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 March 2026.
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.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
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 Conflict 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.
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.
Dual-Model Fusion for Ultra-Accurate Embedded Object Detection
Numerous industries consider their needs ultra-high accuracy with regard to an artificial intelligence (AI)-detected object and pose a challenge with real-world variations. This study specifically focuses on industrial manufacturing lines, where quality control is critical. The methods we discuss here should be transferable to other industry domains with similar constraints, such as logistics or packaging. The primary objective is to achieve greater than 99.9% accuracy with object detection in real-time industrial environments, without significantly impacting the latency. For that, we worked on an SSD_MobileNet model that was refined to the utmost precision and implemented alongside a dual-model system that used a generalist surrogate trained on blurred synthetic images. To achieve blur efficacy, the second model had to be blur trained, blending contextual depth and resilience. Both models’ outputs are fused through a low-computational-cost, high-confidence detection using Intersection over Union metrics selection (>=0.8) to strike a balance between efficiency and detection reliability. Model fusion has better results compared to model stacking or score-based thresholds because it decides on the best detection by considering the spatial overlap of detections and the agreement of class IDs. On the Nvidia Jetson Orin NX platform, deploying this ensemble achieved 99.8% accuracy and further boosted the system to 99.97% without expanding inference passes. Smart dual-model implementation helps increase precision and fault-tolerant parameters while maintaining streamlined recalibrated embedded systems thresholds, proving non-breach. This work supports the shift toward AI-powered advanced industrial surveillance, and research focuses on multidisciplinary approaches toward precise, reliable object detection. Received: 28 April 2025 | Revised: 26 June 2025 | Accepted: 15 July 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The dataset used is proprietary and not available in the public domain. Author Contribution Statement Leendert Remmelzwaal: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Synchronized Measurement of Electromechanical Responses of Fabric Strain Sensors Under Large Deformation
Fabric-based strain sensors hold significant potential across various applications, including sports, healthcare, rehabilitation, etc. Nonetheless, their complex performance under large deformation and varying loading rates, arising from material viscoelasticity and textile structure intricacies, remains inadequately understood. The primary constraint in evaluating their performance lies in the absence of electromechanically coupled instrumentation. This paper endeavors to overcome the limitation by developing a synchronized measurement system, which integrates mechanically controlled loading, voltage divider circuits, and visual measurement technologies. This system enables synchronized acquisition of mechanical and electrical signals spanning from 0.01 mm/min to 6 m/s, by employing material testing machines for low-speed loading and split Hopkinson pressure bars for medium-to-high-speed loading, and combining electrical performance measurements with displacement and strain field analyses. Experiments revealed that sensor sensitivity increased linearly with the logarithm of loading rates, while deformation patterns evolved with loading speed, thereby offering valuable insights into design and calibration of fabric-based strain sensors under dynamic conditions. Received: 27 April 2025 | Revised: 11 June 2025 | Accepted: 26 June 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Bao Yang: Conceptualization, Methodology, Formal analysis, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration, Funding acquisition. Xianyu Zhu: Software, Formal analysis, Data curation, Writing – original draft. Chang Peng: Software, Validation, Formal analysis, Investigation, Data curation, Visualization. Licheng Zhou: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Funding acquisition. Fei Wang: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing, Supervision, Funding acquisition. Zejia Liu: Conceptualization, Methodology, Resources, Supervision, Project administration, Funding acquisition. Liqun Tang: Conceptualization, Methodology, Resources, Supervision, Funding acquisition. Zhenyu Jiang: Conceptualization, Methodology, Resources, Supervision, Funding acquisition. Yiping Liu: Conceptualization, Methodology, Resources, Supervision, Funding acquisition. Senxin Chen: Software, Validation, Investigation.
Study on Diabetes Detection Based on DBO-RF Pulse Wave Analysis
As society progresses, diabetes has emerged as one of the major global health concerns. Traditional diagnostic methods for diabetes rely heavily on blood tests, which are often invasive, costly, and require specialized equipment. Consequently, this study adopts a non-invasive pulse wave analysis technique, utilizing key feature extraction from pulse wave signals combined with machine learning techniques to predict the presence of diabetes risk. Data for this study were sourced from the Guilin People’s Hospital, with a collection of 657 pulse wave samples from 219 participants. Through signal preprocessing and feature extraction, a classification model centered on Dung Beetle Optimization (DBO) and Random Forest (RF) was developed to detect diabetes. The preprocessing included the use of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise combined with Permutation Entropy and wavelet thresholding methods to enhance signal quality. Subsequently, 29 key features, including time domain, entropy domain, and statistical features, were extracted from the processed signals to create a comprehensive feature set. Here optimize the model’s classification performance and address issues of missing values in feature vectors and sample imbalance, further evaluate various machine learning algorithms and select the most effective one. Ultimately, the DBO algorithm was applied to optimize the number of decision trees and the selection of feature numbers in the RF classifier. Experimental results demonstrated that our method achieved a 100% accuracy rate on the training set and 92.9% on the test set, significantly outperforming traditional machine learning approaches. These findings confirm the potential of non-invasive pulse wave analysis in the early detection of diabetes, offering possibilities for future clinical applications. Received: 21 April 2025 | Revised: 20 June 2025 | Accepted: 23 June 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. Author Contribution Statement Quanyu Wu: Conceptualization, Methodology, Validation, Formal analysis, Resources, Writing – review & editing, Visualization. Shilong Gong: Software, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing. Mingying Hu: Software, Investigation, Writing – original draft, Writing – review & editing. Lingjiao Pan: Writing – review & editing. Xiaojie Liu: Supervision. Weige Tao: Project administration.
Frontier Exploration of the Fusion of Embodied Intelligence and Large Language Models
Embodied intelligence (EI) generates intelligent behaviors through interactions between the body and the environment but exhibits deficiencies in areas such as semantic understanding. In contrast, large language models (LLMs) possess powerful language processing capabilities. Integrating the two can enable a unified system that combines perception, cognition, and decision-making. This article first introduces the concepts, challenges, and recent breakthroughs in EI. It then analyzes the origin, core technologies, and research progress of LLMs. Subsequently, it explores the integration pathways and application scenarios of combining the two paradigms. Finally, the paper highlights ongoing challenges, such as the need for high-quality data and standardized benchmark environments, and underscores the importance of cross-industry collaboration as well as partnerships between academia and industry. The integration of EI and large language models represents a promising direction for the development of general artificial intelligence and is expected to drive innovative applications of intelligent systems across diverse domains. Received: 21 April 2025 | Revised: 16 June 2025 | Accepted: 17 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 Fujiang Yuan: Conceptualization, Methodology, Writing – original draft, Writing – original draft. Hong Jiang: Validation, Formal analysis, Resources, Writing – original draft, Writing – review & editing. Haoran Guan: Software, Investigation, Data curation, Visualization. Yanhong Peng: Supervision, Project administration, Funding acquisition.
Photonic Crystal-Based Wi-Fi Patch Antenna Implemented on FR4 Substrate
Antennas are an important part of the current ecology of wireless communication, particularly as it concerns Wi-Fi data rates. The traditional antennas suffer from the issues of urban bandwidth constraints and material efficiency. These issues lead to bad performance and coverage of services. The proposed work outlines a distinctive rectangular patch antenna designed on the photonic crystal structure and partial ground plane at the frequency of 2.4 GHz Wi-Fi band (2.4–2.48 GHz). The antenna is constructed on 40 mm by 50 mm FR4 substrate with εr = 4.3, tan δ = 0.025, 1.6 mm thick and excite by 50-ohm microstrip inset-feed to provide the optimal impedance matching. It is carried out using CST Microwave Studio Suite 2024 in terms of the design, simulation, and performance assessment. It is remarkable that the antenna has VSWR of 1.039064, return loss (S11) of−34.26823 dB, and bandwidth of 180.043 MHz (2.314581 GHz to 2.494624 GHz). It would serve as an excellent choice in many Wi-Fi applications because of its incredible gain of 2.86 dBi, directivity of 4.61 dBi, and a radiation efficiency of 66.88 percent. The compact size, the wide bandwidth, and the effective signal transmission characteristics of the proposed antenna make it easy to incorporate into the smart home systems of consumer electronics and numerous devices of the Internet of Things. This research aims at making the performance and reliability of current wireless communication network in dynamic environment better and reliable through the provision of a scalable solution. Received: 9 April 2025 | Revised: 28 May 2025 | Accepted: 17 June 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 A.N. M. Shihab Uddin: Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Visualization. Samiul Bashir: Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Visualization. Pronab Kumar Paul: Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Visualization. Md. Firoz Ahmed: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Md. Bipul Islam: Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Visualization. M. Ismail Hossain: Writing – review & editing. M. HasnatKabir: Writing – review & editing.
Software Defect Prediction Using Traditional Machine Learning and Ensemble Learning Algorithms
Software defect prediction (SDP) leverages machine learning to identify potential defects in software systems, enhancing software quality and reducing maintenance costs. Traditional techniques like Naïve Bayes, Decision Trees, and support vector machines (SVM) have been widely used for defect prediction due to their simplicity and interpretability. However, ensemble techniques such as Random Forest (RF), AdaBoost, and Bagging have gained prominence for their ability to improve accuracy by combining multiple models. While these methods have demonstrated promising results, challenges like overfitting, imbalanced datasets, and inadequate model tuning remain critical for further improvement in SDP. There are various types of datasets available in online. We have selected four datasets. We employed RF, Logistic Regression (LR), Gradient Boosting (GB), K-Nearest Neighbor (KNN), Decision Tree (DT), and XGB Classifier. In order to thoroughly investigate and assess each model’s predictive potential for identifying software defects and the issues they cause, we also used ensemble approaches. The results were promising, with the KNN achieving a noteworthy test accuracy of 87.08% for Dataset PC1, KNN of 76.49% for dataset JM1, KNN of 81.45% for dataset KC1 and GB of 92.89% for dataset CM1. Furthermore, Boosting GB also demonstrated accuracy matching this high standard of 94.5% for dataset CM1. To further enhance performance, we implemented a range, including Bagging, Boosting, Stacking, and Voting algorithms, optimizing each classifier with the best parameters through hyperparameter tuning. Through our experimental investigation, we not only contributed to the body of knowledge on SDP but also identified the Gradient Boosting with Boosting model as the most accurate, achieving an outstanding accuracy rate of 94.5% for SDPs. This research endeavors to provide invaluable insights into software defect management, offering a potential solution for early intervention and ultimately improving software outcomes. Received: 10 March 2025 | Revised: 22 April 2025 | Accepted: 12 May 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 Software Defect Prediction Datasets at https://github.com/SinghJasmeet585/Software-Defect-Prediction/tree/master/Data/data. Author Contribution Statement S.M. Hasan Kabir: Methodology, Software, Formal analysis, Data curation, Writing – original draft, Writing – review & editing. Md. Tanim Rahman: Software, Validation, Data curation, writing – original draft, Writing – review & editing. Aunik Hasan Mridul: Conceptualization, Software, Investigation, Resources, Data curation, Writing – review & editing, Visualization, Supervision, Project administration.
Tunable Graphene-Based Wave Absorber: Combined Electrical and Mechanical Tuning
Here in this work, a graphene-based absorber in THz waves is introduced. The structure exploits the mechanical adjusting setup using a Micro-Electro-Mechanical Systems comb driver. In addition, the periodic graphene rings and disks are used on top of the TOPAS (cyclic olefin copolymer) spacer. The middle layer spacer is considered as an air gap that can be filled by any target samples. The mechanical tuning besides the influence of the chemical potential makes the proposed structure most tunable with more than five absorption peaks in the THz gap. Two parallel simulation paths are followed in this work. First, an equivalent circuit model representation is developed, and the absorption response is obtained by leveraging the impedance matching theorem. Then the full-wave numerical modeling via the finite element method is performed to investigate the first approach’s validity and accuracy. It is shown that combining electrical and mechanical stimulations can change the number of absorption peaks and also their frequencies. According to the simulation results, the proposed absorber is highly reliable and robust against probable geometrical mismatches, while almost all of the THz spectrum is covered by the proposed structure. Received: 5 April 2025 | Revised: 9 June 2025 | Accepted: 12 June 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Amir Ali Mohammad Khani: Software, Resources. Alireza Barati Haghverdi: Methodology, Formal analysis. Ilghar Rezaei: Validation, Investigation. Seyed Javad Hosseini Zoljalali: Conceptualization, Data curation, Visualization. Toktam Aghaee: Writing – original draft, Writing – review & editing, Supervision, Project administration.
Risk Appraisal and Protection Against the Adverse Effects of Electromagnetic Fields on Living Tissues and Near-Tissues Health Devices
This contribution aims to analyze the hostile side effects of electromagnetic fields on living tissues as well as smart health tools that work in proximity to living tissues. These include medical intervention tools, wearable devices, and imaging scanners. The evaluation, control, and protection against these adverse effects are also targeted. The implicated physics and their corresponding ruling mathematics are highlighted and discussed. Mathematical modeling using discretized local 3D numerical tools allowed the design, control, and validation of the durability of the devices. These different themes are supported by illustrated cases and an overview of bibliographic examples. The results of the investigation illustrate that the physical events involved in the origins of radiation and stray fields, as well as their adverse effects on exposed materials and their protection, allow a thorough understanding and control of their management. Moreover, the corresponding mathematical formulations dictating the behaviors of the involved fields allow their assessment according to Responsible Attitude and One Health approaches that enable ecological biodiversity and ecosystem. Received: 3 April 2025 | Revised: 29 May 2025 | Accepted: 4 June 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 Adel Razek: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Motion Trajectory Estimation for Hand Grasping States Using a Deep Learning Approach
Predicting the final grasp tendency at the start of movement in prosthetic hands is crucial for improved control. Biological data, such as 3D movement and muscle activity, have been using by researchers to predict the final grasp. Early prediction of the intended grasp allows the prosthetic device to initiate control actions before the motion is complete, resulting in faster and more intuitive responses. Most machine learning algorithms are trained to predict the gesture of the final grasp. The aim of this study is to accurately estimate the final grasp state using inertial measurement unit (IMU) data. This estimation, based on movement trajectories, will allow prosthetic devices to respond more quickly to user actions. Deep Learning model was trained using movement data collected from a prosthetic hand controlled certain gesture trajectories without any human involvement. Data such as acceleration, angular velocity, and orientation were gathered through IMU sensors to create 3D orientation matrices representing the movement process. A deep convolutional neural network was used for training, with data labeled by the final grasp states. The deep learning algorithm successfully predicted the final hand motion with 93% accuracy. This trained model enables the generation of smooth supervisory trajectories, facilitating faster and more accurate control of the prosthesis. The proposed model demonstrates significant potential in improving prosthetic hand control by predicting the final hand movement at an early stage of motion, contributing to more responsive and effective prosthetic devices. Received: 12 March 2025 | Revised: 24 April 2025 | Accepted: 12 May 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 GitHub respiratory at https://github.com/BioAstLab/MotionTrajectoryEstimation. Author Contribution Statement Erdem Erdemir: Conceptualization, Methodology, Software, Investigation, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Erkan Kaplanoglu: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Project administration. Cihan Uyanik: Software, Validation, Formal analysis, Resources, Data curation, Writing – review & editing. Gazi Akgun: Validation, Data curation, Writing – original draft.
Flexible Tactile Sensors for Enhancing Robotic Perception
As robotic technology advances towards autonomy and intelligence, tactile sensing systems have become a key technology for overcoming the bottleneck of environmental interaction. Unlike visual perception, which captures macroscopic information, tactile sensing provides precise mechanical feedback for robotic manipulation by real-time analysis of microscopic physical parameters such as contact force, material properties, and surface morphology. This is particularly important in application scenarios that require high precision in force control, such as minimally invasive surgery and precision assembly, where the sensitivity, multimodal perception capabilities, and environmental adaptability of tactile sensors directly determine the operational performance of the robotic system. However, traditional tactile sensors are limited by rigid structures, limited sensitivity, and a single perception modality, making it difficult to meet the demands of complex interactions. In recent years, breakthroughs in flexible electronic materials, biomimetic microstructure design, and multimodal sensing integration technologies have provided innovative opportunities for tactile sensing systems. This review provides a detailed overview of the key technological advancements in tactile sensors and their applications in robotic surfaces, analyzes the major challenges currently faced, and looks ahead to future development directions, particularly the potential in flexible materials and intelligent algorithms. Received: 25 March 2025 | Revised: 26 May 2025 | Accepted: 28 May 2025 Conflicts of Interest PingAn Hu is the Editor-in-Chief 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 sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Huiwen Ren: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Wangyang Li: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Yanan Ding: Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Yuming Feng: Writing – original draft. Hexin Li: Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Jianyang Li: Writing – original draft, Writing – review & editing. Jia Zhang: Writing – original draft, Writing – review & editing, Supervision. Haiying Xiao: Writing – original draft, Writing – review & editing. Jialu Li: Writing – original draft, Writing – review & editing. Shuai Wang: Writing – original draft. Cong Huang: Writing – original draft, Writing – review & editing, Supervision. Li Jiang: Writing – original draft, Writing – review & editing, Supervision. PingAn Hu: Conceptualization, Methodology, Writing – original draft, Writing – review & editing, Supervision, Project administration, Funding acquisition.