Machine Learning Enhanced Multimodal Bioelectronics: Advancement Toward Intelligent Healthcare Systems

Abstract

Multimodal bioelectronics has enabled comprehensive understanding of complex biological states by capturing diverse biosignals and interacting with the physiological changes with the biological environment. These systems are categorized into multi-sensing devices, which collect and analyze multiple biosignals concurrently, and multifunctional devices, which provide dynamic feedback through mechanisms such as drug release, electrical stimulation, and mechanical actuation. However, the acquisition and integrated analysis of heterogeneous data from these biosensors pose significant computational challenges, necessitating advanced analytical frameworks to extract meaningful insights. Machine learning has emerged as an essential tool for data interpretation and real-time decision-making through addressing challenges in broad data integration, feature extraction, and predictive modeling. Implementation of machine learning to multimodal devices extend their capabilities beyond conventional biosensors, performing crossmodal correlation analysis, real-time anomaly detection, and situation-dependent feedback. This review explores recent progress in multimodal bioelectronics and the integration of machine learning in multimodal bioelectronics. Moreover, evaluations of various machine learning applications are conducted by discussing key advancements, challenges, and future research directions in intelligent multimodal biosensor technology, which holds immense potential to revolutionize biomedical applications, facilitating the development of autonomous and responsive health monitoring systems.