With the rapid advancements in artificial intelligence, hydrogel-based sensors, acting as the critical interfaces between biological organisms and the digital realm, are becoming essential for emerging human-machine interactions (HMI) and health-monitoring platforms. However, conductive hydrogels still encounter trade-offs between "conductivity-mechanics" coupling as well as reliability challenges in complex environments. Specifically, enhanced carrier pathways often compromise the polymer network strength and fatigue tolerance. In this study, we constructed a superior ionic-electronic conductive network by incorporating hierarchically self-assembled magnesium boride into a copolymer matrix under ultraviolet light irradiation. The hierarchical magnesium boride structures exhibit inherently high conductivity, while their anisotropic three-dimensional architecture promotes strong mechanical interlocking and multi-point coordination effects. These characteristics endow the composite hydrogel with robust adhesion (~200 kPa), remarkable stretchability (~1383 %), rapid responsiveness (<20 ms), and self-healing capability. Utilizing these advantages, the hydrogel enables high-fidelity physiological signal monitoring and versatile strain sensing, ranging from subtle pulse wave detection to Morse code recognition. Furthermore, a hand-wearable control system combined with a lightweight convolutional neural network (CNN) algorithm enables precision gesture recognition with an overall classification accuracy of 92.15% and stable control of drone posture. This work validates the reliability of hydrogel electronic skin in complex HMI scenarios, and lays the groundwork for future applications in hazardous environment operations.

Accurate diagnosis of Alzheimer’s Disease (AD) is essential for early intervention. Traditional methods relying on single-modality data often fail to capture the complexity of the disease, limiting diagnostic accuracy. Integrating multimodal data, such as structural Magnetic Resonance Imaging (sMRI) and Single Nucleotide Polymorphism (SNP) data, can provide a more comprehensive understanding of AD. However, existing multimodal fusion methods often overlook the intricate relationships among different data types, resulting in suboptimal performance. To address these challenges, we propose a novel graph-based multimodal fusion framework for AD prediction. The framework constructs brain and gene ontology networks using domain-specific prior knowledge from sMRI and SNP data. It leverages Graph Convolutional Networks (GCN) to extract deep features from each modality and employs a cross-attention mechanism to dynamically weigh feature importance across modalities. Additionally, a Correlation-Aware Learning (CAL) module explicitly models inter-modal correlations, enhancing the interpretability and robustness of the fusion. We validate the effectiveness of our framework using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Results show that our framework significantly outperforms traditional methods in classification accuracy and feature representation. Our method enables accurate AD diagnosis by integrating multimodal data and explicitly modeling inter-modal correlations. It enhances the interpretability of multimodal integration and provides new insights into the genetic and structural mechanisms underlying AD, serving as a valuable tool for clinical diagnosis and research in neurodegenerative diseases.

Cardiovascular diseases are a prominent cause of mortality, emphasizing the need for early prevention and diagnosis. Utilizing artificial intelligence (AI) models, heart sound analysis emerges as a noninvasive and universally applicable approach for assessing cardiovascular health conditions. However, real-world medical data are dispersed across medical institutions, forming “data islands” due to data sharing limitations for security reasons. To this end, federated learning (FL) has been extensively employed in the medical field, which can effectively model across multiple institutions. Additionally, conventional supervised classification methods require fully labeled data classes, e.g., binary classification requires labeling of positive and negative samples. Nevertheless, the process of labeling healthcare data is time-consuming and labor-intensive, leading to the possibility of mislabeling negative samples. In this study, we validate an FL framework with a naive positive-unlabeled (PU) learning strategy. Semisupervised FL model can directly learn from a limited set of positive samples and an extensive pool of unlabeled samples. Our emphasis is on vertical-FL to enhance collaboration across institutions with different medical record feature spaces. Additionally, our contribution extends to feature importance analysis, where we explore 6 methods and provide practical recommendations for detecting abnormal heart sounds. The study demonstrated an impressive accuracy of 84%, comparable to outcomes in supervised learning, thereby advancing the application of FL in abnormal heart sound detection.

Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade. Nevertheless, lacking on standard open-access database made it difficult to maintain a sustainable and comparable research before the first release of the PhysioNet CinC Challenge Dataset. However, inconsistent standards on data collection, annotation, and partition are still restraining a fair and efficient comparison between different works. To this line, we introduced and benchmarked a first version of the Heart Sounds Shenzhen (HSS) corpus. Motivated and inspired by the previous works based on HSS, we redefined the tasks and make a comprehensive investigation on shallow and deep models in this study. First, we segmented the heart sound recording into shorter recordings (10 s), which makes it more similar to the human auscultation case. Second, we redefined the classification tasks. Besides using the 3 class categories (normal, moderate, and mild/severe) adopted in HSS, we added a binary classification task in this study, i.e., normal and abnormal. In this work, we provided detailed benchmarks based on both the classic machine learning and the state-of-the-art deep learning technologies, which are reproducible by using open-source toolkits. Last but not least, we analyzed the feature contributions of best performance achieved by the benchmark to make the results more convincing and interpretable.

The sound generated by body carries important information about our health status physically and psychologically. In the past decades, we have witnessed a plethora of successes achieved in the field of body sound analysis. Nevertheless, the fundamentals of this young field are still not well established. In particular, publicly accessible databases are rarely developed, which dramatically restrains a sustainable research. To this end, we are launching and continuously calling for participation from the global scientific community to contribute to the Voice of the Body (VoB) archive. We aim to build an open access platform to collect the well-established body sound databases in a well standardized way. Moreover, we hope to organize a series of challenges to promote the development of audio-driven methods for healthcare via the proposed VoB. We believe that VoB can help break the walls between different subjects toward an era of Medicine 4.0 enriched by audio intelligence.