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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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 https://github.com/XinZ0419/Deep-Hybrid-Spectro. Source Data are provided with this paper.
The code that supports the plots within this paper and other findings of this study are available at https://github.com/XinZ0419/Deep-Hybrid-Spectro.
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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.
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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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 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). https://doi.org/10.1038/s41565-023-01383-6