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Hierarchically resistive skins as specific and multimetric on-throat wearable biosensors


Resistive skin biosensors refer to a class of imperceptible wearable devices for health monitoring and human–machine interfacing, in which conductive materials are deposited onto or incorporated into an elastomeric polymeric sheet. A wide range of resistive skins has been developed so far to detect a wide variety of biometric signals including blood pressure, skin strain, body temperature and acoustic vibrations; however, they are typically non-specific, with one resistive signal corresponding to a single type of biometric data (one-mode sensors). Here we show a hierarchically resistive skin sensor made of a laminated cracked platinum film, vertically aligned gold nanowires and a percolated gold nanowire film, all integrated into a single sensor. As a result, hierarchically resistive skin displays a staircase-shaped resistive response to tensile strain, with distinct sensing regimes associated to a specific active material. We show that we can, through one resistive signal, identify up to five physical or physiological activities associated with the human throat speech: heartbeats, breathing, touch and neck movement (that is, a multimodal sensor). We develop a frequency/amplitude-based neural network, Deep Hybrid-Spectro, that can automatically disentangle multiple biometrics from a single resistive signal. This system can classify 11 activities—with different combinations of speech, neck movement and touch—with an accuracy of 92.73 ± 0.82% while simultaneously measuring respiration and heart rates. We validated the classification accuracy of several biometrics with an overall accuracy of >82%, demonstrating the generality of our concept.

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Fig. 1: Bio-inspired HR-skin-neuron network sensory processing framework.
Fig. 2: Structure design, characterization and working principles of the HR skin.
Fig. 3: Mechano-electrical performance characterization of the HR skin under acoustic, strain and pressure stimulations.
Fig. 4: Performance evaluation to monitor multilevel throat activities.
Fig. 5: Development of DeepHS for classification and recognition of throat activities.

Data availability

All relevant data are available from the authors on reasonable request, and/or are included within the manuscript and its Supplementary Information. The machine learning datasets used in this study are available at Source Data are provided with this paper.

Code availability

The code that supports the plots within this paper and other findings of this study are available at


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This research was financially supported under Discovery Projects funding scheme (grant nos. DP180101715, DP200100624 and DP210101045) and Jack Brockhoff foundation (JBF grant no. 4659-2019). This work was performed in part at the Melbourne Centre for Nanofabrication (MCN) in the Victorian Node of the Australian National Fabrication Facility (ANFF). The authors also acknowledge the use of facilities at the Monash Centre for Electron Microscopy.

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Authors and Affiliations



S.G. and W.C. conceived the idea, designed the experiment, and composed the manuscript. W.C. and Z.G. supervised the whole project. S.G., X.Z., X.A.N., Q.S. and F.L. performed all of the experiments. All authors made technical comments on the manuscript. W.C. submitted the manuscript and was the lead contact.

Corresponding authors

Correspondence to Zongyuan Ge or Wenlong Cheng.

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Competing interests

W.C. declares that the v-AuNW growth on PDMS is related to the patented technology (patent nos. US11387012B2 and AU2018263276B2) that has been licensed to Soft Sense Pty Ltd in which W.C. is the founder. The other authors declare no competing interests.

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Nature Nanotechnology thanks Bin Ding and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary information.

Supplementary Video 1

Visualization demonstration of a human–robot interaction in the ‘apple-picking’ environment.

Supplementary Video 2

Visualization demonstration of a human–robot interaction in the surgical environment.

Source data

Source Data Fig. 2

SEM/TEM raw file images. Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

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Gong, S., Zhang, X., Nguyen, X.A. et al. Hierarchically resistive skins as specific and multimetric on-throat wearable biosensors. Nat. Nanotechnol. (2023).

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