Search Results (3,413)

Wearable pressure sensors have emerged as vital tools in personalized monitoring, promising transformative advances in patient care and diagnostics. Nevertheless, conventional devices frequently suffer from limited sensitivity, inadequate flexibility, and concerns regarding biocompatibility. Herein, we introduce silk fibroin, a naturally occurring protein extracted from silkworm cocoons, as a promising material platform for next-generation wearable sensors. Owing to its remarkable biocompatibility, mechanical robustness, and structural tunability, silk fibroin serves as an ideal substrate for constructing capacitive pressure sensors tailored to medical applications. We engineered silk-derived capacitive architecture and evaluated its performance in real-time human motion and physiological signal detection. The resulting sensor exhibits a high sensitivity of 18.68 kPa⁻¹ over a broad operational range of 0 to 2.4 kPa, enabling accurate tracking of subtle pressures associated with pulse, respiration, and joint articulation. Under extreme loading conditions, our silk fibroin sensor demonstrated superior stability and accuracy compared to a commercial resistive counterpart (FlexiForce™ A401). These findings establish silk fibroin as a versatile, practical candidate for wearable pressure sensing and pave the way for advanced biocompatible devices in healthcare monitoring. Full article

Electroless nickel deposition (ELD) is an autocatalytic technique extensively used to impart conductive, protective, and mechanical functionalities to inherently non-conductive synthetic substrates. This review systematically explores the fundamental mechanisms of electroless nickel deposition, emphasising recent advancements in surface activation methods, solvent systems, and microstructural control. Critical analysis reveals that bio-inspired activation methods, such as polydopamine (PDA) and tannic acid (TA), significantly enhance coating adhesion and durability compared to traditional chemical etching and plasma treatments. Additionally, solvent engineering, particularly using polar aprotic solvents like dimethyl sulfoxide (DMSO) and ethanol-based systems, emerges as a key strategy for achieving uniform, dense, and flexible coatings, overcoming limitations associated with traditional aqueous baths. The review also highlights that microstructural tailoring, specifically the development of amorphous-nanocrystalline hybrid nickel coatings, effectively balances mechanical robustness (hardness exceeding 800 HV), flexibility, and corrosion resistance, making these coatings particularly suitable for wearable electronic textiles and smart materials. Furthermore, commercial examples demonstrate the real-world applicability and market readiness of nickel-coated synthetic fibres. Despite significant progress, persistent challenges remain, including reliable long-term adhesion, internal stress management, and environmental sustainability. Future research should prioritise environmentally benign plating baths, standardised surface activation protocols, and scalable deposition processes to fully realise the industrial potential of electroless nickel coatings. Full article

The COVID-19 pandemic catalyzed the rapid adoption of remote patient monitoring (RPM) technologies such as telemedicine and wearable devices (WDs), significantly transforming healthcare delivery. Telemedicine made virtual consultations possible, reducing in-person visits and infection risks, particularly for the management of chronic diseases. Wearable devices enabled the real-time continuous monitoring of health that assisted in condition prediction and management, such as for COVID-19. This narrative review addresses these transformations by uniquely synthesizing findings from 13 diverse studies (sourced from PubMed and Google Scholar, 2020–2024) to analyze the parallel evolution of telemedicine and WDs as interconnected RPM components. It highlights the pandemic’s dual impact, as follows: accelerating RPM innovation and adoption while simultaneously unmasking systemic challenges such as inequities in access and a need for robust integration approaches; while telemedicine usage soared during the pandemic, consumption post-pandemic, as indicated by the reviewed studies, suggests continued barriers to adoption among older adults. Likewise, wearable devices demonstrated significant potential in early disease detection and long-term health management, with promising applications extending beyond COVID-19, including long COVID conditions. Addressing the identified challenges is crucial for healthcare providers and systems to fully embrace these technologies and this would improve efficiency and patient outcomes. Full article

MXenes, a class of two-dimensional transition metal carbides and nitrides, emerged as a promising material for next-generation energy storage and corresponding applications due to their unique combination of high electrical conductivity, tunable surface chemistry, and lamellar structure. This review highlights recent advances in MXene-based composites, focusing on their integration into electrode architectures for the development of supercapacitors, batteries, and multifunctional devices, including triboelectric nanogenerators. It serves as a comprehensive overview of the multifunctional capabilities of MXene-based composites and their role in advancing efficient, flexible, and sustainable energy and sensing technologies, outlining how MXene-based systems are poised to redefine multifunctional energy platforms. Electrochemical performance optimization strategies are discussed by considering surface functionalization, interlayer engineering, scalable synthesis techniques, and integration with advanced electrolytes, with particular attention paid to the development of hybrid supercapacitors, triboelectric nanogenerators (TENGs), and wearable sensors. These applications are favored due to improved charge storage capability, mechanical properties, and the multifunctionality of MXenes. Despite these aspects, challenges related to long-term stability, sustainable large-scale production, and environmental degradation must still be addressed. Emerging approaches such as three-dimensional self-assembly and artificial intelligence-assisted design are identified as key challenges for overcoming these issues. Full article

Molecular sensor systems, e.g., implantables and wearables, provide extensive health-related monitoring. Glucose sensor systems have historically prevailed in wearable bioanalysis applications due to their continuous and reliable glucose monitoring, a feat not yet accomplished for other biomarkers. However, the advancement of reagentless detection methodologies may facilitate the creation of molecular sensor systems for multiple analytes. Improving the sensitivity and selectivity of molecular sensor systems is also crucial for biomarker detection under intricate physiological circumstances. The term multidomain molecular sensor systems is utilized to refer, in general, to both biological and chemical sensor systems. This review examines methodologies for enhancing signal amplification, improving selectivity, and facilitating reagentless detection in multidomain molecular sensor devices. The review also analyzes the fundamental components of multidomain molecular sensor systems, including substrate materials, bodily fluids, power, and decision-making units. The review article further investigates how extensive data gathered from multidomain molecular sensor systems, in conjunction with current data processing algorithms, facilitate biomarker detection for precision medicine. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Conductive hydrogels demonstrate substantial potential for flexible wearable sensors in motion monitoring, owing to their unique physicochemical properties; however, current implementations still confront persistent challenges in long-term stability, sensitivity, response speed, and detection limits under complex dynamic conditions, which material innovations are urgently required to resolve. Consequently, this paper comprehensively reviews the recent advancements in conductive hydrogel-based flexible wearable sensors for sports applications. The paper examines the conductivity, self-adhesion, self-repair, and biocompatibility of conductive hydrogels, along with detailed analyses of their working principles in resistance, capacitance, piezoelectric, and battery-based sensing mechanisms. Additionally, the paper summarizes innovative strategies to enhance sensor performance through polymer blending, polyelectrolyte doping, inorganic salt doping, and nanomaterial integration. Furthermore, the paper highlights the latest applications of conductive hydrogel flexible wearable sensors in human motion monitoring, electrophysiological signal detection, and electrochemical biosignal monitoring. Finally, the paper provides an in-depth discussion of the advantages and limitations of existing technologies, offering valuable insights and new perspectives for future research directions. Full article

Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers. Full article

Nanotechnology is revolutionizing medicine by enabling highly precise diagnostics, targeted therapies, and personalized healthcare solutions. This review explores the multifaceted applications of nanotechnology across medical fields such as oncology and infectious disease control. Engineered nanoparticles (NPs), such as liposomes, polymeric carriers, and carbon-based nanomaterials, enhance drug solubility, protect therapeutic agents from degradation, and enable site-specific delivery, thereby reducing toxicity to healthy tissues. In diagnostics, nanosensors and contrast agents provide ultra-sensitive detection of biomarkers, supporting early diagnosis and real-time monitoring. Nanotechnology also contributes to regenerative medicine, antimicrobial therapies, wearable devices, and theranostics, which integrate treatment and diagnosis into unified systems. Advanced innovations such as nanobots and smart nanosystems further extend these capabilities, enabling responsive drug delivery and minimally invasive interventions. Despite its immense potential, nanomedicine faces challenges, including biocompatibility, environmental safety, manufacturing scalability, and regulatory oversight. Addressing these issues is essential for clinical translation and public acceptance. In summary, nanotechnology offers transformative tools that are reshaping medical diagnostics, therapeutics, and disease prevention. Through continued research and interdisciplinary collaboration, it holds the potential to significantly enhance treatment outcomes, reduce healthcare costs, and usher in a new era of precise and personalized medicine. Full article

The incorporation of digital health into the medical curricula is becoming more important to better prepare doctors in the future. Digital health comprises a wide range of tools such as electronic health records, health information technology, telemedicine, telehealth, mobile health applications, wearable devices, artificial intelligence, and virtual reality. The present study aimed to explore the medical and biomedical students’ perspectives on the integration of digital health in medical curricula. A cross-sectional study was conducted on the medical and biomedical undergraduate students at the College of Medicine and Health Sciences at Sultan Qaboos University. Data was collected using a self-administered questionnaire. The response rate was 37%. The majority of respondents were in the MD (Doctor of Medicine) program (84.4%), while 29 students (15.6%) were from the BMS (Biomedical Sciences) program. A total of 55.38% agreed that they were familiar with the term ‘e-Health’. Additionally, 143 individuals (76.88%) reported being aware of the definition of e-Health. Specifically, 69 individuals (37.10%) utilize e-Health technologies every other week, 20 individuals (10.75%) reported using them daily, while 44 individuals (23.66%) indicated that they never used such technologies. Despite having several benefits, challenges exist in integrating digital health into the medical curriculum. There is a need to overcome the lack of infrastructure, existing educational materials, and digital health topics. In conclusion, embedding digital health into medical curricula is certainly beneficial for creating a digitally competent healthcare workforce that could help in better data storage, help in diagnosis, aid in patient consultation from a distance, and advise on medications, thereby leading to improved patient care which is a key public health priority. Full article

Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article

This article presents a comprehensive modeling and simulation framework for a capacitive MEMS accelerometer integrated with a sigma-delta analog-to-digital converter (ADC), with a focus on applications in wearable health and motion monitoring devices. The accelerometer used in the system is connected to a smartphone equipped with dedicated software and will be used to assess the risk of falling, which is crucial for patients with balance disorders. The authors designed the accelerometer with special attention paid to the specification required in a system, where the acceleration is ±2 g and the frequency is 100 Hz. They investigated the sensor’s behavior in the DC, AC, and time domains, capturing both the mechanical response of the proof mass and the resulting changes in output capacitance due to external acceleration. A key component of the simulation is the implementation of a second-order sigma-delta modulator designed to digitize the small capacitance variations generated by the sensor. The Simulink model includes the complete signal path from analog input to quantization, filtering, decimation, and digital-to-analog reconstruction. By combining MEMS+ modeling with MATLAB-based system-level simulations, the workflow offers a fast and flexible alternative to traditional finite element methods and facilitates early-stage design optimization for MEMS sensor systems intended for real-world deployment. Full article

Biomarkers play a pivotal role in disease diagnosis, therapeutic efficacy evaluation, prognostic assessment, and drug screening. However, the trace concentrations of these markers in complex physiological environments pose significant challenges to efficient detection. It is necessary to avoid interference from non-specific signals, which may lead to misjudgment of other substances as biomarkers and affect the accuracy of detection results. With the rapid advancements in electrochemical technologies and artificial intelligence (AI) algorithms, intelligent electrochemical biosensors have emerged as a promising approach for biomedical detection, offering speed, specificity, high sensitivity, and accuracy. This review focuses on elaborating the latest applications of AI-empowered electrochemical biosensors in the biomedical field, including disease diagnosis, treatment monitoring, drug development, and wearable devices. AI algorithms can further improve the accuracy, sensitivity, and repeatability of electrochemical sensors through the screening and performance prediction of sensor materials, as well as the feature extraction and noise reduction suppression of sensing signals. Even in complex physiological microenvironments, they can effectively address common issues such as electrode fouling, poor signal-to-noise ratio, chemical interference, and matrix effects. This work may provide novel insights for the development of next-generation intelligent biosensors for precision medicine. Full article

Nafion has long been recognized as the gold standard for proton exchange membranes, due to its exceptional ion exchange capacity and its advanced performance in chemically aggressive environments. In recent years, a growing body of evidence has demonstrated that Nafion is equally well-suited in complex biological conditions owing to its structural robustness, responsive functionality and intrinsic biocompatibility. These characteristics have enabled its transition into the biomedical and healthcare sectors, where it is currently being explored for a diverse and expanding range of applications. To that end, Nafion has been systematically investigated as a key component in bioelectronic systems for energy harvest, sensors, wearable electronics, tissue engineering, lab-on-a-chip platforms, implants, controlled drug delivery systems and antimicrobial surface coatings. This review examines the distinctive structural and electrochemical characteristics that underpin Nafion’s performance in these biomedical contexts, provides an overview of recent advancements, emphasizes critical performance metrics and highlights the material’s growing potential to shape the future of biomedical technology. Full article

In this study, a facile and mask-free femtosecond laser direct writing (FLDW) approach is proposed to fabricate porous graphene (FLIG) patterns directly on polyimide (PI) substrates. By systematically adjusting the laser scanning spacing (10–25 μm), denser and more continuous microstructures are obtained, resulting in significantly enhanced thermal sensitivity. The optimized sensor demonstrated a temperature coefficient of 0.698% °C⁻¹ within the range of 40–120 °C, with response and recovery times of 10.3 s and 20.9 s, respectively. Furthermore, it exhibits remarkable signal stability across multiple thermal cycles, a testament to its reliability in extreme conditions. Moreover, the sensor was successfully integrated into a 3D-printed robotic platform, achieving both contact and non-contact temperature detection. These results underscore the sensor’s practical adaptability for real-time thermal sensing. This work presents a viable and scalable methodology for fabricating high-performance FLIG-based flexible temperature sensors, with extensive application prospects in wearable electronics, electronic skin, and intelligent human–machine interfaces. Full article