The paper introduces ArrythML, a highly efficient autoencoder-based TinyML model that enables accurate, low-power arrhythmia detection directly on resource-constrained embedded wearable devices.

The paper proposes Family-Grouped Hierarchical Federated Learning (Family-FL) combined with a highly optimized Tiny CNN-LSTM model to enable privacy-preserving ECG monitoring on ultra-resource-constra…

The paper proposes a novel convolutional block and optimization algorithm to implement resource-efficient 1D-CNNs for atrial fibrillation detection on tiny smart sensor systems, achieving high accurac…

This paper introduces LVCG, a novel self-supervised framework that learns unified, view-invariant latent representations of cardiac electrical activity directly in the physically grounded Vectorcardio…

DSTAN-Med is a novel dual-channel attention framework that significantly improves False Data Injection (FDI) attack detection in IoMT medical devices by explicitly separating spatial and temporal depe…

The paper proposes a novel, highly secure real-time ECG monitoring framework that uses a patient's own ECG signal characteristics to generate unique, dynamic encryption keys, ensuring confidential dat…

This paper presents an end-to-end spatial-temporal transformer framework for remote heart-rate estimation from RGB camera images under varying illumination.

The paper proposes an SE ViT-BiLSTM hybrid model for enhanced intrusion detection in IIoT and IoMT environments, achieving superior performance on real-world datasets, especially after data balancing.

This paper presents BenDi, an energy-efficient quasi-stochastic systolic architecture for bioelectronic systems on the edge.

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

This paper proposes a lightweight CNN architecture that significantly enhances the adversarial robustness of EEG-based Brain-Computer Interfaces (BCIs) against malicious perturbations.

BIDO introduces a device-free, NIST AAL2-compliant biometric authentication standard that deterministically generates ephemeral ECDSA keys from live biometric measurements, eliminating the need for st…

SCAFDS introduces a novel, seven-stage graph attention system that models fraud propagation using co-occurrence edge features and generates forensically traceable SAR narratives, significantly improvi…

The paper proposes UF-AMA, a unified framework that achieves state-of-the-art cross-domain emotion recognition by adaptively aligning and fusing multimodal physiological signals like EEG and eye-track…

The paper introduces FHRFormer, a masked transformer-based autoencoder designed to accurately reconstruct missing and forecast fetal heart rate (FHR) time-series data, thereby enabling robust AI-based…

The paper proposes a dual-encoder architecture that fuses processed acoustic waveforms and spectrograms using a differentiable Choquet integral to improve underwater acoustic classification while main…

The paper proposes the Distilled Explanation Model (DEM), a novel glass-box framework that achieves high-accuracy, clinically interpretable anomaly detection in physiological sensor data by distilling…

The paper proposes EEG-FuseFormer, a transformer-based framework that fuses features from CNN-LSTM and ResNet-18 to achieve high accuracy in predicting seizure onset from EEG signals.