A5035772048- Heart Rate Variability and Autonomic Control
- EEG and Brain-Computer Interfaces
- Emotion and Mood Recognition
- Pediatric Pain Management Techniques
- Human Pose and Action Recognition
- Pain Management and Opioid Use
- Pain Mechanisms and Treatments
- Musculoskeletal pain and rehabilitation
- Brain Tumor Detection and Classification
- Infrared Thermography in Medicine
- Advanced Neural Network Applications
- Medical Imaging and Analysis
Foundation for Research and Technology Hellas
2023-2024
Hellenic Mediterranean University
2022-2024
Accurate and objective pain evaluation is crucial in developing effective management protocols, aiming to alleviate distress prevent patients from experiencing decreased functionality. A multimodal automatic assessment framework for acute utilizing video heart rate signals introduced this study. The proposed comprises four pivotal modules: the Spatial Module , responsible extracting embeddings videos; Heart Rate Encoder tasked with mapping into a higher dimensional space; AugmNet designed...
The automatic estimation of pain is essential in designing an optimal management system offering reliable assessment and reducing the suffering patients. In this study, we present a novel full transformer-based framework consisting Transformer (TNT) model leveraging cross-attention self-attention blocks. Elaborating on videos from BioVid database, demonstrate state-of-the-art performances, showing efficacy, efficiency, generalization capability across all primary tasks.
Pain assessment is essential in developing optimal pain management protocols to alleviate suffering and prevent functional decline patients. Consequently, reliable accurate automatic systems are for continuous effective patient monitoring. This study presents synthetic thermal videos generated by Generative Adversarial Networks integrated into the recognition pipeline evaluates their efficacy. A framework consisting of a Vision-MLP Transformer-based module utilized, employing RGB unimodal...
Automatic pain assessment plays a critical role for advancing healthcare and optimizing management strategies. This study has been submitted to the First Multimodal Sensing Grand Challenge Next-Gen Pain Assessment (AI4PAIN). The proposed multimodal framework utilizes facial videos fNIRS presents modality-agnostic approach, alleviating need domain-specific models. Employing dual ViT configuration adopting waveform representations fNIRS, as well extracted embeddings from two modalities,...