA 112, 8942–8946 (2015).

    Article  Google Scholar 

  13. Lee, S. et al. A high-fidelity skin-attachable acoustic sensor for realizing auditory electronic skin. Adv. Mater. 34, 2109545 (2022).

    Article  Google Scholar 

  14. Lee, S. et al. An ultrathin conformable vibration-responsive electronic skin for quantitative vocal recognition. Nat. Commun. 10, 2468 (2019).

    Article  Google Scholar 

  15. Lin, Z. et al. A personalized acoustic interface for wearable human–machine interaction. Adv. Funct. Mater. 32, 2109430 (2022).

    Article  Google Scholar 

  16. Yang, J. et al. Eardrum-inspired active sensors for self-powered cardiovascular system characterization and throat-attached anti-interference voice recognition. Adv. Mater. 27, 1316–1326 (2015).

    Article  Google Scholar 

  17. Kar, B. & Wallrabe, U. Performance enhancement of an ultrasonic power transfer system through a tightly coupled solid media using a KLM model. Micromachines 11, 355 (2020).

    Article  Google Scholar 

  18. Tang, T. et al. Stretchable polymer composites with ultrahigh piezoelectric performance. Natl. Sci. Rev. 10, nwad177 (2023).

    Article  Google Scholar 

  19. Lavallée, Y. et al. Seismogenic lavas and explosive eruption forecasting. Nature 453, 507–510 (2008).

    Article  Google Scholar 

  20. Boger, D. V. Demonstration of upper and lower Newtonian fluid behaviour in a pseudoplastic fluid. Nature 265, 126–128 (1977).

    Article  Google Scholar 

  21. Zhou, Y. et al. Giant magnetoelastic effect in soft systems for bioelectronics. Nat. Mater. 20, 1670–1676 (2021).

    Article  Google Scholar 

  22. Liu, X. et al. Reconfigurable ferromagnetic liquid droplets. Science 365, 264–267 (2019).

    Article  MathSciNet  Google Scholar 

  23. Mertelj, A., Lisjak, D., Drofenik, M. & Copic, M. Ferromagnetism in suspensions of magnetic platelets in liquid crystal. Nature 504, 237–241 (2013).

    Article  Google Scholar 

  24. Vecchio, D. A., Mahler, S. H., Hammig, M. D. & Kotov, N. A. Structural analysis of nanoscale network materials using graph theory. ACS Nano 15, 12847–12859 (2021).

    Article  Google Scholar 

  25. Libanori, A., Chen, G., Zhao, X., Zhou, Y. & Chen, J. Smart textiles for personalized healthcare. Nat. Electron. 5, 142–156 (2022).

    Article  Google Scholar 

  26. Chen, G. et al. Electronic textiles for wearable point-of-care systems. Chem. Rev. 122, 3259–3291 (2022).

    Article  Google Scholar 

  27. Chen, L., Huang, S., Ras, R. H. A. & Tian, X. Omniphobic liquid-like surfaces. Nat. Rev. Chem. 7, 123–137 (2023).

    Article  Google Scholar 

  28. Tang, X., Shen, H., Zhao, S., Li, N. & Liu, J. Flexible brain–computer interfaces. Nat. Electron. 6, 109–118 (2023).

    Article  Google Scholar 

  29. Koo, J. H. et al. A vacuum-deposited polymer dielectric for wafer-scale stretchable electronics. Nat. Electron. 6, 137–145 (2023).

    Article  Google Scholar 

  30. Jiang, Y. et al. A universal interface for plug-and-play assembly of stretchable devices. Nature 614, 456–462 (2023).

    Article  Google Scholar 

  31. Yan, Z. et al. Highly stretchable van der Waals thin films for adaptable and breathable electronic membranes. Science 375, 852–859 (2022).

    Article  Google Scholar 

  32. Park, B. et al. Cuticular pad–inspired selective frequency damper for nearly dynamic noise–free bioelectronics. Science 376, 624–629 (2022).

    Article  Google Scholar 

  33. Zhao, Y. et al. Ultra-conformal skin electrodes with synergistically enhanced conductivity for long-time and low-motion artifact epidermal electrophysiology. Nat. Commun. 12, 4880 (2021).

    Article  Google Scholar 

  34. Ershad, F. et al. Ultra-conformal drawn-on-skin electronics for multifunctional motion artifact-free sensing and point-of-care treatment. Nat. Commun. 11, 3823 (2020).

    Article  Google Scholar 

  35. Yin, J., Wang, S., Tat, T. & Chen, J. Motion artifact management in soft bioelectronics. Nat. Rev. Bioeng. 2, 541–558 (2024).

    Article  Google Scholar 

  36. Yang, C. & Suo, Z. Hydrogel ionotronics. Nat. Rev. Mater. 3, 125–142 (2018).

    Article  Google Scholar 

  37. Keplinger, C. et al. Stretchable, transparent, ionic conductors. Science 341, 984–987 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the Henry Samueli School of Engineering & Applied Science and the Department of Bioengineering at the University of California, Los Angeles, for their startup support. J.C. acknowledges the Vernroy Makoto Watanabe Excellence in Research Award at the UCLA Samueli School of Engineering, the Office of Naval Research Young Investigator Award (award ID N00014-24-1-2065), National Science Foundation Grant (award ID 2425858), National Institutes of Health Grant (award ID R01 CA287326), the American Heart Association Innovative Project Award (award ID 23IPA1054908), the American Heart Association Transformational Project Award (award ID 23TPA1141360), the American Heart Association’s Second Century Early Faculty Independence Award (award ID 23SCEFIA1157587), the Brain & Behavior Research Foundation Young Investigator Grant (grant no. 30944) and the NIH National Center for Advancing Translational Science UCLA CTSI (grant no. KL2TR001882).

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Author notes

  1. These authors contributed equally: Xun Zhao, Yihao Zhou.

Authors and Affiliations

  1. Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA

    Xun Zhao, Yihao Zhou, Aaron Li, Jing Xu, Shreesh Karjagi, Edward Hahm, Lara Rulloda, Justin Li, Pirouz Kavehpour & Jun Chen

  2. Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA, USA

    John Hollister & Pirouz Kavehpour

Authors
  1. Xun Zhao
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Contributions

J.C. guided the whole research project. X.Z., Y.Z. and J.C. conceived the idea, designed the experiment, analysed the data, drew the figures and wrote the manuscript. A.L., J.X., S.K., E.H., L.R., J.L., J.H. and P.K. assisted in device fabrication and testing. All authors read the paper, agreed to its content and approved the submission.

Corresponding author

Correspondence to Jun Chen.

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Competing interests

A US patent 63/596,815 related to this work has been filed by the University of California, Los Angeles.

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Nature Electronics thanks Sang-Woo Kim and Li Tan for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–23, Notes 1–3, Table 1 and References.

Reporting Summary

Supplementary Video 1

A 3D ORM network structure of the PFM.

Supplementary Video 2

Drop-ball tests on the solid and liquid devices.

Source data

Source Data Fig. 2

Source data of Fig. 2.

Source Data Fig. 3

Source data of Fig. 3.

Source Data Fig. 4

Source data of Fig. 4.

Source Data Fig. 5

Source data of Fig. 5.

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Zhao, X., Zhou, Y., Li, A. et al. A self-filtering liquid acoustic sensor for voice recognition. Nat Electron 7, 924–932 (2024). https://doi.org/10.1038/s41928-024-01196-y

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