AI-based metamaterial design for wearables

Abstract

Continuous monitoring of physiological parameters has remained an essential component of patient care. With an increased level of consciousness regarding personal health and wellbeing, the scope of physiological monitoring has extended beyond the hospital. From implanted rhythm devices to non-contact video monitoring for critically ill patients and at-home health monitors during Covid-19, many applications have enabled continuous health monitorization. Wearable health sensors have allowed chronic patients as well as seemingly healthy individuals to track a wide range of physiological and pharmacological parameters including movement, heart rate, blood glucose, and sleep patterns using smart watches or textiles, bracelets, and other accessories. The use of metamaterials in wearable sensor design has offered unique control over electromagnetic, mechanical, acoustic, optical, or thermal properties of matter, enabling the development of highly sensitive, user-friendly, and lightweight wearables. However, metamaterial design for wearables has relied heavily on manual design processes including human-intuition-based and bio-inspired design. Artificial intelligence (AI)-based metamaterial design can support faster exploration of design parameters, allow efficient analysis of large data-sets, and reduce reliance on manual interventions, facilitating the development of optimal metamaterials for wearable health sensors. Here, AI-based metamaterial design for wearable healthcare is reviewed. Current challenges and future directions are discussed. Artificial intelligence (AI)-based metamaterial design can support faster exploration of design parameters, allow efficient analysis of large data-sets, and reduce reliance on manual interventions, facilitating the development of optimal metamaterials for wearable health sensors. Here, AI-based metamaterial design for wearable healthcare is reviewed. Current challenges and future directions are discussed.