Search Results (330)

Wearable electronic systems require compact and sustainable power sources together with reliable motion-sensing functions. This study presents a gear-driven plantar energy harvester that integrates biomechanical energy conversion with self-sensing locomotion recognition. The device converts low-frequency vertical foot loading into rotary motion through a wedge–lever transmission and amplifies the rotational speed using a multistage gear train with a total transmission ratio of 12. A one-way bearing enables directional power transmission during loading and prevents reverse rotation during recovery. The generated voltage serves both as the electrical output and as the sensing signal for locomotion recognition. Human-subject experiments were conducted under six locomotion modes: walking at 1, 2 and 3 m/s; running; ascending; and descending. Voltage signals were sampled at 2000 Hz and segmented into overlapping sequences. A CNN–LSTM model was used to extract local waveform features and temporal dependencies from the nonstationary signals. The model achieved an overall recognition accuracy of 98.8%, with most errors occurring between ascending and descending. The results demonstrate that a single plantar device can simultaneously harvest biomechanical energy and provide motion-related information, offering a compact solution for integrated energy harvesting and self-sensing in wearable systems. Full article

Flexible wearable piezoelectric sensors have attracted increasing attention in human motion monitoring and motion classification applications due to their self-powered sensing capability and rapid response. In this work, poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) flexible piezoelectric films were fabricated using a phase separation method with different loading masses of PVDF-HFP to regulate the crystal structure and output signal characteristics of the films. X-ray diffraction and Fourier-transform infrared spectroscopy analyses demonstrated that an appropriate mass of PVDF-HFP promoted the formation of polar β-phase crystals, and the optimized film exhibited a β-phase content of 86.81%. The prepared films generated stable and distinguishable response signals under different gait conditions, indicating high potential for flexible motion sensing. Furthermore, machine learning-assisted motion classification was preliminarily performed based on the acquired sensing signals, achieving an accuracy above 90%. This work demonstrates the potential of phase-separated PVDF-HFP films for flexible gait sensing and wearable motion recognition applications. Full article

Two-dimensional transition-metal carbides and nitrides (MXenes) are increasingly adopted in advanced electronic devices, where their metallic conductivity, optical tunability, and chemically addressable surfaces support next-generation multifunctional optoelectronics. Their practical performance, however, depends not only on their intrinsic properties but also on the heterogeneous interfaces where charges, photons, and ions interact. Unlike earlier reviews organized around synthesis routes or separate device categories, this review takes interfacial chemistry as a single organizing principle and follows it from surface terminations through to integrated systems. The structural and surface-chemical characteristics of MXenes are described first, showing how dynamic terminations and interfacial dipoles regulate work functions and energy-level alignment. We then discuss molecular functionalization, defect passivation, and heterojunction formation as strategies for reducing Schottky barriers and improving charge-transfer kinetics. Optoelectronic platforms built on these engineered interfaces, including high-efficiency photovoltaics, broadband photodetectors, and stretchable wearable systems, are subsequently detailed, together with emerging architectures that merge self-powered sensing with neuromorphic visual functions, a scope seldom treated alongside conventional devices in previous surveys. By connecting surface chemistry with device integration, this review outlines a materials-to-systems pathway toward more reliable and scalable MXene-based optoelectronic technologies. Full article

Background: Obesity is a chronic, multifactorial disease that demands personalized and sustainable management approaches. Digital health technologies, such as artificial intelligence, wearable devices, mobile health apps, and virtual reality (VR), may support obesity care by providing enhanced behavioral monitoring, personalized feedback, and patient engagement. Objective: This critical narrative review discusses the current evidence on artificial intelligence, wearable technologies, and VR in the context of precision nutrition and obesity management and their possible clinical applications and limitations. Method: A critical narrative review was conducted using peer-reviewed literature published between 2019 and 2026 and identified through PubMed and Google Scholar. Search terms included combinations of “precision nutrition,” “personalized nutrition,” “obesity,” “weight management,” “metabolic health,” “digital health,” “artificial intelligence,” “machine learning,” “mobile health,” “wearable devices,” and “omics” using Boolean operators. Evidence from randomized controlled trials, systematic reviews, meta-analyses, and key conceptual studies was critically synthesized due to substantial heterogeneity in interventions and outcomes. Result: Wearables and mobile applications can enable continuous self-monitoring of physical activity, dietary intake, sleep, and physiological measures. Artificial intelligence may improve dietary personalization, risk prediction, glycemic control, and adaptive feedback. VR offers an immersive way to tackle behavioral and cognitive mechanisms related to overeating such as cravings, food cue reactivity, and inhibitory control. However, the evidence is heterogeneous, with many studies limited by short follow-up periods, small samples, variable adherence, and insufficient clinical validation. Conclusions: Artificial intelligence, wearable technologies, and VR are promising tools for precision obesity management, but their long-term clinical effectiveness remains uncertain. Future research should prioritize adequately powered trials, longer follow-up, standardized outcomes, transparent algorithms, ethical data governance, and integration with multidisciplinary nutrition and obesity care. Full article

The rapid development of wearable electronics has created a demand for flexible, lightweight, and sustainable power-supply technologies. The persistent temperature difference between the human body and the environment provides a low-grade thermal source for thermoelectric energy harvesting. However, traditional flexible thermoelectric devices still face limited self-supporting capabilities and difficulties in maintaining sufficiently low cold-side temperatures. Here, we designed a passively radiative-cooled thermoelectric film (PRT film) by integrating a PAM/PEDOT:PSS self-supporting thermoelectric composite layer with a polymer metamaterial radiative cooling (PMRC) film. The PAM/PEDOT:PSS layer serves as a self-supporting thermoelectric conversion component for harvesting low-grade heat, while the PMRC film layer acts as a passive cold-side regulator without energy input to lower the cold-side temperature and enhance the temperature gradient. By optimizing the PAM content, the PAM/PEDOT:PSS composite material with 85 wt% PAM achieved the highest power factor of 72.3 μW m⁻¹ K⁻². Under a temperature difference of 39 °C, the optimized PAM/PEDOT:PSS sample provided an open-circuit voltage of 0.47 V, a maximum output power of 1.1 μW, and a power density of 11.2 μW cm⁻². According to the temperature-difference enhancement measured in experiments and the independently obtained load characteristics, the integration of PMRC films is expected to increase the maximum output power from 1.1 to 1.4 μW, with the corresponding power density rising from 11.2 to 14.25 μW cm⁻², representing a 27.2% enhancement. This work demonstrates the feasibility of passive radiative cold-side regulation in enhancing low-level thermoelectric energy harvesting for wearable applications. Full article

This review systematically summarizes recent advances in moisture-electric generation technology from four perspectives: functional-material modification, device-structure design, multi-source energy-harvesting strategies, and practical applications. It discusses the classification and modification of moisture-responsive functional materials; analyzes how device architectures regulate ion transport, reviews the mechanisms of coupling moisture energy with solar, thermal, and mechanical energy; and summarizes representative applications of moisture-electric generators (MEGs) in power supply, self-powered sensing, and wearable electronics. The review further compares the advantages and limitations of different material and structural strategies and evaluates challenges related to output performance, environmental adaptability, long-term stability, power management, and scalable fabrication. Finally, future research directions are discussed to support the practical development of MEGs. Full article

In recent years, the rapid development of flexible electronics, smart materials, and micro/nanofabrication technologies has greatly promoted the advancement of flexible pressure sensors. These sensors have achieved significant improvements in sensitivity, detection range, stability, and functional integration, demonstrating great potential for applications in wearable electronics, smart healthcare, and human–machine interaction. This review summarizes recent progress in flexible pressure sensors in terms of sensing mechanisms, functional materials, structural designs, and intelligent applications. First, the working principles and performance characteristics of typical sensing mechanisms, including piezoresistive, capacitive, piezoelectric, triboelectric, iontronic, self-powered, and electrochemical sensing, are introduced and compared. Then, the development of key materials, such as flexible substrates, carbon-based nanomaterials, metal nanostructures, conductive hydrogels, and MXenes, is summarized. The effects of structural designs, including serpentine, three-dimensional porous, crack, wrinkle, and Kirigami structures, on flexibility, stretchability, sensitivity, detection range, and cycling stability are also discussed. Furthermore, the applications of flexible pressure sensors in pulse monitoring, blood pressure monitoring, human motion detection, cardiovascular health assessment, disease diagnosis, gesture recognition, human–machine interaction, and electronic skin are reviewed. Finally, the major challenges and future perspectives of flexible pressure sensors are discussed. Full article

The von Neumann architecture, due to the physical separation between memory and processor, has limited the development of data-intensive applications. Neuromorphic computing technologies inspired by the brain’s parallel and event-driven operation mechanisms have enabled low-power in-memory computing. Memristors with tunable conductance can emulate biological synapses, while flexible memristors further offer mechanical flexibility, making them suitable for wearable electronics and intelligent sensing systems. This review systematically summarizes the switching mechanisms of flexible neuromorphic memristors, including conductive filaments, interface effects, ferroelectricity, phase change, and multiple synergistic mechanisms. It categorically discusses natural and bio-derived materials, synthetic organic/polymer materials, and inorganic functional materials, and introduces strategies for enhancing flexibility. The article also covers device architectures such as sandwich structures, crossbar arrays, and fiber-based textile structures, along with low-temperature fabrication techniques. Finally, it reviews recent advances in neuromorphic computing, in-memory computing, biomimetic sensing, and biomedical wearable systems. Challenges related to mechanical stability and device uniformity are analyzed, and future directions toward self-healing materials and integrated sensing-storage-computing systems are outlined. This comprehensive review bridges the gap between material innovation and system-level integration in flexible neuromorphic memristors, providing a valuable roadmap for accelerating the development of next-generation wearable artificial intelligence, edge computing, and bio-integrated electronic technologies. Full article

Soft piezoelectric electronic skins are expected to work reliably under repeated joint bending, where both stretchability and stress transfer efficiency are important. In this work, a BaTiO₃/PDMS piezoelectric electronic skin, denoted as BPPE-skin, was prepared by a blade-coating method. Rather than only increasing the amount of piezoelectric filler, this study focuses on how the PDMS matrix composition affects the mechanical deformation and electrical response of the composite. The PDMS base-to-curing-agent ratio was adjusted from 5:1 to 30:1, and the BaTiO₃ loading was varied from 10 to 90 wt%. Tensile tests show that increasing the base-to-curing-agent ratio reduces the elastic modulus and improves stretchability, while excessive softening weakens effective stress transfer. As a result, the voltage output reaches its highest value at a 10:1 ratio. Increasing the BaTiO₃ content further enhances the output, mainly because more active piezoelectric particles participate in electromechanical conversion. The optimized film, containing 90 wt% BaTiO₃ with a 10:1 PDMS ratio, maintains good flexibility and can be attached conformally to different body joints. Tests on the knee, elbow, wrist, and fingers produce distinct voltage patterns during bending motions, indicating the potential of BPPE-skin for wearable, self-powered human motion monitoring. Full article

Soft actuators made from soft organic materials that can exhibit biomimetic motions, such as artificial muscles, have recently attracted substantial interest for applications in soft robotics, wearable electronics, and haptic interfaces, where flexible, adaptive, and biocompatible actuation is essential. In this context, piezoelectric and electroactive materials have emerged as important platforms for electromechanical transduction; however, conventional piezoelectric materials are predominantly ceramic-based, making them brittle, limiting achievable strain, and often requiring high operating voltages. Ionic liquids (ILs) have emerged as promising alternatives due to their high ionic conductivity, negligible volatility, and wide thermal and electrochemical stability windows. Notably, recent reports of piezoelectric behavior in ionic liquids, representing the first observation of such effects in liquid systems, have opened new opportunities for IL-based soft actuators. These advances highlight the potential of IL-based materials for developing next-generation soft robotic systems with enhanced functionality. Accordingly, there is a growing interest in designing sustainable actuators that integrate self-healing, self-powering, and self-actuating capabilities while maintaining efficient energy use, long-term stability, and user-specific adaptability. This review summarizes recent progress in IL-based soft actuators, including material design, actuation mechanisms, sensing integration, and control strategies, and also discusses current challenges and future research directions in this emerging field. Full article

Portable sensing systems are increasingly important in biological and chemical analyses because they can provide analytical information at the point of decision-making. While traditional laboratory methods remain crucial for reference measurements, regulatory validation, and high-precision quantification, portable systems emphasize rapid response, convenience, cost-effectiveness, robustness, and relevance to decision-making. This paper views portable sensing systems as integrated analytical platforms rather than isolated sensing elements. The paper discusses recognition elements, including enzymes, antibodies, nucleic acid probes, aptamers, molecularly imprinted polymers, nanomaterials, and hybrid recognition interfaces, as well as electrochemical, optical, mass-sensitive, thermal, field-effect, and hybrid sensing technologies. Furthermore, this paper reviews platform designs, including paper-based analytical devices, chip lab systems, smartphone-assisted sensors, wearable and flexible sensors, handheld instruments, and wireless sensor networks. It explores their applications in sample handling, calibration, data processing, and field deployment. Applications of this technology include point-of-care diagnostics, pathogen detection, wearable health monitoring, agriculture, veterinary medicine, environmental monitoring, food safety, industrial process control, forensic analysis, public safety, and occupational exposure assessment. The report focuses on sample acquisition, miniaturized preparation, reagent storage, matrix interference, calibration transfer, signal conditioning, machine learning, cloud platforms, analytical validation, and decision support. Furthermore, it identifies key obstacles to translating academic prototypes into industrial products, including reproducibility, stability, manufacturability, ease of use, cybersecurity, regulatory approval, and market acceptance. Future development requires fully integrated sample-to-result systems, multimodal sensing, artificial intelligence, sustainable single-use materials, self-powered devices, and system-level validation under real-world operating conditions. Full article

Flexible metamaterial absorbers have exhibited tremendous potential for applications in intelligent wearable devices and radar stealth protection due to their remarkable electromagnetic response characteristics and mechanical conformal adaptability. However, conventional metamaterial development relies heavily on iterative full-wave simulations, which not only incurs prohibitive computational costs but also hinders the efficient identification of global optima within high-dimensional geometric parameter spaces. To address these challenges, this paper proposes an inverse design framework based on a deep learning-powered adversarial diffusion model. By integrating residual blocks and self-attention mechanisms within the U-Net architecture, the model’s capacity to capture global spectral features is significantly enhanced. Furthermore, the introduction of a discriminator for adversarial fine-tuning optimizes generation quality, resulting in a 22.46% reduction in the target loss function compared with conventional approaches. This method effectively resolves the “one-to-many” inverse mapping challenge between spectral requirements and geometric structures. Experimental results demonstrate that the designed absorber exhibits excellent polarization insensitivity and maintains efficient, stable absorption performance even under large-angle conformal bending. Moreover, a multi-sample collaborative validation strategy is employed to cross-verify measured samples across different frequency bands, establishing the model’s high precision and engineering reliability. Full article

With the rapid advancement of the IoT society and growing awareness of environmental issues, thermoelectric conversion technology—which directly converts waste heat into electricity—is gaining attention as a self-powered, autonomous power source capable of driving countless devices. While currently mainstream metal-based inorganic thermoelectric materials demonstrate high performance, their high rigidity and brittleness, as well as their frequent inclusion of toxic heavy metals, have limited their application in biological systems and on curved surfaces. As a next-generation alternative, single-walled carbon nanotubes (SWCNTs)—which possess excellent flexibility, electrical conductivity, and mechanical strength while being low in toxicity—are garnering significant attention. However, n-type SWCNT materials, which are essential for thermoelectric module fabrication, have faced two major barriers to practical application: low atmospheric stability (they easily revert to p-type upon exposure to atmospheric oxygen and moisture) and thermoelectric performance that falls short of inorganic materials. This review comprehensively outlines the latest composite approaches designed to overcome these critical challenges and achieve both extreme atmospheric stability and high thermoelectric performance in n-type SWCNT materials, along with the flexibility required to withstand severe deformation. Three main strategies are discussed. The first is the organic/polymer approach, which involves doping with organic small molecules that control the LUMO level or bicyclic organic superbases with strong electron-donating properties, as well as polymer coating, to achieve long-term stable n-type characteristics and high power output even in air or under severe high-temperature conditions. The second is the inorganic hybrid strategy, which involves nanoscale compositing with inorganic materials such as Bi₂Te₃ and Cu₂O; this reduces thermal conductivity through phonon scattering via interface control, while the inorganic layer physically blocks oxygen to ensure long-term atmospheric stability. The third approach involves ultra-long-term stabilization techniques, such as bulk encapsulation using cationic or gemini surfactants, and environmentally friendly aqueous processes utilizing natural amino acids. Furthermore, we discuss the latest developments in imparting practical-level toughness (flexibility) capable of withstanding thousands of bending cycles and high tensile stress through the introduction of dynamic covalent network polymers and elastomers. The conformal flexible thermoelectric power generation modules created through the integration of composite optimization, low-environmental-impact processes, and doping techniques will serve as a crucial foundational technology for realizing a sustainable next-generation electronics society, including future wearable devices, artificial skin, and smart sensor networks. Full article

Predicting pain in physical rehabilitation is challenging due to subjectivity, patient variability, and self-report bias, especially in telerehabilitation. This study aims to determine whether machine-learning models based on heart rate (HR), heart rate variability (HRV), and peripheral oxygen saturation (SpO₂) can reliably detect clinically meaningful pain during real rehabilitation sessions, including home-based settings where self-report is least reliable; we hypothesized that these low-cost, non-invasive markers carry sufficient information to flag low-to-moderate pain episodes without relying on self-report. We combined these markers with machine-learning models. These markers were selected for their association with autonomic pain responses and ease of measurement with only two low-cost, non-invasive sensors (a wearable band providing HR and HRV, and a fingertip oximeter providing SpO₂) suitable for clinical and home-based rehabilitation. We evaluated linear regression (LR), random forest (RF), and artificial neural networks (ANNs) using data from 25 participants (aged 20–50) undergoing lower-limb rehabilitation. Signals acquired at 1 Hz were processed via temporal filtering, quality screening, and three missing-value strategies (interpolation, zero imputation, deletion) before normalization and training. LR showed limited predictive power. RF achieved 97.77% accuracy in detecting low-pain episodes, and balanced per-class performance under deletion (76.64%). ANN models contributed a more balanced three-class profile on interpolated data but remained sensitive to class imbalance. Given high-pain scarcity in supervised therapy and underreporting at home, reliable detection of low-to-moderate pain enables timely therapy adjustments. Unlike prior studies using experimentally induced pain, this work captured naturally occurring pain during real rehabilitation, making findings applicable to clinical and telerehabilitation contexts. Physiology-based models with low-cost sensors show promise for personalized rehabilitation, improving adherence and enabling proactive adjustments without added complexity. Full article

Pyroelectric nanogenerators (PyNGs) have attracted extensive attention for converting thermal energy into electricity, yet their low output power remains a critical bottleneck hindering practical use. This review summarizes various pyroelectric materials and device structures, elucidates the working principle, and discusses their output performances and application scenarios. The correlation between device output and key factors, including intrinsic material properties, electrode dimensions, and external thermal excitation, is systematically examined. Hybrid nanogenerators (HNGs) that couple pyroelectric with piezoelectric, triboelectric, and photovoltaic effects are also reviewed. In addition, the evaluation criteria for pyroelectric energy conversion efficiency are examined, highlighting the need for more systematic studies in this aspect. Finally, key challenges and corresponding strategies are discussed to facilitate the practical deployment of PyNGs in areas such as wearable electronics and self-powered sensors. Full article