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.

AI-Based Metamaterial Design

“…Such regulations are essential to safeguard patient rights. While high-quality data is often required to train AI models, some models have been developed using limited and/or unstructured medical data. , Alternatively, AI-based design can be employed to develop diagnostic sensors or healthcare monitors with improved functionalities. , Moreover, several groups have reported AI-based metamaterial design in healthcare.…”

“…The fields of acoustics, optics, healthcare, and power systems emerge as pivotal avenues for innovation and have the potential to drastically improve the quality of human life. Metamaterials have gained attention in recent years due to their potential to revolutionize various fields. , They offer unusual properties including unique electromagnetic, thermal, and acoustic (Table ) , and mechanical characteristics (Figure A). , They offer a promising solution to overcome the limitations of conventional materials. − Moreover, with the aging population and rising medical demands, healthcare can greatly benefit from AI-driven solutions . WHO expects to present an upcoming global scarcity of healthcare excess by 2030 .…”

“…Models should be able to perform accurate predictions while avoiding overfitting . The assessment of the models is performed via performance indices, bootstrapping, and cross-validation. ,, By utilizing AI, researchers can efficiently explore complex parameter spaces, enabling the discovery of metamaterials . Several questions regarding the data availability to train models remain to be addressed for design accuracy.…”

“…38,39 This article is licensed under CC-BY 4 The literature contains three subcategories of AI usage in metamaterial design: data analysis, algorithm development, and model evaluation. 1,40 Before any AI-based application, data sets undergo data preprocessing and assessment. Raw data in metamaterials are often harvested from experimental or numerical simulations and can be incomplete or inconsistent, or contain background noise.…”

“…In contrast, AI-based procedures leverage machine learning (ML) algorithms to automate and accelerate the design process, facilitating the discovery of novel metamaterial structures with enhanced performance . The frameworks usually involve initial training in which computational models learn the desired properties of the metamaterial and its structural parameters, thus generating new designs or optimizing existing ones to achieve specific objectives. , Regression and probabilistic algorithms evaluate complex structures and combinations of unit materials, while probability algorithms are primarily utilized for designing novel metamaterials. , The literature contains three subcategories of AI usage in metamaterial design: data analysis, algorithm development, and model evaluation. , Before any AI-based application, data sets undergo data preprocessing and assessment. Raw data in metamaterials are often harvested from experimental or numerical simulations and can be incomplete or inconsistent, or contain background noise .…”