Special Session 3

 

Special Session 3: AI-Enabled Biomedical Signals and Image Processing for IoT Healthcare Systems

Description: Artificial intelligence is transforming healthcare by enabling automated analysis, continuous monitoring and timely decision support from diverse biomedical data. This special session focuses on AI-enabled methods that integrate biomedical signals and medical or biomedical imaging with Internet of Things (IoT) healthcare technologies. It provides a forum for researchers, engineers, clinicians and practitioners to share advances in signal and image acquisition, preprocessing, feature extraction, multimodal fusion, explainable learning, edge intelligence and connected health applications. Relevant biomedical signals include EEG, ECG, EMG, photoplethysmography and other physiological measurements, while imaging studies may involve modalities such as fundus, microscopy, ultrasound, X-ray, CT and MRI. Particular attention is given to reliable and interpretable solutions that can operate on wearable, portable or resource-constrained devices. The session also welcomes work addressing data quality, privacy, security, interoperability, clinical validation and real-world deployment. By bringing together AI, biomedical engineering, imaging and IoT perspectives, the session aims to encourage cross-disciplinary collaboration and responsible translation of intelligent healthcare technologies into practical screening, monitoring, diagnosis and personalised care.

Session organizer
Ts. Dr. Chew Kim Mey, Universiti Malaysia Sarawak (UNIMAS), Malaysia

The topics of interest include, but are not limited to:
• AI and machine learning for biomedical signal processing and image analysis
• Multimodal fusion of physiological signals, images and contextual data
• Wearable, portable and IoT-enabled healthcare monitoring
• Deep learning for disease detection and clinical decision support
• Explainable, reliable and personalised AI for healthcare
• Data quality, privacy, security and interoperability in connected healthcare
• Edge AI for biomedical sensing and smart healthcare
• Clinical validation and real-world deployment of AI-enabled healthcare systems
• Ontology learning and semantic knowledge for healthcare data
• OCR and NLP integration for medical and health information

Submission method
Submit your Full Paper (no less than 10 pages) or your paper abstract-without publication (200-400 words) via Online Submission System, then choose Special Session 3 (AI-Enabled Biomedical Signals and Image Processing for IoT Healthcare Systems)
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Introduction of session organizer

 

Ts. Dr. Chew Kim Mey
Universiti Malaysia Sarawak (UNIMAS), Malaysia

Ts. Dr. Chew Kim Mey is a Senior Lecturer in the Faculty of Computer Science and Information Technology at Universiti Malaysia Sarawak (UNIMAS), Malaysia. She has more than 15 years of research experience in computer science and interdisciplinary applications. Her research interests include artificial intelligence, biomedical signal processing, EEG analysis, explainable AI, Internet of Things healthcare applications and data analytics. Her current work investigates EEG-based neurovisual strain and eye-strain patterns in children, with emphasis on signal-quality assessment, ocular-artifact reduction, interpretable biomarkers and personalised monitoring. She is also involved in research on AI-supported biomedical imaging and preventive healthcare. Dr. Chew has contributed to conference organisation, academic publication and editorial activities, including work related to IEEE conference proceedings and special issues.