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Artificial intelligence-enabled wearable microgrids for self-sustained energy management

Nature Reviews Electrical Engineering volume 2, pages 683–693 (2025)Cite this article

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Abstract

Wearable technology has the potential to advance health monitoring by enabling continuous, multimodal sensing. A major bottleneck that hampers the adoption of such advanced health monitoring systems is the need for continuous power supply. Integrated energy-autonomous wearable microgrids offer a compelling solution to support the growing power demands of long-term health care and wellness monitoring. However, wearable microgrid systems require optimal energy management, tailored to changing environmental conditions and dynamic user demands. This Perspective highlights the transformative role of artificial intelligence (AI) in optimizing and guiding the development of powerful wearable microgrids. Leveraging intelligent, accurate prediction of future energy needs, AI empowers autonomous, on-demand, continuous power supply, able to dynamically adapt to fluctuating energy needs in diverse everyday scenarios. AI’s key roles in guiding wearable microgrids include data processing, energy budgeting, sustainable energy harvesting and tailoring systems to behavioural patterns and environmental factors. The developmental trends of AI-enabled wearable microgrids are categorized into three proposed generations, with an in-depth analysis of their advanced functions and intelligent operations. The resulting microgrids balance in real-time energy production, storage and demand to achieve greater efficiency, autonomy and sustained performance, as desired for supporting continuous health monitoring.

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Fig. 1: The operation of artificial intelligence-enabled wearable energy microgrid system.
Fig. 2: Overview of artificial intelligence models for wearables.
Fig. 3: Future generations of artificial intelligence-enabled wearable microgrids.

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Acknowledgements

This work was supported by the University of California, San Diego Center for Wearable Sensors (CWS).

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  1. These authors contributed equally: Shichao Ding, Yizhou Bian.

Authors and Affiliations

  1. Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA, USA

    Shichao Ding, Yizhou Bian, Tamoghna Saha, Muhammad Inam Khan, An-Yi Chang, Lu Yin, Sheng Xu & Joseph Wang

  2. Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA

    Sheng Xu & Joseph Wang

  3. Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA

    Sheng Xu

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S.D., Y.B., S.X. and J.W. substantially contributed to the discussion of the content. S.D., Y.B., M.I.K., S.X. and J.W. wrote the manuscript. All the authors reviewed and edited the manuscript before submission.

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Correspondence to Sheng Xu or Joseph Wang.

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Ding, S., Bian, Y., Saha, T. et al. Artificial intelligence-enabled wearable microgrids for self-sustained energy management. Nat Rev Electr Eng 2, 683–693 (2025). https://doi.org/10.1038/s44287-025-00206-1

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