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

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. Wearable multimodal monitoring systems deliver continuous insight into patients’ health status but are constrained by power needs. Next-generation artificial intelligence-enabled wearable microgrids can drive sustainable energy harvesting, intelligent budgeting and adaptive management for autonomous, on-demand power delivery for wearable devices.