Electromyography (EMG) is a widely used diagnostic technique for evaluating the electrical activity of muscles and their controlling nerves. However, conventional surface electrodes with planar structures often suffer from low spatial resolution and suboptimal signal quality. Here, 3D-shaped, substrate-free, soft, and biocompatible liquid metal electrodes (LMe) are presented as a wearable interface for neuromuscular signal recording. These electrodes enable the acquisition of high-quality EMG signals while their intrinsic mechanical softness supports long-term use in clinical settings. In a human pilot study, continuous, high-spatial-resolution EMG monitoring of musculoskeletal activity over 25 days is achieved. Furthermore, in vivo EMG monitoring in mouse models of ischemia and volumetric muscle loss demonstrated the therapeutic effects of extracellular muscle-rehabilitative drugs. This work highlights the potential of LMe for advanced drug screening and long-term clinical diagnostics.

Electronic retinal prostheses for stimulating retinal neurons are promising for vision restoration. However, the rigid electrodes of conventional retinal implants can inflict damage on the soft retina tissue. They also have limited selectivity due to their poor proximity to target cells in the degenerative retina. Here we present a soft artificial retina (thickness, 10 μm) where flexible ultrathin photosensitive transistors are integrated with three-dimensional stimulation electrodes of eutectic gallium-indium alloy. Platinum nanoclusters locally coated only on the tip of these three-dimensional liquid-metal electrodes show advantages in reducing the impedance of the stimulation electrodes. These microelectrodes can enhance the proximity to the target retinal ganglion cells and provide effective charge injections (72.84 mC cm^-2) to elicit neural responses in the retina. Their low Young's modulus (234 kPa), owing to their liquid form, can minimize damage to the retina. Furthermore, we used an unsupervised machine learning approach to effectively identify the evoked spikes to grade neural activities within the retinal ganglion cells. Results from in vivo experiments on a retinal degeneration mouse model reveal that the spatiotemporal distribution of neural responses on their retina can be mapped under selective localized illumination areas of light, suggesting the restoration of their vision.

Electromyography (EMG) is a widely used diagnostic technique for evaluating the electrical activity of muscles and their controlling nerves. However, conventional surface electrodes with planar structures often suffer from low spatial resolution and suboptimal signal quality. Here, 3D-shaped, substrate-free, soft, and biocompatible liquid metal electrodes (LMe) are presented as a wearable interface for neuromuscular signal recording. These electrodes enable the acquisition of high-quality EMG signals while their intrinsic mechanical softness supports long-term use in clinical settings. In a human pilot study, continuous, high-spatial-resolution EMG monitoring of musculoskeletal activity over 25 days is achieved. Furthermore, in vivo EMG monitoring in mouse models of ischemia and volumetric muscle loss demonstrated the therapeutic effects of extracellular muscle-rehabilitative drugs. This work highlights the potential of LMe for advanced drug screening and long-term clinical diagnostics.

Electronic retinal prostheses for stimulating retinal neurons are promising for vision restoration. However, the rigid electrodes of conventional retinal implants can inflict damage on the soft retina tissue. They also have limited selectivity due to their poor proximity to target cells in the degenerative retina. Here we present a soft artificial retina (thickness, 10 μm) where flexible ultrathin photosensitive transistors are integrated with three-dimensional stimulation electrodes of eutectic gallium-indium alloy. Platinum nanoclusters locally coated only on the tip of these three-dimensional liquid-metal electrodes show advantages in reducing the impedance of the stimulation electrodes. These microelectrodes can enhance the proximity to the target retinal ganglion cells and provide effective charge injections (72.84 mC cm^-2) to elicit neural responses in the retina. Their low Young's modulus (234 kPa), owing to their liquid form, can minimize damage to the retina. Furthermore, we used an unsupervised machine learning approach to effectively identify the evoked spikes to grade neural activities within the retinal ganglion cells. Results from in vivo experiments on a retinal degeneration mouse model reveal that the spatiotemporal distribution of neural responses on their retina can be mapped under selective localized illumination areas of light, suggesting the restoration of their vision.

The eye contains a complex network of physiological information and biomarkers for monitoring disease and managing health, and ocular devices can be used to effectively perform point-of-care diagnosis and disease management. This comprehensive review describes the target biomarkers and various diseases, including ophthalmic diseases, metabolic diseases, and neurological diseases, based on the physiological and anatomical background of the eye. This review also includes the recent technologies utilized in eye-wearable medical devices and the latest trends in wearable ophthalmic devices, specifically smart contact lenses for the purpose of disease management. After introducing other ocular devices such as the retinal prosthesis, we further discuss the current challenges and potential possibilities of smart contact lenses.

Continuous monitoring of physiological activities within the internal regions of three-dimensional (3D) organoids holds significant promise for advancing organoid-based research. However, conventional methods are constrained to capturing signals from the peripheral surfaces of organoids, limiting insights into internal dynamics. Here, we present a soft 3D bioelectrode platform for continuous intraorganoid signal monitoring. These bioelectrodes, formed via 3D printing of liquid metal, are designed with customizable geometric parameters, including height and diameter, to adapt to various organoid structures. The tissue-comparable softness of the electrodes minimizes damage to cardiac organoids, ensuring a stable interface for reliable signal recording even under dynamic deformations caused by rhythmic contractions or displacements in aqueous environments. The array configuration enables simultaneous electrocardiogram (ECG) recordings from 32 organoids. Demonstrating real-time monitoring of drug-induced ECG responses, this scalable platform highlights its potential for high-throughput drug screening.

To build in vitro tissues for therapeutic applications, it is essential to replicate the spatial distribution of cells that occurs during morphogenesis in vivo. However, it remains technically challenging to simultaneously regulate the geometric alignment and aggregation of cells during tissue fabrication. Here, we introduce the acoustofluidic bioassembly induced morphogenesis, which is the combination of precise arrangement of cells by the mechanical forces produced by acoustofluidic cues, and the morphological and functional changes of cells in the following in vitro and in vivo cultures. The acoustofluidic bioassembly can be used to create tissues with regulated nano-, micro-, and macro-structures. We demonstrate that the neuromuscular tissue fabricated with the acoustofluidic bioassembly exhibits enhanced contraction dynamics, electrophysiology, and therapeutic efficacy. The potential of the acoustofluidic bioassembly as an in situ application is demonstrated by fabricating artificial tissues at the defect sites of living tissues. The acoustofluidic bioassembly induced morphogenesis can provide a pioneering platform to fabricate tissues for biomedical applications.

To comprehend the volumetric neural connectivity of a brain organoid, it is crucial to monitor the spatiotemporal electrophysiological signals within the organoid, known as intra-organoid signals. However, previous methods risked damaging the three-dimensional (3D) cytoarchitecture of organoids, either through sectioning or inserting rigid needle-like electrodes. Also, the limited numbers of electrodes in fixed positions with non-adjustable electrode shapes were insufficient for examining the complex neural activity throughout the organoid. Herein, we present a magnetically reshapable 3D multi-electrode array (MEA) using direct printing of liquid metals for electrophysiological analysis of brain organoids. The adaptable distribution and the softness of these printed electrodes facilitate the spatiotemporal recording of intra-organoid signals. Furthermore, the unique capability to reshape these soft electrodes within the organoid using magnetic fields allows a single electrode in the MEA to record from multiple points, effectively increasing the recording site density without the need for additional electrodes.

Stretchable electrodes are crucial healthcare device components for electrophysiology signal sensing. Compared to conventional rigid gel electrodes, stretchable electrodes are advantageous for long-term monitoring with superior skin conformality. However, current devices often show limited compliance and adhesive reliability, especially during human motion. Here, a skin-tailored adhesive bio-electrode (STABL) is fabricated using a substrate designed to comply with the deformation behavior of wrist skin. The substrate patch possesses a negative Poisson's ratio (PR) through an auxetic structured glass fabric reinforcement embedded in a soft, adhesive elastomer film. Based on the direct measurement of wrist skin deformation behavior, the chemical composition and geometry of the substrate patch are optimized to match the negative PR of the skin. Using this tailored substrate, STABL is fabricated with EGaIn liquid metal conductors and exhibits superior stability in electrophysiology communication during repetitive wrist motion compared to conventional pristine PDMS substrates. This result highlights the potential of skin-tailored auxetic substrates and liquid metal electrodes for reliable, long-term wearable bioelectronics applications.

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.

The advancement of wearable bioelectronics has significantly improved real-time biosignal monitoring, enabling continuous health tracking and providing personalized medical insights. However, the sheer volume and complexity of biosignal data collected over extended periods, along with noise, missing values, and environmental artifacts, present significant challenges for accurate analysis. Machine learning (ML) plays a crucial role in biosignal analysis by improving processing capabilities, enhancing monitoring accuracy, and uncovering hidden patterns and relationships within datasets. Effective ML-driven biosignal analysis requires careful model selection, considering data preprocessing needs, feature extraction strategies, computational efficiency, and accuracy trade-offs. This review explores key ML algorithms for biosignal processing, providing guidelines on selecting appropriate models based on data characteristics, processing goals, computational efficiency, and accuracy requirements. We discuss data preprocessing techniques, ML models (clustering, regression, classification), and evaluation methods for assessing the accuracy and reliability of ML-driven analyses. Furthermore, we introduce ML applications in health monitoring, disease diagnosis, and prediction across neurological, cardiovascular, biochemical, and other biosignals. Finally, we discuss the integration of ML with wearable bioelectronics and its revolutionary impact on future healthcare systems.