The Smart Wearable Technology (SWT) aims to advance the knowledge and understanding of wearable devices by publishing high-quality research that addresses the latest developments, trends, and challenges in the field. The journal seeks to explore the intersection of cutting-edge technologies, including related materials, sensors, artificial intelligence, machine learning, and wireless communication, with wearable systems designed for health monitoring, fitness, personal well-being, and beyond. It strives to provide a platform for innovative solutions that enhance user experience, optimize data collection and analysis, and improve real-time decision-making. The journal also aims to bridge the gap between engineering, design, and healthcare applications, facilitating interdisciplinary research and collaboration. By fostering the exchange of ideas and knowledge, the Smart Wearable Technology (SWT) seeks to contribute to the development of smarter, more efficient, and user-centric wearable technologies that can transform everyday life and improve the quality of life globally. The journal is a Gold Open Access journal, online readers don't have to pay any fee. The journal is currently free to the authors, and all Article Processing Charges (APCs) are waived until 31 December 2026.
A Proof-of-Concept Deterministic Phase-Memory Operator for Respiratory Instability Detection Using Chest-Mounted Smartphone IMU Signals
Wearable respiratory monitoring often relies on heuristic pipelines or opaque machine learning models, which can limit interpretability and auditability in safety-sensitive or clinical adjacent contexts. Here, we present a proof-of-concept deterministic phase-memory operator for respiratory instability detection using chest-mounted smartphone inertial measurement unit (IMU) signals. The proposed instability metric △Φ(t) quantifies deviations of instantaneous phase velocity from short-term phase memory, enabling transparent threshold-based decision logic without training dependence. A controlled validation protocol based on N = 5 publicly available Beth Israel Deaconess Medical Center (BIDMC) respiratory recordings with semi-synthetic perturbations was used to examine representative deviation regimes, including frequency drift, intermittent pauses, and burst irregularities. Performance was compared against low-overhead baseline methods based on root mean square (RMS)-envelope and fast Fourier transform (FFT)-peak tracking. Within this limited proof-of-concept setting, the framework showed reproducible responses to structured perturbations. The reference Python pipeline processed 60 s respiratory traces in 2.37 ms on a standard x86-64 system, while the intended mobile implementation remains compatible with streaming-capable, low-overhead on-device processing through a causal approximation of the analytic-signal stage. The present study should be interpreted as a methodological investigation of interpretable chest-based IMU respiratory instability sensing rather than as a clinical validation study. Further work is required to evaluate physiological specificity, robustness across heterogeneous cohorts, adaptive baseline strategies, and performance under broader real-world motion conditions. Received: 27 February 2026 | Revised: 17 April 2026 | Accepted: 28 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The reference implementation, validation scripts, baseline comparison modules, and reproducibility infrastructure described in this manuscript are publicly available at: https://github.com/dfeen87/Smartphone-Based-Chest-Monitoring The repository includes the deterministic C++ core implementation, the Python validation pipeline, PhysioNet BIDMC integration scripts, controlled perturbation protocols, and computational profiling documentation. Author Contribution Statement Marcel Krüger: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Supervision, Project administration. Don Michael Feeney Jr.: Methodology, Software, Validation, Resources, Data curation, Writing – review & editing, Visualization.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
An Explainable Prediction Model for Cerebral Small Vessel Disease Based on Motor Function and Feature Interactions
Cerebral small vessel disease (CSVD) is a major condition with systemic and whole-brain effects, significantly impacting the daily lives of middle-aged and elderly people. Currently, predictions for CSVD are limited to traditional statistical methods and lack consideration of interactions between risk factors. Based on this, this study proposes an interpretable risk prediction method for CSVD based on combined motor function and interaction features. First, the proposed multidimensional feature selection method is applied for feature selection. Next, the RS+GWO+Voting method is proposed for model construction. Finally, the model constructed using SHapley Additive exPlanations (SHAP) is applied for interpretation, enhancing the clinical applicability of the model. The results show that the proposed model achieved a training set Area Under the Curve (AUC) of 0.829 and a validation set AUC of 0.824, demonstrating its strong predictive performance. The method provides theoretical support and technical backing for CSVD risk prediction under small-sample conditions and holds promise for integration with wearable devices to enable early warning and health management of CSVD. Received: 8 January 2026 | Revised: 31 March 2026 | Accepted: 15 April 2026 Conflicts of Interest Congsi Wang is an editorial board member for Smart Wearable Technology and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Benben Wang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Visualization. Yuefei Yan: Methodology, Writing – review & editing, Project administration. Huizhen Lu: Validation, Investigation, Data curation. Baoqing Han: Writing – review & editing, Project administration. Yuangen Deng: Software. Chengliang Zhang: Data curation. Jianwei Xu: Data curation. Jingtan Chen: Supervision. Chuanliu Wang: Resources, Supervision. Congsi Wang: Conceptualization, Formal analysis, Resources, Funding acquisition.
A Method for Determining Obstacle Contours Using Wearable Video Cameras for Orienting Blind and Visually Impaired People in Urban Environment
The essence of the method is based on the division of the video camera matrix into submatrices, in which the Euclidean distance is determined between adjacent rows and columns. If there is a gradient of pixel intensities exceeding a certain threshold, this leads to a jump in the Euclidean distance, which indicates the presence of an element of the contour of the entire object in a separate submatrix. The quality of such a contour determination depends on the sizes of the submatrices and the selected threshold. To expand the capabilities of the method in estimating the complex shape of objects, a combined submatrix method is proposed, in which contours are determined at different submatrix sizes and threshold values, and then superimposed on each other. The sizes of two different submatrices are determined based on previous research. The possibilities of optimizing such sizes using a genetic algorithm are considered. The method provides the highest similarity of contours to the original image of steps compared to other methods, and the accuracy of determining the contours of a rectangle, triangle, and circle is comparable to the accuracy of such methods. Received: 14 December 2025 | Revised: 5 March 2026 | Accepted: 17 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Oleksandr Poliarus: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Yevhen Poliakov: Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Visualization.
navigation of blind and visually impaired people
Recent Progress in High-Performance Conductive Hydrogels for Wearable Sensors
Hydrogel materials are regarded as an ideal platform for constructing next-generation high-performance, bio-integrated wearable sensors due to their unique softness, high water content, excellent biocompatibility, and tunable conductivity. This review aims to systematically summarize recent advances in the field, providing a comprehensive exploration of the entire technical pathway—from material design and performance regulation to device integration and application. We first analyze optimization strategies for key properties of hydrogels—such as conductivity, self-healing, adhesion, and environmental stability—through approaches like nanocomposite integration and dynamic cross-linking. And we further discuss advanced fabrication techniques, including the construction of hybrid physical–chemical cross-linking networks and 3D printing. In addition, we categorize five types of hydrogel sensors—resistive, capacitive, piezoelectric, triboelectric, and electrochemical—based on their sensing mechanisms, detailing their working principles and performance characteristics. Finally, this review outlines their specific applications in cutting-edge fields such as physiological monitoring, human–machine interaction, and implantable medical devices. Research findings demonstrate that hydrogel sensors can balance flexibility and sensing performance under moderate strain levels (100–200%) and controlled hydration conditions, whereas extreme deformation or prolonged exposure to dry environments often leads to signal drift or mechanical degradation enabling strain and pressure sensing with reported gauge factors typically ranging from ~2 to 100, detectable pressure ranges from a few Pa to hundreds of kPa, and operational lifetimes from hours to several weeks depending on encapsulation and environment. Received: 9 December 2025 | Revised: 27 January 2026 | Accepted: 12 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Jianrui Zhao: Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Qingwang Liu: Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing. Xinzu Yue: Conceptualization, Methodology, Software, Resources. Weibao Wang: Resources. Weiwei Zhang: Resources. Xinning Liu: Resources. Guoshen Yang: Conceptualization, Methodology, Validation, Formal analysis, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.
Biosensors for IgG Identification in the Absence of Infection to Isolate Autoimmune Activity
People living with chronic autoimmune conditions could run the risk of misdiagnosis. This study involved a systematic review of literature examining how elevated autoimmune-prevalent immunoglobulin G (IgG) coincides with average or low procalcitonin (PCT) levels and normal lymphocyte and neutrophil levels, which can indicate autoimmunity versus bacterial infection or viral infection, respectively. The study results showed a trend in cases where high IgG levels, despite normal PCT and lymphocytes/neutrophils, were concurrent in people with autoimmune disease, alongside a gap in research surrounding how wearable devices could be used to measure IgG against the presence or absence of indicators of acute infection. These findings helped inform the need for a rudimentary algorithm for a wearable biosensor to identify excess IgG in the presence of baseline or low levels of PCT and normal levels of lymphocytes and neutrophils in the blood serum. This device would theoretically aid in differentiating autoimmune conditions from active infection. Received: 7 December 2025 | Revised: 23 January 2026 | Accepted: 6 February 2026 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Sarah Katz: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0
Next-Generation Wearable and Stretchable Strain Sensors for Healthcare: Materials, Mechanisms, Architectures, and Applications
Wearable and stretchable strain sensors are central to smart wearable technology, enabling continuous, real-time, and noninvasive tracking of human motion and physiological signals for healthcare, sports, and human–machine interaction. Rapid progress in soft materials, structural design, and flexible electronics has led to devices with improved sensitivity, large strain range, durability, and skin conformability, yet practical translation into robust products remains limited. This review provides a comprehensive overview of recent advances in wearable and stretchable strain sensors with emphasis on healthcare applications. We first summarize key sensing mechanisms—including piezoresistive, capacitive, piezoelectric, triboelectric, and optical modes—and compare their operating principles and performance trade-offs. We then discuss material platforms such as elastomeric substrates, conductive polymers, nanomaterials, and hybrid composites, followed by critical design parameters (gauge factor, stretchability, hysteresis, response time, durability) that govern device performance. Structural and device engineering strategies, including microstructured surfaces, percolated networks, serpentine interconnects, porous scaffolds, and liquid-metal architectures, are highlighted as routes to achieving high performance under complex deformations. Representative applications in vital-sign monitoring, motion and gait analysis, rehabilitation, wound care, and smart textiles are reviewed, along with broader uses in soft robotics and structural health monitoring. Finally, we identify key challenges—long-term stability on skin, biocompatibility, power autonomy, secure data handling, standardized benchmarking, and scalable manufacturing—and outline future directions in self-powered systems, multimodal sensing, and AI-assisted analytics. This review aims to provide a consolidated framework to guide the design of next-generation, clinically relevant wearable strain-sensing platforms. Received: 2 December 2025 | Revised: 2 February 2026 | Accepted: 24 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Boluwatife Oluwasegun: Formal analysis, Investigation, Writing – original draft, Visualization. Oluwaseun Oni-Adimabua: Conceptualization, Writing – original draft. Kyrian Odo: Conceptualization, Methodology. Opeyemi Akanbi: Conceptualization, Writing – review & editing, Supervision, Project administration. Aimanose Eigbedion: Methodology, Formal analysis. Joseph Igbama: Validation, Investigation. Joseph Alieme: Validation, Investigation. Folakemi Ijagbemi: Validation, Resources. Ayotunde Igbekele: Resources, Visualization. Michael Adelere: Supervision, Project administration. Hakeem Oyeshola: Formal analysis, Writing – review & editing.
Structural, Optical, and Nonlinear Optical Properties of ZnO Nanostructures Grown on Porous Silicon
Zinc oxide (ZnO) nanostructures were effectively produced on a porous silicon (PS) substrate using thermal evaporation, thereafter undergoing oxidation at 600 °C. The structural, morphological, elemental, optical, and nonlinear optical (NLO) characteristics of the synthesized films were methodically examined. X-ray diffraction research indicated that the ZnO/PS films possess a polycrystalline hexagonal wurtzite structure with a preferential orientation along the (101) plane, and the average crystallite size was calculated to be around 3.8 nm, affirming their nanocrystalline characteristics. Scanning electron microscopy pictures revealed the presence of agglomerated nanostructures and nanorods over the PS surface, signifying elevated surface roughness and extensive surface area. Energy-dispersive X-ray investigation verified the predominance of Zn and O, with modest contributions from Si originating from the substrate. The optical absorption spectra exhibited a significant blue shift attributable to quantum confinement phenomena, with the optical band gap measured at approximately 3.6 eV. The calculated Urbach energy (Eu = 145 meV) indicates a moderate level of structural disorder and the presence of tail states near the band edge, aligning with the nanocrystalline characteristics of ZnO films. The Z-scan approach utilized for NLO measurements demonstrated saturable absorption behavior, suggesting possible applications in photonic and optical limiting devices. Received: 2 December 2025 | Revised: 12 January 2026 | Accepted: 11 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Marwa. K. Abood: Conceptualization, Methodology, Validation, Resources, Project administration. Wafaa Khalid Khalef: Conceptualization, Methodology, Validation, Supervision. Amenah Ali Salman: Software, Formal analysis. Nibras Salah Hameed: Investigation, Data curation, Writing – review & editing. Mohammed Khalid Khalaf: Writing – original draft, Visualization.
Dual-Model Approach for Knee Osteoarthritis Classification Using Custom CNN and Fine-Tuned VGG16 with Histogram Equalization
Knee osteoarthritis (KOA) is a progressive joint disorder that often remains asymptomatic in early stages, making timely diagnosis essential. This study presents a deep learning-based framework for KOA classification using preprocessed knee X-ray images. Image enhancement techniques such as histogram equalization and adaptive histogram equalization were applied to improve local contrast. Two independent architectures were developed: a custom convolutional neural network (CNN) designed from scratch and a fine-tuned VGG16 model based on transfer learning. The custom CNN incorporated L1/L2 regularization, batch normalization, and dropout layers to improve generalization. However, initial results revealed reduced performance due to severe class imbalance in the dataset. To address this, data augmentation was employed as an oversampling strategy, using rotation, flipping, translation, shearing, and zoom transformations to balance all KOA grades. To further improve classification performance, an ensemble strategy was introduced by combining the softmax probability outputs of the custom CNN and VGG16 using weighted averaging. This fusion allows the complementary strengths of both models to jointly influence the final Kellgren–Lawrence grade prediction, improving robustness and early-stage KOA detection. Although augmentation increased data diversity, the custom CNN achieved limited accuracy, prompting the adoption of the more robust VGG16 architecture. Comparative evaluation showed that VGG16 and the proposed ensemble achieved superior classification performance, demonstrating the effectiveness of transfer learning for KOA detection. The proposed framework offers a reliable approach for assisting early KOA diagnosis and supporting clinical decision-making. Received: 27 October 2025 | Revised: 29 January 2026 | Accepted: 13 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement This study utilized data from the OAI database, a public resource available at https://nda.nih.gov/oai/. The OAI is a multicenter, longitudinal study of KOA, funded by the National Institutes of Health. Author Contribution Statement G. Prema: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization, Supervision, Project administration. P. Rithanya: Software, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. A. Shiva Priya: Software, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Examine the Physical Properties of Rubber-Modified Unsaturated Polyester for Potential Wearable Technology Applications
This study examines the influence of incorporating natural rubber (NR) into unsaturated polyester resin (UP) on the thermal conductivity, friction coefficient, and wear rate. Three blend ratios (90/10, 60/40, and 50/50 UP/NR) were prepared to explore how varying NR content affects these properties. The results demonstrate that increasing the NR content leads to a significant rise in both the coefficient of friction and wear rate, particularly evident at the 50/50 composition. Specifically, the coefficient of friction increased by approximately 45–50%, while the wear rate nearly doubled compared with the 90/10 blend. Conversely, the thermal conductivity decreased progressively with higher NR ratios—by roughly 30% from the 90/10 to the 50/50 composition—due to the insulating nature of rubber. The 90/10 blend thus showed the highest heat transfer capability. Overall, the study confirms that blending UP with NR allows controlled tuning of material properties according to specific engineering needs. High NR content is advantageous for applications requiring impact absorption or vibration damping, whereas low NR content favors heat dissipation and wear resistance. In this context, UP reinforced with NR provides a balance of structural integrity and elasticity that can be valuable in wearable systems, including flexible housings, protective layers, vibration-damping elements, and thermal-insulating structures. Received: 25 November 2025 | Revised: 15 January 2026 | Accepted: 2 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Rafah Alwan Nassif: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Raghad Hamid Hilal: Software, Validation, Investigation, Resources, Data curation, Supervision, Project administration.
Verification of the Self-Load Assessment Capability of Inertial Measurement Units via Lumbar Musculoskeletal Simulation
This study aims to validate the capability of inertial measurement units (IMUs) combined with surface electromyography (sEMG) sensors for assessing lumbar self-load. Lumbar biomechanical simulation was performed using OpenSim software, and data collected by IMUs and sEMG sensors were integrated into the simulation to evaluate the system’s performance in assessing lumbar load under static postures in real environments. In the experiment, 15 IMUs were fixed at human anatomical landmarks, and motion data and sEMG signals were synchronously collected during isometric tests of lumbar muscles. IMU data were converted into an OpenSim-compatible format through a self-developed processing workflow to realize musculoskeletal dynamic and kinematic analysis. The experiment included four types of static posture tests, and six sets of isometric test motion data were collected. The results showed that under different static postures, the L1–L5 lumbar joints remained stable, the distribution of joint driving torque showed a regular pattern, and the muscle driving forces were different and basically consistent with physiological reality; sEMG signal analysis verified the evaluation capability of the system, and the muscle fatigue characteristics were consistent with expectations. This study confirms the methodological effectiveness of the combination of IMU and sEMG sensors for static lumbar load assessment in real environments, providing a potential methodological reference for the clinical evaluation of nonspecific low back pain. Received: 21 November 2025 | Revised: 7 January 2026 | Accepted: 26 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Zhong Wang: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Xiaohuan Yuan: Writing – review & editing, Supervision. Yuanliang Tang: Conceptualization, Resources, Writing – review & editing, Supervision. Shaohui Zhang: Conceptualization, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.
Smart Assistive Shoes for Blind People Based on IoT
Innovations that help people with special needs are among the most important services scientific research can provide to serve societies. From this standpoint, the idea behind this work is to help blind and visually impaired people live their lives normally. For example, it is difficult for blind people to walk independently; they must rely on others in most of their daily activities. Walking on the streets is one of the most difficult problems that blind people face, as they cannot notice every obstacle on the road using a cane. From this perspective, the proposed model in this work served this sector of society and made it more effective. The model presented in our work includes designing a smart shoe based on Internet of Things technologies, a Mobile App, sensors, Raspberry Pi microcontrollers, a camera, and an integrated alarm device. The smart shoe offers a long-term solution for the visually impaired. Moreover, it will allow them to reach their destination without stress and independently. The model was tested under different conditions, and the results were satisfactory, as the accuracy reached 80%. Received: 13 November 2025 | Revised: 5 January 2026 | Accepted: 13 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Muneera Altayeb: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Supervision, Project administration. Laith Al-mandeel: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization. Areen Arabiat: Validation, Resources, Data curation, Writing – review & editing, Visualization.
Clinical Efficacy of Neuromodulation Interventions and Rehabilitation Advancements in Ataxic Subtypes
Ataxia is a progressive neurological disorder that impairs motor and functional ability due to cerebellar dysfunction. Conventional therapies, which include pharmacological interventions, offer limited benefits, creating a need for mechanism-based and objectively measurable alternatives. Current therapeutic strategies increasingly focus on neuromodulation techniques, physical training, hybrid protocols, and the use of smart wearable technology in some of these therapies. This review compares the clinical efficacy of physical therapy, neuromodulation techniques—deep brain stimulation, repetitive transcranial magnetic stimulation, and transcranial direct current stimulation (tDCS)—and their hybrid therapy modalities, aiming to assess how stimulation-induced neuroplasticity interacts with motor training to optimize rehabilitation techniques across hereditary and acquired ataxias. Gait indices such as the Scale for the Assessment and Rating of Ataxia, International Cooperative Ataxia Rating Scale, Berg Balance Scale, Timed Up and Go, stimulation settings, and intensity of the therapy were considered for evaluating the impact of the intervention. All interventions demonstrated short-term gains in coordination, gait, and balance. However, hybrid protocols, which integrate different physical rehabilitation techniques or a neuromodulation technique paired with physical therapy, showed stronger and more durable recovery. Mechanistically, neuromodulation models induce neuroplasticity in cerebellar-cortical pathways, while physical therapy stabilizes neuroplastic adaptations. Robot-assisted, remote tDCS interventions and wearable sensor-supported monitoring have increased ease of access and participant compliance. Limitations across studies included small cohorts, variability in stimulation parameters, and short follow-up durations. Collectively, hybrid and technology-integrated rehabilitation is a promising framework for reinforcing motor function and independence in ataxic patients. Future multicenter trials incorporating wearable gait biomarkers, neuroimaging, and personalized strategies are required to validate long-term efficiency and enable precision in therapies for ataxic patients. Received: 10 November 2025 | Revised: 29 December 2025 | Accepted: 9 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Krishnakumar Sankar: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Samyuktha Shanmugam: Conceptualization, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Sanjana Parlikad Krishnan: Conceptualization, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Sushmitha Sree Saravanan: Conceptualization, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Modified Two-Point Scleral Fixation of a Four-Haptic Intraocular Lens with Cerclage Pupilloplasty
Aphakia in the absence of adequate capsular or zonular support remains one of the most demanding challenges in anterior segment surgery. The condition may arise secondary to trauma, hereditary connective tissue disorders such as Marfan syndrome, complicated cataract surgery, or long-term pseudophakia with in-the-bag intraocular lens (IOL) dislocation. Our study describes outcomes of a modified two-point Canabrava scleral fixation technique, using 6-0 polypropylene sutures, combined with cerclage pupilloplasty for anterior segment reconstruction in eyes with zonular and iris damage. In our study, eight eyes have been treated with modified two-point scleral fixation between 2021 and 2025. The treatment involves external docking of the suture, elimination of intraocular docking combined with cerclage pupilloplasty, where it is indicated. Mean preoperative best-corrected visual acuity is 0.05 ± 0.04, improved to 0.80 ± 0.14 at 1 month. Mean intraocular pressure decreased from 21.0 ± 8.7 mmHg to 14.3 ± 1.2 mmHg. All IOLs remained well-centered, pupils were round and reactive, and no intraoperative/postoperative complications occurred. The main limitation of this case series is the retrospective design and limited sample size of eight cases using the modified technique. A larger, prospective comparative study with long-term follow-up is needed to confirm these preliminary results and to assess potential late flange degradation or suture-related changes beyond 2–3 years. This modification simplifies scleral fixation by externalizing the docking step, offering a safe, minimally invasive method in complex eyes with absent capsular support. Received: 5 November 2025 | Revised: 24 December 2025 | Accepted: 7 January 2026 Conflicts of Interest Migena Beqiri and Klea Bojaxhi are employees of the Albanian Eye Centre. The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Albanian Eye Centre at www.unitir.edu.al. Author Contribution Statement Ali Tonuzi: Conceptualization, Validation, Writing – review & editing, Project administration. Orjeta Tonuzi: Conceptualization, Investigation. Migena Beqiri: Methodology, Formal analysis, Writing – original draft, Visualization. Klea Bojaxhi: Conceptualization, Investigation. Luan Qafmolla: Methodology, Software, Validation, Data curation, Writing – original draft, Writing – review & editing, Visualization.
From Wires to Wearables (3): ST-Segment Fidelity Across Modalities: Sydäntek’s Algorithmic Transition from Legacy Leads to Wearables
Accurate low-frequency response in digital electrocardiogram (ECG) systems is critical for preserving ST-segment morphology and ensuring diagnostic fidelity in ischemia detection. This study validates the low-frequency performance of SydäntekTM, a wearable 12-lead ECG platform, using impulse-based protocols aligned with IEC 60601-2-25 standards. Synthetic 3 mV square wave-forms with 100 ms isoelectric segments were injected to simulate baseline drift and assess high-pass filter behavior near the 0.67 Hz cutoff. Forward–reverse finite impulse response (FIR) convolution and filtfilt-based processing ensured zero-phase distortion and amplitude preservation. Hardware calibration employed WhaleTeq modules generating programmable impulses and sinusoidal drifts, confirming analog-to-digital fidelity across physiologic bradycardia thresholds. Real-world validation was conducted using 79 annotated records from the European ST-T Database from Physionet, encompassing both ST elevation and depression events. The SydäntekTM system consistently preserved ST-segment morphology across heart rate bins, maintaining a bias of less than 5 µV at J+40, J+60, and J+80 ms intervals. Among these, J-point anchoring at J+60 ms demonstrated the strongest correlation with reference annotations in the ST-T Database. Of note, J+60 aligns with published literature identifying it as a sensitive and specific marker for subendocardial ischemia. Artifact suppression in lead V2 was achieved using adaptive Wiener filtering, which effectively minimized motion-induced distortion while preserving low-amplitude ST features critical for diagnostic fidelity. Digitization fidelity was confirmed through direct waveform analysis, demonstrating consistent temporal precision and preservation of ST-segment morphology. The platform adheres to key performance criteria outlined in IEC 60601-2-25, -2-27, and -2-47, supporting its suitability for deployment in resting, monitoring, and ambulatory ECG contexts. These findings position SydäntekTM as a clinically capable solution for low-frequency ECG acquisition, with relevance to infarct detection, pericarditis, and myocardial disease screening. The methodology provides a reproducible framework for waveform validation, morphology-aware signal processing, and alignment with regulatory expectations in next-generation cardiac diagnostics. Received: 23 October 2025 | Revised: 2 December 2025 | Accepted: 22 December 2025 Conflicts of Interest Dr Sugandhi Gopal, the principal investigator served as both system designer and study director; this dual role is disclosed, governance measures minimized bias, and all underlying data are available for review to ensure transparency and reproducibility. The investigator’s primary professional role is as an interventional cardiologist, with research activities conducted in alignment with that clinical focus. Mr Mohith Subramanian, Vishwateja Reddy, and Ms Poulami Roy Engineers, are involved in this project and are affiliated with Carditek Medical Devices in roles specific to signal processing, algorithm development. Their contribution was technical in nature and did not influence the study’s clinical design or data interpretation. Shasha Jumbe also declares no conflict of interest. All contributors affirm adherence to ethical research practices, and confirm that their roles did not introduce bias or commercial influence into the study’s execution or reporting. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Sugandhi Gopal: Conceptualization, Methodology, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Prabhavathi Bhat: Validation. Shasha Jumbe: Validation. Mohith Subramanian: Methodology, Software, Formal analysis, Writing – review & editing. Vishwateja Reddy: Methodology, Software, Formal analysis, Investigation, Data curation. Poulami Roy: Methodology, Software, Formal analysis, Investigation, Data curation.
Comparative Analysis of Non-Local Means and Hybrid Median Bilateral Filtering for Mobile Image Denoising
Image denoising is an important step in many computer vision applications, including but not limited to intelligent transportation systems, mobile robotics, remote sensing, autonomous driving, and surveillance. In wearable devices such as smart glasses, wearable health monitoring devices, and other body-mounted devices, the image quality suffers due to non-uniform illumination, motion, occlusions, etc. The image denoising is required prior to image analysis for many computer vision tasks. Many computer vision algorithms perform well when the images are captured using high-resolution cameras under controlled conditions. However, the performance of these methods degrades when images are captured using low-resolution mobile phone cameras or when the images suffer from environmental noise. The images captured using mobile phones act as a proxy for many wearable devices to a reasonable extent because of the similarity in the size of sensors used and the imaging conditions. In this paper, the images captured by a mobile phone are subjected to Gaussian noise. Two different methods, namely, the non-local means denoising method and the hybrid median bilateral method, are used for denoising the images. The performance evaluation of the two methods is done using metrics, namely, peak signal-to-noise ratio and structural similarity index. It is observed that the non-local means denoising method performs better than the hybrid median bilateral denoising method. The method is useful in many image analysis tasks for images captured with smart wearable devices. Received: 30 October 2025 | Revised: 18 March 2026| Accepted: 2 April 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Ashwani Kumar Aggarwal: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Assessing Pulse Rate Variability from a Wrist-Worn PPG Device Against ECG-Derived Heart Rate Variability in Ambulatory Settings
This study systematically evaluates the consistency and applicability limits of photoplethysmography (PPG)-derived pulse rate variability (PRV) versus electrocardiogram (ECG)-derived heart rate variability (HRV) in real-world settings. It integrates three methodological dimensions: 24-hour multi-context monitoring, dual-level consistency analysis (inter- and intra-individual), and controlled motion intensity via a 27-level acceleration gradient. Data from 14 healthy participants were collected using synchronized wrist-worn PPG, portable ECG, and triaxial accelerometry. Standardized preprocessing and motion artifact suppression based on acceleration thresholds enabled the extraction of time-domain, frequency-domain, and nonlinear HRV and PRV metrics. Consistency was assessed using Pearson correlation and root mean square error, with false discovery rate-corrected significance testing. Results show strong PPG-ECG agreement during sleep (r > 0.9 for HR, MeanNN, Prc80NN) but marked degradation under high motion. Notably, Prc20NN demonstrated exceptional robustness across contexts, retaining significant correlation even during active phases. Stringent motion filtering substantially improved correlations. These findings delineate metric-specific validity boundaries for wearable PRV, distinguishing motion-induced errors from inherent physiological discrepancies, and offer evidence-based recommendations for deploying PRV in context-appropriate applications such as sleep monitoring, passive health tracking, and longitudinal stress assessment. Received: 15 October 2025 | Revised: 2 December 2025 | Accepted: 15 December 2025 Conflicts of Interest Dan Zhang is the Editorial Board Member for Smart Wearable Technology and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work. Data Availability Statement To protect participant privacy while supporting scientific transparency, anonymized raw physiological signals (ECG, PPG, and accelerometer) and derived HRV/PRV features from five representative participants have been deposited and are publicly accessible at https://cloud.tsinghua.edu.cn/d/9629ab21aaba4f03a70d/. Author Contribution Statement Jie Huang: Methodology, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization. Yuhong Wu: Methodology, Validation, Data curation, Writing – original draft, Supervision. Fang Li: Validation, Data curation. Dan Zhang: Conceptualization, Methodology, Writing – review & editing, Project administration, Funding acquisition. Shuping Tan: Conceptualization, Writing – review & editing, Project administration.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
Advancement and Future of Nanorobotics in Medicine: A Narrative Review
Nanorobots are nanoscale devices engineered to perform precise diagnostic or therapeutic tasks in the body. This narrative review synthesizes advances in nanorobot design (materials, propulsion, navigation, and communication) and highlights applications in drug delivery, imaging/diagnostics, surgery, and immune system modulation. We searched PubMed and Google Scholar using keywords “nanorobots,” “nanorobotics,” “drug delivery,” “diagnostics,” and “theranostics,” including peer-reviewed English original research and review articles and excluding non-peer-reviewed. In drug delivery, targeted nanocarriers have enabled precise chemotherapy delivery; a stimulus-responsive DNA-origami nanobot delivered ligands to cluster death receptors on breast cancer cells, yielding ~70% tumor reduction in a mouse model. Other designs use magnetic or chemical propulsion to traverse barriers (e.g., blood–brain barrier) and release drugs via triggers (pH, temperature, enzymes). In imaging/diagnostics, magnetically actuated nanorobots carrying radiopaque materials (barium sulfate/magnetite) have been navigated for micro-Computed Tomography tracking of gastrointestinal targets, and sensor-equipped nanobots can detect tumor biomarkers and relay signals for early disease detection. In microsurgery, remotely controlled microdrills and soft robots enable minimally invasive procedures such as plaque removal and targeted thrombectomy. Nanorobots can also modulate immunity: polymeric nanocarriers deliver immunosuppressants to inflamed tissue, and artificial antigen-presenting nanobots expand regulatory T cell populations in vivo. These advances demonstrate high-precision capabilities, but findings remain largely preclinical, and we have tempered language about “imminent” translation. Clinical readiness requires overcoming biocompatibility, powering, manufacturability, and safety hurdles; regulatory classification is unclear, and many may be high-risk (Class III) devices requiring full premarket approval. Ethical and societal issues (patient autonomy, data privacy, long-term persistence, equitable access) also demand attention, while bio-hybrid designs (cell-membrane coatings, living cell robots) seek to mitigate immune clearance. Conclusion: nanorobots hold transformative potential for personalized medicine, but cautious optimism is warranted. Received: 2 September 2025 | Revised: 8 December 2025 | Accepted: 31 December 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Sai Han Htun: Conceptualization, Methodology, Validation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Iqra Mumtaz: Conceptualization, Validation, Resources, Data curation, Writing – original draft. Umaira Abbasi: Methodology, Resources, Writing – original draft. Marriyum Rashid Khan: Conceptualization, Resources, Writing – original draft. Saptarshi Mukherjee: Resources, Writing – original draft, Visualization, Supervision. Arun Kumar Maloth: Conceptualization, Resources, Writing – original draft. Fadila shuaibu Bello: Resources, Writing – original draft.
AI-Enhanced Mobile and Collaborative Robotics for Autonomous Inspection and Predictive Maintenance in Smart Manufacturing
The implementation of new Industry 4.0 technologies in robotics (mobile and collaborative robotics) with artificial intelligence (AI) is reshaping maintenance planning in advanced manufacturing. This paper analyzes the application of robotic systems combining collaborative robots (cobots) and autonomous mobile robots (AMRs) as support for predictive maintenance. Predictive maintenance is based on continuous real-time visual monitoring with the goal of managing faults. A mixed-methods approach was used, combining quantitative metrics such as downtime reduction, mean time to repair, and return on investment with qualitative staff assessments. The results of implementing robotic systems to support predictive maintenance indicate a significant reduction in production downtime, increased operational efficiency, and faster resolution of faults in the manufacturing process. In addition to technical efficiency, the study analyzes the economic feasibility, stability, and challenges of implementing AI vision systems within Industry 4.0. Compared to previously published studies in this field, this work is distinguished by the implementation of a cobot and an AMR in a unified system for visual inspection and control, with real-time data used for predictive maintenance. The system is connected to Computerized Maintenance Management Systems software for maintenance planning and monitoring and Enterprise Resource Planning software for real-time business activity planning. The results demonstrate that the integration of advanced robotics, computer vision, and machine learning algorithms enables the transformation of the traditional reactive approach into a proactive asset management model, thereby ensuring a long-term sustainable increase in reliability, safety, and competitiveness of the manufacturing processes. Received: 26 August 2025 | Revised: 21 October 2025 | Accepted: 27 October 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Isak Karabegovic: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
Influence of Nano Zinc Oxide Reinforcement on the Diffusion Coefficient of Unsaturated Polyester and Nitrile Butadiene Rubber Composites
This research investigates the influence of zinc oxide (ZnO) nanoparticle reinforcement on the diffusion coefficient of epoxy/nitrile butadiene rubber (EP/NBR) composites. The study evaluates various ZnO concentrations (1%, 2%, and 3% by weight) incorporated into the EP/NBR matrix. The dispersion of ZnO nanoparticles (ZnONP) was analyzed using X-ray diffraction and scanning electron microscopy, revealing improved particle dispersion with increased ZnO content, contributing to better structural stability. Diffusion coefficient measurements in water, nitric acid, and sodium hydroxide (NaOH) solutions indicated that higher ZnO concentrations reduce the diffusion coefficient for water absorption, enhancing moisture resistance. However, a slight increase in diffusion coefficients was observed in NaOH solutions, demonstrating the modified composite’s variable interaction with different environmental conditions. The findings emphasize the role of ZnONPs in enhancing the mechanical and physical properties of EP/NBR composites, particularly in improving their resistance to moisture and alkaline environments, thereby broadening their potential applications in moisture-sensitive and chemically aggressive environments. Received: 11 August 2025 | Revised: 22 September 2025 | Accepted: 13 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Rafah Alwan Nassif: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Raghad Hamid Hilal: Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization, Project administration.
Dynamic THz Hologram Generation via Temperature-Tunable Aerogel-Spacer Graphene Metasurfaces for Sensing Applications
Metasurfaces, as two-dimensional artificial materials, have revolutionized optics by enabling unprecedented control over light propagation, with profound implications for sensing, encryption, displays, and computational imaging. This research presents the modeling and design of a circuit-based metasurface engineered to dynamically generate holographic images within the terahertz (THz) spectrum, thereby expanding the horizon of practical technological applications. The proposed methodology employs an equivalent circuit model, grounded in transmission line theory, to represent the THz metasurface. This innovative approach effectively reduces complex electromagnetic interactions into discrete circuit components, facilitating a highly efficient and intuitive analysis of key performance metrics such as reflection magnitude and phase shift. A genetic algorithm was subsequently deployed to optimize the critical unit cell parameters including the dimensions of the graphene ribbons, their periodicity, and the thickness of the dielectric spacer to achieve a full 2π phase coverage at the target operational frequency of 0.8 THz, a prerequisite for high-fidelity hologram generation. Crucially, the dynamic reconfiguration of the generated THz holograms is enabled by the integration of temperature-sensitive aerogels, which modulate the metasurface’s optical response. The outcomes of this optimization are validated through full-wave electromagnetic simulations, which confirm enhanced operational efficiency and remarkable flexibility. This work underscores the potent synergy of circuit modeling and metasurface technology, paving the way for scalable, cost-effective, and high-performance active THz devices for next-generation communication and imaging systems. Received: 8 August 2025 | Revised: 17 September 2025 | Accepted: 16 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Alireza Barati Haghverdi: Software, Resources. Amir Ali Mohammad Khani: Validation, Investigation, Visualization. Ilghar Rezaei: Methodology, Formal analysis. Ali Soldoozy: Conceptualization, Data curation. Toktam Aghaee: Writing – original draft, Writing – review & editing, Supervision, Project administration.
From Wires to Wearables (2): Temporal Fidelity Assessment of the Sydäntek Wearable ECG System Against a Legacy Cloud-Based Standard
Validation of wearable electrocardiography (ECG) systems is critical to establishing diagnostic reliability and meeting regulatory standards. In this second installment of the Wires to Wearablesseries, we evaluate the temporal fidelity of the Sydäntek platform from Carditek Medical Devices against the Food and Drug Administration (FDA) approved, cloud-enabled Welch Allyn CardioPerfect™ system. The analysis focuses on core interval measurements: PR, QRS, and QT durations. Simultaneous recordings of calibration pulses and clinical ECGs were obtained using both devices across 498 patient datasets. Interval measurements were assessed using IEC 60601-2-25 benchmarks. Validation tools included agreement funnel plots for tolerance compliance assessment, Bland–Altman analysis for bias and dispersion visualization, and polar accuracy spider plots for sectoral alignment and precision mapping. Together, these methods captured comparative accuracy and reference fidelity between systems. Sydäntek demonstrated >99% concordance within strict ±5 ms tolerance thresholds in calibration pulse analyses, outperforming Welch Allyn, which showed broader dispersion. In clinical ECG recordings, Sydäntek consistently exhibited tighter limits of agreement and stable sectoral placement across all intervals. Despite the recognized variability, Welch Allyn served as the comparator due to its FDA-approved status and established cloud infrastructure. The Sydäntek wearable system achieves superior temporal accuracy and consistent reference alignment across both calibration and clinical datasets. With its high-fidelity signal acquisition, cloud-ready design, and decentralized deployment potential, Sydäntek is well-positioned for expanded validation within the “Wires to Wearables” framework and future clinical integration. Received: 1 August 2025 | Revised: 19 September 2025 | Accepted: 30 September 2025 Conflicts of Interest Dr. Sugandhi Gopal is an interventional cardiologist and is also the principal designer of the Sydäntek wearable ECG platform and holds a directorship position at Carditek Medical Devices, the entity responsible for its development and strategic deployment. This affiliation may be perceived as a potential conflict of interest. The author affirms that all data interpretation, study design, and manuscript preparation were conducted objectively and independently and that institutional efforts have been made to maintain transparency and scientific rigor throughout the research process. Mr Mohith Subramanian and Ms Poulami Roy, engineers, are involved in this project and are affiliated with Carditek Medical Devices in roles specific to signal processing and algorithm development. Their contribution was technical in nature and did not influence the study’s clinical design or data interpretation. Ms Indu Subramanya served as clinical trial coordinator, and her role was limited to study logistics, data collection, and regulatory alignment. None of the above holds any financial or professional interest in Carditek Medical Devices. Five senior cardiologists—alongside and including Dr Sugandhi Gopal, Dr Prabhavati Bhat, Mukund Prabhu, Sharada Sivaram, and V J Karthikeyan—participated in clinical evaluation and interpretation of ECGs, specifically assessing device-generated changes and their implications on diagnostic decision-making. These clinicians have no financial ties to Carditek Medical Devices and declare no conflicts of interest regarding this study. All contributors affirm adherence to ethical research practices and confirm that their roles did not introduce bias or commercial influence into the study’s execution or reporting. Data Availability Statement The data is available in a folder that is domain dependent and may be provided for research purposes with adequate justifications. Author Contribution Statement Sugandhi Gopal: Conceptualization, Methodology, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Prabhavathi Bhat: Validation. Sharada Sivaram: Validation. Mukund Prabhu: Validation. V. J. Karthikeyan: Validation. Poulami Roy: Methodology, Software, Formal analysis, Investigation. Mohith Subramanian: Software, Formal analysis, Investigation, Data curation, Writing – review & editing, Visualization. Indu Subramanya: Investigation.
From Wires to Wearables (1): Clinical equivalence with an Axis-Aware Validation of a Wearable ECG System (Sydäntek) with Compressed Lead Geometry
Conventional 12-lead electrocardiogram (ECG) systems, reliant on wired configurations and distal limb electrodes, restrict patient mobility and hinder continuous monitoring. Recent advances in wearable ECG aim to deliver diagnostic-grade fidelity in ambulatory settings. Sydäntek—a compact wearable ECG developed by Carditek Medical Devices—addresses these limitations by compressing Einthoven’s triangle into a 5 × 5 cm patch positioned at the left shoulder crease. This novel geometry enables full 12-lead acquisition without compromising signal fidelity. In a clinical comparison with a standard ECG system (Welch Allyn CardioPerfect™), Sydäntek’s performance was evaluated using Bland–Altman analysis and polar histogram visualization across a stratified population. Patients were grouped by baseline QRS axis into three ranges (−30° to 0°, 0° to +45°, and +45° to +90°) to assess axis-dependent variability. Sydäntek demonstrated consistent waveform morphology and a predictable QRS axis rotation (∼ +14°), attributable to anterior repositioning of limb leads. Polar histograms revealed a leftward skew without significant outliers. Subgroup Bland–Altman analysis showed systematic, baseline-dependent variability: the 0° to +45° group exhibited the narrowest limits of agreement, while the −30° to 0° cohort displayed greater dispersion. Applying a +14° correction effectively minimized axis bias within the clinically relevant −30° to +90° range, preserving diagnostic waveform integrity. Overall, Sydäntek achieves clinical equivalence with standard ECG through a geometry-driven axis adaptation that is consistent, manageable, and clinically acceptable. Its compact, wire-free form factor enables high-fidelity cardiac monitoring in real-time ambulatory settings—demonstrating that spatial innovation can coexist with gold-standard diagnostics and advance the ECG into wearable practice. Received: 20 July 2025 | Revised: 8 September 2025 | Accepted: 25 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Sugandhi Gopal: Conceptualization, Methodology, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Prabhavathi Bhat: Validation. Sharada Sivaram: Validation. V J Karthikeyan: Validation. Mukund Prabhu: Validation. Mohith Subramanian: Methodology, Software, Formal analysis, Investigation, Data curation, Writing – review & editing, Visualization. Vishwateja Reddy: Software, Formal analysis, Investigation. Aishwarya Srinivasan: Software, Formal analysis, Investigation.
Switching on Smart Care: The Ascendancy of Wireless Technologies in Continuous Health Surveillance
Guided by such relentless scientific curiosity, the field of wearable diagnostics has evolved from experimental concepts into sophisticated, organ-centric platforms capable of capturing rich physiological and biochemical data in real time. This review encapsulates the interdisciplinary transformation wherein bioelectronics, materials science, and artificial intelligence (AI) converge to create next-generation wearables that intimately interface with organs such as the brain, eyes, heart, skin, and lungs. Graphene-based imperceptible e-skins now enable neuromuscular signal acquisition with angular resolutions approaching ∼4° and signal fidelity exceeding traditional Ag/AgCl electrodes. AI-enhanced electroencephalographic (EEG) headbands decode motor intent with >92% accuracy in under 2 s, paving the way for real-time brain–computer interactions. Simultaneously, noninvasive microneedle arrays and sweat-interfacing chemosensors demonstrate femtomolar sensitivity for glucose, lactate, and even nucleic acids, boasting >80% correlation with gold-standard clinical assays. The domain has experienced a >60% increase in advanced functional materials—PEDOT: PSS hybrids, MXenes, oxide nanosheets—and a >70% rise in mechanical adaptability and miniaturization, dramatically expanding diagnostic possibilities in ambulatory environments. Dry electrode systems in smart eyewear, epidermal patches, and Virtual Reality (VR)-integrated systems now maintain <1.13 μV Root Mean Square (RMS) noise levels, 98–99% classification accuracy, and uninterrupted operation exceeding 12 hours, even in motion-rich conditions. As these intelligent, autonomous devices continue to shrink the gap between biological and digital systems, they are poised not merely to monitor health but to redefine human–machine symbiosis in the era of predictive and personalized medicine. Received: 15 July 2025 | Revised: 9 October 2025 | Accepted: 21 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Simranjit Kaur: Conceptualization, Methodology, Investigation, Resources, Data curation, Writing – original draft. Tania Acharjee: Conceptualization, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Project administration. Debashree Das: Methodology, Resources. Monika Bhatia: Validation, Formal analysis, Data curation. Sushman Sharma: Validation, Visualization. Ashish Patel: Validation, Visualization. Dinesh Bhatia: Conceptualization, Methodology, Validation, Data curation, Writing – review & editing, Visualization, Supervision, Project administration.
An Android-Based Embedded Modified Iterative Fusion Deep Neural Network for Automated Detection of Macular Diseases Using OCT Images
Retinal optical coherence tomography (OCT) is a noninvasive and high-resolution diagnostic imaging modality used for early detection of choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen-based macular diseases (MDs). The manual procedure for screening these MDs using OCT images is cumbersome and may result in a false diagnosis. Hence, there is a requirement for an artificial intelligence-enabled automated approach for the accurate detection of macular disorders. This paper presents a modified iterative fusion deep neural network (MIFDNN) model and its implementation on an Android framework for the automated detection of CNV, DME, and drusen using OCT images. Initially, the U-Net model is used to obtain the enhanced OCT image from the noisy OCT image. The enhanced OCT image is used as input for the proposed MIFDNN model for detecting MDs. The MIFDNN employs a sophisticated dual-branch architecture comprising a custom basic branch deep convolutional neural network (CNN) module for hierarchical feature extraction, a fusion branch for iterative multiscale feature integration, dense layers, and a softmax layer for detecting MDs. The basic branch deep CNN consists of five convolution layers, five batch normalization (BN) layers, and three max-pooling layers. The classification performance of the proposed MIFDNN model is evaluated using OCT images from a public dataset. Our model has achieved a remarkable average classification accuracy of 98.17% using a 3-fold cross-validation strategy, outperforming several existing methods to detect MDs using OCT images. To facilitate real-world application, we have performed post-training quantization to reduce the size of the proposed MIFDNN model. The quantized model is successfully deployed on an Android platform, where the on-device inference maintained an impressive 98% accuracy with a throughput of approximately 92 images per minute, validating the proposed model’s practical feasibility for screening MDs using OCT images. Received: 11 July 2025 | Revised: 9 September 2025 | Accepted: 23 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in [Mendeley, Kaggle] at [https://data.mendeley.com/datasets/rscbjbr9sj/3, https://www.kaggle.com/datasets/obulisainaren/retinal-oct-c8]. Author Contribution Statement Umar Shafiq Ahmad: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Rajesh Kumar Tripathy: Conceptualization, Writing – review & editing, Visualization, Supervision, Project administration.
Inaugural Editorial: Pioneering the Future of Wearable Technology
It is my privilege to introduce Smart Wearable Technology (SWT), a premier open-access journal launched by Bon View Publishing Pte Ltd. in 2025. As Founding Editor-in-Chief, I envision SWT as a catalyst for interdisciplinary innovation—where materials science, electronics, and artificial intelligence converge to redefine wearables' role in healthcare, human-computer interaction, and sustainable development. This inaugural issue marks our commitment to pioneering solutions for global challenges. SWT arises from the pressing need to integrate diverse disciplines—materials science, electronics, AI, and medicine—to address complex, real-world problems. Our scope encompasses functional materials, wearable devices, and foundational technologies; yet, our ambition extends further: to promote innovations that significantly enhance quality of life. Whether through soft robotics restoring mobility, AI-enabled sensors predicting health crises, or e-textiles reshaping fashion, SWT is committed to disseminating research that blends creativity with real-world applicability. To ensure SWT evolves into a trusted and high-impact academic platform, we rigorously adhere to the Core Practices of the Committee on Publication Ethics (COPE), upholding the highest standards in peer review, research ethics, and editorial transparency. Key policies include: immediate Desk Rejection for submissions outside the journal’s scope or lacking novelty; Single-Blind Peer Review by a minimum of two independent experts; and mandatory plagiarism detection and formatting/language checks prior to publication. This inaugural issue is the result of collective dedication. I express my sincere appreciation to our Editorial Board—comprising leading scholars in wearable technology—whose strategic guidance shapes the journal's vision; to our reviewers for their meticulous and constructive feedback; and to the authors for entrusting SWT with their pioneering contributions. To the global research community: SWT is your platform. We welcome original research articles, reviews, and proposals for special issues addressing emerging frontiers in wearable technology. Together, let us accelerate the advancement of next-generation wearables. Finally, I extend heartfelt thanks to Bon View Publishing Pte Ltd—especially our Managing Editor, Miss Catherine Zheng—for their unwavering support. As we embark on this journey, I invite your continued engagement and feedback to further elevate SWT’s role in shaping the future of wearable innovation. Wishing all contributors and readers of Smart Wearable Technology (SWT) continued success!
Integrating Marine Mussel Distribution and Carbon Sequestration with Smart Wearable Technology: A Novel Intersection for Blue Carbon Monitoring and Environmental, Social, and Governance Innovation
Marine mussels contribute to blue carbon through biogenic calcification and filtration that enhance carbon burial in coastal sediments. Their ecological role is important yet under-quantified in many regions. Here, we present a practical framework that links mussel ecology with smart wearable sensing to enable continuous in situ observation of environmental drivers and organism responses relevant to carbon sequestration. We specify sensor modalities such as ion-sensitive field effect transistor pH, optical dissolved oxygen, temperature, salinity, turbidity, and inertial shell gape sensing. We detail device form factors for mussel-attached tags and habitat-mounted modules on buoys and autonomous platforms. Mussel-inspired adhesives and hydrogels are used as functional coatings for underwater attachment, encapsulation, and biofouling resistance. We describe renewable energy harvesting, energy storage, and power management for long deployments, together with an Internet of Things data pipeline, edge preprocessing, and cloud-based time series modeling and anomaly detection. We set measurable accuracy and resolution targets and a validation plan against standard instruments. The approach supports environmental, social, and governance reporting, aquaculture management, and citizen science. We discuss anti-biofouling strategies, ethics of bio-tagging, comparison with conventional monitoring, and future directions for robust blue carbon observatories. Received: 19 June 2025 | Revised: 4 September 2025 | Accepted: 23 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Chee Kong Yap: Conceptualization, Methodology, Data curation, Writing – original draft, Supervision, Project administration. Musefiu Adebisi Tiamiyu: Methodology, Writing – original draft. Kennedy Aaron Aguol: Methodology. Meng Chuan Ong: Software, Project administration. Wan Mohd Syazwan: Software. Noor Azrizal-Wahid: Validation, Writing – review & editing, Visualization. Faridah Qamaruz Zaman: Validation, Writing – review & editing, Visualization. Rosimah Nulit: Validation, Writing – review & editing, Visualization. Yoshifumi Horie: Formal analysis, Writing – review & editing, Visualization. Mohamad Saupi Ismail: Formal analysis, Writing – review & editing. Ahmad Dwi Setyawan: Investigation, Writing – review & editing. Krishnan Kumar: Investigation, Writing – review & editing. Wan Hee Cheng: Resources, Writing – review & editing. Chee Seng Leow: Resources, Writing – review & editing.
Polymer-Based Smart Material in 4D Printing Applications
The area of 4D printing is a revolutionary one that emerged from the integration of additive manufacturing and polymer science. In this area, materials can change dynamically in response to external stimuli. Complex structures with developing, shape-morphing, and adaptive behaviors can be created by integrating innovative 3D printing technology with intelligent materials, which are naturally sensitive to environmental cues. Polymer chemistry determines these smart materials and discusses the design concepts that control their responsiveness. A particular focus is on incorporating stimuli-responsive polymers, like hydrogels and shape memory polymers, into the 4D printing process. These ensuing structures suggest programmable behavior in response to light, humidity, and temperature variations in the environment. 4D printing has been designated a revolutionary technology with significant implications for sectors looking for dynamic, responsive, and personalized solutions. The study of polymer-based innovative materials in 4D printing presents novel prospects for ingenuity, presenting extraordinary chances for creating and producing materials with dynamic properties. The future of advanced manufacturing is being shaped by this research, which advances our fundamental understanding of intelligent materials and opens up novel applications in various fields. Received: 12 June 2025 | Revised: 26 September 2025 | Accepted: 22 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Md. Abu Shyeed: Conceptualization, Resources, Writing – original draft. Jannatul Ferdaus: Resources, Writing – original draft, Writing – review & editing. Md Faruk Hossain: Resources, Writing – original draft. Md Alal Hossain: Writing – review & editing. Khan Rajib Hossain: Conceptualization, Software, Writing – review & editing, Supervision.
The Recent Advancements to Measure the Blood Pressure Using Photoplethysmography, Electrocardiogram, and Microchannel
Uncontrolled blood pressure poses significant health risks, making accurate measurement essential in healthcare. Conventional blood pressure measurement methods, typically using inflatable cuffs, can cause patient discomfort, tissue damage, and are unsuitable for long-term monitoring. Consequently, researchers are exploring noninvasive, cuffless methods that provide continuous and accurate blood pressure assessment. This article presents a comprehensive review of sensors and estimation models used in cuffless blood pressure monitors, with a focus on enhancing accuracy and minimizing calibration requirements. A literature search was conducted using Google Scholar and reputable journals, including IEEE, Frontiers, and MDPI, resulting in the selection of 35 relevant studies. The review examines innovative techniques based on electrical, mechanical, and optical sensors. Particular attention is given to photoplethysmography (PPG), electrocardiography (ECG), and bioimpedance (Bio-Z), which, when combined with advanced signal analysis and deep learning models, show promising results. PPG enables blood volume measurement at accessible sites like the fingertip or wrist, leveraging parameters such as pulse transit time. ECG, which directly reflects heart activity, is also widely used for blood pressure estimation. Recent advancements in machine learning have improved accuracy, with models such as HGCTNet (a hybrid CNN-Transformer architecture) achieving an error margin of 0.9 ± 6.5 mmHg for diastolic and 0.7 ± 8.3 mmHg for systolic blood pressures. Despite the potential, challenges remain, including the need for continuous calibration of PPG-based systems. Ongoing research aims to address these limitations by improving signal quality and developing robust algorithms. The demonstrated accuracy and reduced calibration requirements suggest that cuffless blood pressure monitoring technologies may soon become viable for widespread clinical and home use. Received: 10 June 2025 | Revised: 28 July 2025 | Accepted: 12 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Hajar Danesh: Conceptualization, Writing - review & editing. Hamidreza Shirzadfar: Conceptualization, Writing - review & editing. Mahla Manian: Investigation, Writing - original draft. Melika Pazhom: Investigation, Writing - original draft.
Video-Based Feedback in Table Tennis Training: Toward a Wearable Learning for Undergraduate Education
The study examined the role of video-based feedback in developing foundational skills in table tennis among undergraduate students. In physical education settings where traditional methods often rely on instructor-led demonstration and repetition, the integration of visual tools offers a way to enhance understanding and support deeper engagement with motor learning. The research focused on how recorded practice sessions could be used to improve stroke accuracy, ball control, and gameplay strategies through self-reflection and peer dialogue. A single-group experimental design was applied over an eight-week period involving 40 university students with no formal background in table tennis. Each week introduced progressively complex skill exercises, supported by student-generated video recordings using personal mobile devices. These clips served as a basis for self-review and peer exchange. Technical performance was evaluated using a structured skill assessment rubric in both pre- and post-intervention phases. Learner experiences were collected through a Likert-scale questionnaire and short written reflections. Statistical analysis showed significant improvement across all skill areas, particularly in backhand technique and rally control. Students reported that video playback enabled clearer awareness of movement errors and supported more precise adjustments over time. This replayability fostered self-awareness and active engagement. Many participants noted that feedback drawn from video felt more personal and actionable compared to traditional verbal correction. Although wearable devices were not yet deployed during the intervention, the instructional approach was intentionally designed toward wearable learning. Future integration may include smart bands or sensor-based feedback systems, enabling learners to overlay biomechanical data with video for enhanced movement analysis. The findings support broader adoption of video feedback as a reflective learning tool and highlight the potential for combining it with wearable technologies in sport education. This direction represents a promising step toward more autonomous and personalized skill development in physical activity contexts. Received: 4 June 2025 | Revised: 4 August 2025 | Accepted: 18 August 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Termpetch Sookhanaphibarn: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing—original draft, Writing—review & editing, Visualization, Supervision, Project administration.
Recent Advances in Flexible Perovskite Light-Emitting Diodes
Flexible perovskite light-emitting diodes (f-PeLEDs) have demonstrated their great potential for next-generation lighting and wearable display technologies due to their high color purity, excellent optoelectronic performance, solution processability, as well as their ultrathin and lightweight nature. Currently, the external quantum efficiency (EQE) and device stability of perovskite light-emitting diodes (PeLEDs) have been constantly updated in the past decade. However, limited by the inherent defect of perovskite and poor interfacial contact, the EQE of f-PeLEDs is still far lower than that of devices on rigid glass substrates. Therefore, it is necessary to provide an insightful summary to promote their further development. This review summarizes the strategies and the latest progress in improving the optoelectronic and mechanical performance of f-PeLEDs through the optimization of flexible substrates and electrodes, hole transport layers, perovskite layers, and fabrication techniques. Finally, this work summarizes and discusses the development and future challenges of flexible light-emitting diodes in the display field. Received: 20 May 2025 | Revised: 1 July 2025 | Accepted: 22 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Qi Han: Methodology, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Peng Han: Formal analysis, Resources. Daizhe Wang: Software. Yong Zhang: Conceptualization, Supervision, Project administration, Funding acquisition.