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Artificial intelligence-powered electronic skin

Nature Machine Intelligence volume 5, pages 1344–1355 (2023)Cite this article

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Abstract

Skin-interfaced electronics is gradually changing medical practices by enabling continuous and non-invasive tracking of physiological and biochemical information. With the rise of big data and digital medicine, next-generation electronic skin (e-skin) will be able to use artificial intelligence (AI) to optimize its design as well as uncover user-personalized health profiles. Recent multimodal e-skin platforms have already used machine learning algorithms for autonomous data analytics. Unfortunately, there is a lack of appropriate AI protocols and guidelines for e-skin devices, resulting in overly complex models and non-reproducible conclusions for simple applications. This Review aims to present AI technologies in e-skin hardware and assess their potential for new inspired integrated platform solutions. We outline recent breakthroughs in AI strategies and their applications in engineering e-skins as well as understanding health information collected by e-skins, highlighting the transformative deployment of AI in robotics, prosthetics, virtual reality and personalized healthcare. We also discuss the challenges and prospects of AI-powered e-skins as well as predictions for the future trajectory of smart e-skins.

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Fig. 1: Overview of AI-powered e-skin and ML pipelines.
Fig. 2: Emerging sensors in e-skin for health monitoring and robotics.
Fig. 3: ML optimizations for e-skin designs.
Fig. 4: AI-powered e-skin for HMIs.
Fig. 5: AI-powered e-skins for personalized healthcare and predictive disease diagnostics.

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Acknowledgements

This work was funded by Office of Naval Research grants N00014-21-1-2483 and N00014-21-1-2845, Army Research Office grant W911NF-23-1-0041, National Institutes of Health grants R01HL155815 and R21DK13266, National Science Foundation grant 2145802 and National Academy of Medicine Catalyst Award. C.X. was supported by Amazon AI4Science Fellowship.

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  1. Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

    Changhao Xu, Samuel A. Solomon & Wei Gao

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  1. Changhao Xu
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  2. Samuel A. Solomon
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Xu, C., Solomon, S.A. & Gao, W. Artificial intelligence-powered electronic skin. Nat Mach Intell 5, 1344–1355 (2023). https://doi.org/10.1038/s42256-023-00760-z

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