The physiological signals obtained from advanced sensors, combined with deep learning techniques for classification and regression tasks, have become a core driving force in enhancing smart healthcare. Recently, dense prediction tasks for physiological signals-aimed at generating predictions that are closely aligned with the input signal to enable fine-grained analysis-have garnered increasing attention. The UNet family, often combined with sophisticated task-specific customizations, has become a popular choice to improve prediction performance. However, pretrained Transformers have recently revolutionized deep learning due to their powerful transferability and effectiveness. In this work, we aim to harness the power of pretrained Transformers for dense prediction, eliminating the need for extensive task-specific architecture design. We propose a simple yet universal encoder-decoder architecture that utilizes a pretrained Transformer encoder and a lightweight convolutional Restormer decoder for dense prediction on physiological signals. To optimize the trade-off between model performance and computational efficiency, we incorporate knowledge distillation (KD). Our experiments focus on four representative dense prediction tasks: blood pressure waveform (BPW) estimation, PPG-to-ECG (P2E) reconstruction, denoising, and fiducial point localization. The results show that our proposed architecture outperforms state-of-the-art models, validating the potential of pretrained Transformers in enhancing physiological signal processing and medical diagnostics. This approach marks a significant step forward in optimizing both the performance and efficiency of dense prediction tasks.

OBJECTIVE: Multiple limitations exist in current automated ECG analysis, including insufficient feature integration across leads, limited interpretability, poor generalization, and inadequate handling of class imbalance. To address these challenges, we develop a novel dual-branch framework that comprehensively captures spatial-temporal features for cardiac disease diagnosis. METHODS: ECG-AuxNet combines a Multi-scale Transformer Attention CNN for spatial feature extraction and a GRU network for temporal dependency modeling. A Dual-stage Cross-Attention Fusion module integrates features from both branches, while a Feature Space Reconstruction (FSR) auxiliary task is introduced as a manifold regularizer to enhance feature discrimination. The framework was evaluated on PTB-XL (15,709 ECGs) and validated in real-world clinical scenarios (SXMU-2k, 1,673 ECGs). RESULTS: For class-imbalanced disease recognition (NORM, CD, MI, STTC), ECG-AuxNet attained 78.34% F1-score on PTB-XL and 82.63% F1-score on SXMU-2k, outperforming 9 baseline models. FSR significantly improved feature discrimination by 11.7%, enhancing class boundary clarity and classification accuracy. Grad-CAM analysis revealed attention patterns that precisely match cardiologists' diagnostic focus areas. CONCLUSION: ECG-AuxNet effectively integrates spatial-temporal features through auxiliary learning, achieving robust generalizability in cardiac disease diagnosis with interpretability aligned with clinical expertise.

Therapeutic robotic systems have emerged as reliable tools for physical rehabilitation, providing variableintensity movement assistance to patients with motor impairments. Robot-assisted rehabilitation facilitates the restoration mobility and dexterity, promotes functional neuroplasticity and potentially enables workforce reentry through training-induced cognitive and motor learning. To boost participant engagement and visuomotor coordination, we propose ArmGuider Pro, an advanced upper-limb training system that integrates hand-eye collaboration and gazetriggered assistance within rehabilitation-tailored serious games. The system implements intuitive eye-tracking and visualtriggering strategies to align therapeutic interventions with participants' intentional focus, incorporating immersive gaming elements and adaptive control algorithms. Experimental validation demonstrates significant activation in motor and cognitive cerebral cortex regions, enhanced visual attention concentration in desired target areas (25.92 % improvement), and improved trajectory adherence across sequential sessions (27.27 % improvement). By harnessing visual attention valence, our proposed system could encourage neuroplasticity, supporting its viability for clinical application and widespread adaption in rehabilitation regimens.

The precise monitoring of hemodynamic parameters is crucial for cardiovascular health assessment and disease prevention. However, current non-invasive hemodynamic monitoring technologies fail to balance performance and efficiency. To overcome these challenges, this study introduces a dual-focus multi-task fusion framework built on a Sparse Multi-gate Mixture-of-Experts Network (SM2oE) and multi-view photoplethysmography (PPG) for the accurate estimation of non-invasive hemodynamic parameters. This framework integrates a multi-scale fusion architecture with a hybrid attention mechanism and incorporates an Uncertainty Regression Loss function (UR-Loss) to enhance inter-task information fusion and overall model performance. Through structured pruning and bidirectional knowledge distillation, the framework effectively reduces computational costs and ensures model efficiency. Cross-database evaluation demonstrates that the proposed method achieves blood pressure estimation performance falling within the numerical thresholds specified by IEEE, BHS, and AAMI guidelines. Under a lightweight strategy, the LSM2oE exhibits significant clinical application potential, with mean absolute errors for systolic blood pressure, mean arterial pressure, diastolic blood pressure, and heart rate being 4.02 mmHg, 2.99 mmHg, 3.00 mmHg, and 2.40 bpm, respectively. The parameter counts and FLOPs are 1.26 M and 0.15 Gmac, respectively, surpassing existing baseline models with only 4.0% of the parameter volume of the teacher model. Additionally, leveraging a cloud-edge collaboration framework, we achieved hemodynamic assessment across multiple IoMT devices via a local area network and the RV1126 platform. The system demonstrates cross-scale analysis capability and real-time parameter estimation performance in IoMT environments, showcasing extensive application potential.

Cardiovascular disease remains a leading global health challenge, necessitating precise and continuous monitoring of blood pressure (BP) and cardiac function. Traditional noninvasive measurement techniques often present operational complexities and discomfort, while photoplethysmography (PPG) technology lacks the ability to capture comprehensive hemodynamic parameters. Herein, we introduce an innovative Internet of Medical Things (IoMT) framework for personalized hemodynamic assessment, driven by advanced flexible sensing technologies and multiscale modeling. Specifically, we propose a pulse wave detection system utilizing microfiber Bragg grating ($\mu $FBG) sensors for comprehensive spatiotemporal monitoring. The system leverages a multiscale perception network (MSP-Net) to achieve precise BP estimation, with mean errors (MEs) and standard deviations (SDs) of$- 0.05~\pm ~2.15$mmHg for systolic BP (SBP) and$- 0.19~\pm ~3.48$mmHg for diastolic BP (DBP). The system’s performance surpasses traditional PPG-based methods across multiple hemodynamic parameters. Leveraging pretraining and fine-tuning strategies, this study realizes personalized estimation models, furnishing technical support for precision medicine. Furthermore, we utilize a cloud-edge collaborative framework to achieve hemodynamic assessment across multiple IoMT devices via local area network and the RV1126 platform, providing real-time feedback, thereby establishing a closed-loop control system. Our study presents an innovative approach for the comprehensive assessment of cardiovascular function and the realization of personalized medicine, potentially serving as a promising solution for Healthcare 5.0 applications.

OBJECTIVE: The proliferation of wearable devices has escalated the standards for photoplethysmography (PPG) signal quality. This study introduces a lightweight model to address the imperative need for precise, real-time evaluation of PPG signal quality, followed by its deployment and validation utilizing our integrated upper computer and hardware system. METHODS: Multiscale Markov Transition Fields (MMTF) are employed to enrich the morphological information of the signals, serving as the input for our proposed hybrid model (HM). HM undergoes initial pre-training utilizing the MIMIC-III and UCI databases, followed by fine-tuning the Queensland dataset. Knowledge distillation (KD) then transfers the large-parameter model's knowledge to the lightweight hybrid model (LHM). LHM is subsequently deployed on the upper computer for real-time signal quality assessment. RESULTS: HM achieves impressive accuracies of 99.1% and 96.0% for binary and ternary classification, surpassing current state-of-the-art methods. LHM, with only 0.2 M parameters (0.44% of HM), maintains high accuracy despite a 2.6% drop. It achieves an inference speed of 0.023 s per image, meeting real-time display requirements. Furthermore, LHM attains a 97.7% accuracy on a self-created database. HM outperforms current methods in PPG signal quality accuracy, demonstrating the effectiveness of our approach. Additionally, LHM substantially reduces parameter count while maintaining high accuracy, enhancing efficiency and practicality for real-time applications. CONCLUSION: The proposed methodology demonstrates the capability to achieve high-precision and real-time assessment of PPG signal quality, and its practical validation has been successfully conducted during deployment. SIGNIFICANCE: This study contributes a convenient and accurate solution for the real-time evaluation of PPG signals, offering extensive application potential.

The aim of this study was to investigate the feasibility and effectiveness of using electrocardiogram (ECG) signals to predict ejection fraction (EF) in an extremely imbalanced dataset. We collected ECG signals from 9365 patients and conducted a correlation analysis with EF. After collecting and preprocessing the ECG signal, we developed a deep learning-based multi-task learning model designed to extract features from ECG signals and perform predictions. Our study employed a model based on Transformer, multi-scale convolutional neural networks (CNN), channel attention, and pre-trained ResNet to predict EF (EF > 50 or EF ≤ 50) and EF value. The experimental results demonstrate that the proposed model exhibits excellent predictive performance, with an AUC of 0.825 and MAE of 4.855 for predicting EF. It outperforms other models and shows better results in comparison. Our study validates the feasibility and effectiveness of using ECG signals to predict EF and provides strong support for early diagnosis and treatment of cardiovascular diseases.

OBJECTIVE: Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that can lead to a wide range of health issues if left untreated. This study aims to address the lack of research on personalized models for single-lead electrocardiogram (ECG)-based OSA detection, by proposing an automatic semi-supervised algorithm for automated low-cost personalization fine-tuning. METHODS: We utilize a convolutional neural network (CNN)-based auto-encoder (AE) with a modified training objective to detect anomalous region of OSA. An indicator based on model outputs is utilized as a benchmark measure to assign pseudo-labels with confidence to each sample. Finally, we perform validation of the semi-supervised algorithm on the same database and cross-database scenarios. RESULTS: By introducing semi-supervised personalization, the accuracy, AUC, and mean absolute error (MAE) of the general model (GM) of 35 subjects from the same database are improved from 86.3%, 0.915, and 5.178 to 90.3%, 0.948, and 2.593. Simultaneously, in the validation of 25 subjects from a cross-database, the accuracy, AUC, and MAE of the GM are enhanced from 75.6%, 0.800, and 9.149 to 84.3%, 0.881, and 3.509. CONCLUSION: The improved version of AE demonstrates excellent adaptability in identifying abnormal features in OSA, employing a data-driven approach to assign pseudo-labels for unknown data automatically. Additionally, leveraging the pseudo-labels through a semi-supervised fine-tuning strategy provides a solution to overcome the limitation of clinical annotations, facilitating low-cost implementation of personalized models. SIGNIFICANCE: The semi-supervised approach proposed in this article provides a high-performance and annotation-free solution for personalized adjustment of automatic OSA detection.