Type 2 diabetes mellitus and prediabetes often develop silently and may remain undiagnosed for years. This is particularly relevant in regions where laboratory-based screening is not always readily accessible. Against this background, the present work explores whether multilead electrocardiography can provide physiologically meaningful markers potentially associated with disturbances in glucose metabolism. We developed and tested an upgraded wearable 12-lead ECG system capable of synchronized multichannel recording under controlled conditions. ECG signals were acquired in sitting and standing positions, with a sampling frequency of 500 Hz and a recording duration of one minute per posture. The hardware architecture included a high resolution analog front-end and wireless data transmission; the accompanying software provided acquisition control, preprocessing, visualization, and data storage within a unified framework. Signal processing focused on the extraction of rhythm-related and morphological parameters, with particular attention to ventricular repolarization indices. QT interval, heart rate–corrected QT (QTc), and QT dispersion (QTd) were calculated across leads, as these parameters are known to reflect heterogeneity of repolarization and autonomic influences on myocardial electrophysiology. The analysis was structured to ensure reproducible boundary detection and systematic feature formation rather than isolated parameter measurement. The study had a pilot character and included a limited and unbalanced sample (healthy n = 10; prediabetes n = 1; T2DM n = 1). For this reason, the results are presented descriptively and should be regarded as preliminary observations. In representative cases, differences in QT-related indices were noted between categories of glycemic status; however, the potential influence of age, sex, and other confounders cannot be excluded. A pilot expert comparison of T-wave end detection demonstrated close agreement between the automated algorithm and cardiologist assessment (mean Δ$T_{end}$ approximately −1 to −2 ms; MAE 10–24 ms). Diagnostic performance metrics such as ROC/AUC, sensitivity, and specificity were not calculated at this stage, as validation in a larger cohort with biochemical confirmation (HbA1c, OGTT) is required. The study demonstrates the technical feasibility of combining synchronized 12-lead wearable acquisition with structured multilead repolarization analysis. The proposed system should therefore be considered a research platform intended to support further clinical validation and methodological development rather than a finished screening solution.

Diabetes mellitus is projected to affect more than 1.3 billion people worldwide by 2050, with millions remaining undiagnosed or in a prediabetic state. Cardiovascular complications account for nearly half of diabetes-related deaths, highlighting the need for scalable tools capable of identifying metabolic dysregulation before irreversible cardiac damage develops. This review synthesizes current mechanistic, clinical, and computational evidence linking diabetes to cardiac electrophysiological remodeling and examines electrocardiography (ECG) as a non-invasive modality for early detection of dysglycaemia. Chronic hyperglycaemia, insulin resistance, oxidative stress, microvascular dysfunction, and cardiac autonomic neuropathy collectively contribute to measurable ECG alterations, including QT/QTc prolongation, increased QT dispersion, changes in Tp–e indices, and reduced heart rate variability. These changes often precede overt cardiovascular disease and correlate with glycaemic burden and diabetes duration. Recent advances in signal processing and artificial intelligence have expanded the diagnostic potential of ECG. Both classical machine learning approaches and large-scale deep learning models demonstrate that ECG contains latent signatures associated with incident type 2 diabetes and glycaemic status. Despite promising results, heterogeneity in study design, limited representation of prediabetes, and lack of standardized validation frameworks remain major barriers to clinical translation. Prospective, multi-ethnic studies are needed to establish ECG-based screening as a reliable component of early diabetes detection strategies.

The control of tendon-driven robotic fingers presents significant challenges due to their inherent underactuation, coupled with complex non-linear dynamics arising from tendon elasticity, friction, and external disturbances. Therefore, achieving precise control of finger motion and contact interactions necessitates advanced modeling, estimation, and control strategies capable of addressing uncertainties in tendon tension, routing, and elasticity. This paper presents a comprehensive comparative study of three distinct control paradigms: feedback linearization with Proportional-Derivative (FBL-PD) control, feedback linearization with super-twisting sliding-mode algorithm (FBL-STA), and deep-deterministic reinforcement learning (DDPG-RL), for the precise trajectory tracking of a three-link tendon-driven robotic finger. Through extensive simulations, the performance of each controller is rigorously evaluated based on trajectory-tracking accuracy and robustness to varying disturbances. The results indicate that under disturbance-free conditions, the FBL-PD and FBL-STA controllers, when properly tuned, achieve precise tracking of the reference trajectory; however, they produce noticeably noisy control signals. When subjected to external disturbances, these controllers exhibit increased sensitivity, producing even noisier responses. In contrast, the DDPG-RL maintains smooth control dynamics and achieves sufficiently accurate tracking in both scenarios. This comparative analysis elucidates the strengths and weaknesses of each control strategy, offering critical insights and practical guidelines for the design and implementation of advanced control systems for dexterous tendon-driven robotic fingers.

Traditional numerical methods for solving inverse kinematics in rehabilitation exoskeletons often suffer from high computational latency and struggle with dynamic torque optimization during high-frequency real-time control loops. Objective: Evaluation and testing a two-stage physics-informed feedforward neural network framework for solving the inverse kinematics of a 2-DOF upper-limb exoskeleton while simultaneously minimizing static gravitational joint torques. Methods. An off-line multi-start optimization routine was used to generate a workspace dataset of 99,208 optimal configurations; a feedforward architecture with 135,814 parameters incorporating a custom differentiable physical gravity layer was then trained to predict joint angles and correct residual loads. Results. Across a test set of 14,882 points, the model achieved a mean spatial positioning error of 0.385 cm (3.85 mm) with an approximate inference latency of 2.0 ms per sample, supporting closed-loop control frequencies up to 500 Hz without relying on resource-intensive online quadratic programming. Conclusions. The proposed physics-informed learning approach effectively combines high spatial accuracy, energy-efficient torque distribution, and deterministic execution speed, providing a lightweight, low-latency control solution for wearable upper-limb rehabilitation robotics.

Wearable electrocardiographic technologies have become increasingly important for continuous cardiac monitoring; however, most existing portable systems are limited by the number of recorded leads or provide only basic signal acquisition without advanced automated analysis. This study presents a third-generation wearable twelve-lead electrocardiographic platform developed for multilead ECG acquisition and automated spatial ECG analysis. Compared with the previous generation, the hardware modification primarily consists of architectural consolidation: functions previously distributed across an STM32 microcontroller and separate wireless communication modules are integrated into a single ESP32-S3-based architecture, while the ECG acquisition principle, ten-electrode configuration, and sampling rate remain unchanged. The main methodological contribution of the present work is the software pipeline for lead-specific ST₈₀ measurement and analysis of ST-segment deviations across anatomically contiguous leads. The system uses an ADS1298 analog front-end for synchronized multichannel ECG acquisition. The host software performs digital preprocessing, R-peak detection, ECG feature extraction, twelve-lead reconstruction, lead-specific ST₈₀ measurement, contiguous-lead analysis, and generation of a preliminary computer-assisted ECG report. The developed platform was clinically evaluated using sequential recordings acquired with the proposed system and a reference clinical electrocardiograph. Quantitative comparison of automated PR, QRS, QT, and QTc measurements in 30 paired recordings demonstrated positive correlations with the reference BTL Flexi 12 ECG (r = 0.756–0.820, all p < 0.001), with mean absolute errors ranging from 2.53 ms for QRS duration to 10.40 ms for the QT interval. The system successfully recorded diagnostically interpretable twelve-lead ECGs in all participants and produced stable signal quality suitable for clinical assessment. The software automatically identified ECG waves and intervals, reconstructed twelve-lead recordings, evaluated ST-segment deviations across individual leads, and localized ischemia-related changes according to standard anatomical lead groups. Integration of signal acquisition, processing, visualization, and automated interpretation into a single telemedicine-oriented platform reduced hardware complexity while maintaining reliable multichannel ECG monitoring. The proposed wearable platform demonstrates the feasibility of combining compact embedded hardware with automated multilead ECG analysis for remote cardiovascular monitoring. The presented architecture provides a practical foundation for telemedicine applications and may support earlier recognition of clinically significant electrocardiographic abnormalities during ambulatory monitoring.