This Review discusses hardware advances, system integration, reliability and translational challenges to outline pathways towards safe, scalable and clinically deployable intelligent healthcare systems.
Wearable sensors have emerged as important platforms for noninvasive, real‐time, and continuous monitoring of physiological information, with growing applications in personalized healthcare, human‐machine interfaces (HMIs), and robotic empowerment. Recent advances in flexible and stretchable materials, device integration, and data‐driven analysis have substantially expanded their functionality, wearability, and practical utility. In particular, artificial intelligence (AI) is transforming wearable sensors from passive data‐acquisition devices into intelligent systems capable of signal interpretation, feature extraction, pattern recognition, and decision support. Meanwhile, progress in flexible substrates, functional sensing materials, and hybrid device architectures has enabled lightweight, conformal, and mechanically compliant wearable systems with improved signal quality and long‐term usability. In this review, wearable sensors are discussed in three major categories: biophysical, biochemical, and electrophysiological sensors. We summarize recent developments in their material foundations, sensing mechanisms, and representative device designs, and further highlight their emerging applications in personalized healthcare, human‐machine interaction, and robotic empowerment. Finally, current challenges and future opportunities in materials design, device integration, and AI‐enabled analysis are discussed.
Tian-Qi Yang, Han-Kun Zhu, Zhi-Hui Pan et al.· Rare Metals· 0 citations
Wearable technologies now permit near-continuous measurement of physiological and behavioral parameters under free-living conditions. Combined with advances in artificial intelligence (AI), these devices support the development of wearable-derived digital biomarkers that may shift healthcare from a reactive to a preventive and personalized model. This narrative review synthesizes current evidence on the technological foundations, AI-based signal processing, clinical applications, implementation challenges, and future directions of wearable-derived digital biomarkers. Evidence supports their use across cardiovascular disease, diabetes and obesity, neurological and mental health conditions, sleep and respiratory medicine, and remote patient monitoring, where continuous data streams can inform early detection, individualized risk prediction, treatment optimization, and clinical decision-making. We propose a conceptual framework describing how wearable sensing, continuous physiological data acquisition, and AI-based analytics translate into clinically actionable digital biomarkers. At the same time, we argue that enthusiasm has outpaced evidence: few candidate biomarkers have undergone prospective validation in diverse populations, analytical performance varies substantially across devices and skin tones, and demonstration of improved clinical outcomes remains rare. Data quality, validation, standardization, interoperability, algorithm transparency, privacy, cybersecurity, regulatory oversight, and equitable access all constrain clinical adoption. Emerging developments in explainable AI, multimodal data integration, digital twins, and predictive analytics may address some of these constraints. Wearable-derived digital biomarkers hold genuine potential for proactive, patient-centered, data-driven care, but realizing that potential will depend less on new sensors than on rigorous validation, standardization, and equitable implementation.
Damilola Alabi, Anyebe Daniel Ameh, D. Okon· Journal of Biomedicine and B...· 0 citations
Clinical translation remains limited by data security and privacy risks, insufficient standardization and regulatory alignment, long-term stability and biocompatibility concerns, and uneven validation maturity across technologies.
Ji-Rui Wen, Jiang Wu, Yi Yang et al.· Chinese Medical Journal· 0 citations
Wearable bioelectronics are increasingly expected to support personalized, multiparametric, and closed-loop health monitoring, yet most systems rely on monolithic integration that limits task adaptability and increases hardware redundancy and environmental burden. Here, we present a reusable modular wearable platform that decouples a common digital back end from interchangeable analog front-end and sensing modules. This architecture enables on-demand reconfiguration across electrophysiological, mechanical, thermal, and electrochemical monitoring tasks without redesigning the complete system. Manufacturing-stage carbon-footprint analysis shows that selective reuse of carbon-intensive back-end electronics reduces emissions compared with monolithic integration, with greater benefits as task complexity increases. The platform captures muscle activity, heart-rate dynamics, electroencephalogram α-band features, body temperature, tactile, and glucose levels and wirelessly links physiological sensing to a stretchable chip-on-array LED display for real-time on-skin visualization and threshold-triggered alerts. This work provides a reusable, task-adaptive, and low-carbon hardware framework for scalable personalized wearable bioelectronics.
Xianyu Wang, Xuyang Feng, Haoyang Wang et al.· ACS Sensors· 0 citations
Background: Chronic disease management, an ageing global population, and the aftermath of the COVID-19 pandemic have pushed real-time, continuous health monitoring from a research curiosity toward routine clinical practice. Artificial intelligence (AI), the Internet of Medical Things (IoMT), edge computing, and next-generation wireless networks are converging to enable systems that sense, interpret, and act on physiological data outside the traditional hospital setting. Methods: This is a narrative review, not a PRISMA-guided systematic or scoping review; it synthesises 75 sources (a mixture of primary studies, systematic/scoping reviews, and meta-analyses), individually verified against their published record. The topics span IoMT system architecture and security, wearable and implantable sensing, deep learning for electrocardiogram (ECG), fall-related and human-activity signal analysis, edge and TinyML deployment, federated learning, blockchain-based health-record security, medical imaging diagnostics, explainable AI (XAI), continuous glucose monitoring, 5G/6G-enabled telemonitoring, digital twins, consumer-grade and contactless cardiac sensing, neurological and mental health monitoring, and the materials, regulatory, and acute care infrastructure surrounding real-time deployment. Studies were organised into a five-layer architectural taxonomy spanning perception, edge, network, cloud, and application layers. Results: Reported accuracies for deep learning models on ECG arrhythmia classification range from 91% to 99.5% across the reviewed studies, but the figures come from different datasets, class definitions, and validation protocols and are therefore not directly comparable; within this heterogeneous evidence, edge-deployed models report accuracies in the 85–96% range at substantially reduced power budgets. Deep-learning-based fall detection and chest radiograph classification are each reported, in the individual studies reviewed, to outperform threshold-based or classical alternatives, though this has not been established through head-to-head comparison across the full evidence base. Federated learning and blockchain are discussed as technical mechanisms that can contribute to data privacy and record integrity; neither constitutes regulatory compliance with frameworks such as HIPAA or the GDPR on its own. Persistent obstacles identified across the reviewed literature include dataset heterogeneity, limited external clinical validation, energy-constrained edge hardware, low clinician trust in opaque models, and fragmented interoperability standards. Conclusions: The evidence reviewed here is consistent with, but does not by itself establish, a layered, privacy-preserving, and explainable architecture that couples lightweight on-device inference with federated or blockchain-secured cloud learning as a design direction for future real-time healthcare monitoring systems. Future work should prioritise standardised benchmarking, prospective clinical validation, and regulatory-aligned data–governance frameworks.
On-skin non-invasive Wearable Health-Monitoring Devices (WHMDs) have rapidly evolved from laboratory prototypes into commercially viable systems capable of continuously tracking physiological and biochemical signals. By integrating epidermal temperature sensors, electrophysiological electrodes, biochemical sensing platforms, low-power electronics, and wireless communication technologies, these systems are emerging as key enablers of personalized and decentralized healthcare through the continuous acquisition of clinically relevant information directly from the skin surface. In this Perspective, we present our view on the state-of-the-art across the key technological pillars that define modern on-skin WHMDs, including non-invasive sensing strategies, advanced materials, processing and wireless communication units, energy-storage solutions, energy-harvesting techniques, and power-management architectures, with a particular focus on technologies that have already reached high Technology Readiness Levels (TRLs). We highlight how the next-generation of on-skin WHMDs must balance performance with sustainability and long-term reliability. This includes the adoption of biodegradable and recyclable materials, low-power and reconfigurable electronics, solid-state batteries, and hybrid energy-harvesting systems. By aligning technological innovation with human-centric and eco-friendly design principles, on-skin WHMDs can evolve into scalable, equitable, and environmentally responsible tools for future digital healthcare.
Francisco J. Romero, Isabel Blasco-Pascual, A. Salinas-Castillo et al.· Italian National Conference...· 0 citations
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