Sensing Technologies and Machine Learning for Cognitive and Physiological Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 10 May 2026 | Viewed by 20
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue of Sensors will focus on the integration of cutting-edge sensor technologies and machine learning (ML) for advancing cognitive and physiological monitoring. It will bring together innovative research and applications that leverage embedded systems, wearable biosensors, and state-of-the-art signal processing techniques to enable real-time, accurate, and personalized health monitoring.
This issue will highlight the role of AI-enhanced sensor fusion in detecting cognitive states and explore emerging sensor technologies for brain–computer interface (BCI) systems, including real-time applications. It will also address the development of energy-efficient sensor architectures and low-power ML models for continuous, wearable physiological monitoring. Additionally, the issue will cover personalized machine learning approaches tailored to individual cognitive and physiological profiles, as well as the use of generative ML and Edge AI for elevated monitoring capabilities.
By showcasing advancements in multi-modal sensor technologies and the fusion of sensor data with ML for real-time cognitive assessment, this Special Issue will provide a comprehensive overview of the latest developments in sensor-driven, AI-powered solutions for healthcare and human–machine interaction. It will serve as a valuable resource for researchers and practitioners working at the intersection of sensor technology, artificial intelligence, and health monitoring.
Topics:
- Embedded systems for physiological monitoring applications.
- AI-enhanced sensor fusion for cognitive state detection.
- Advanced signal processing for wearable biosensors.
- Advanced sensor technologies for brain–computer interface (BCI) systems.
- Emerging sensors for real-time brain-computer interface applications
- The fusion of sensor data and machine learning for real-time cognitive state assessment.
- Energy-efficient sensor architectures for continuous health monitoring.
- Low-power machine learning architectures for wearable physiological sensors.
- Personalized machine learning models for physiological and cognitive monitoring.
- Generative machine learning for cognitive and physiological monitoring.
- Edge AI for cognitive and physiological monitoring.
- Advanced sensor technologies for multi-modal physiological monitoring.
Dr. Carlos Valderrama
Guest Editor
Manuscript Submission Information
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Keywords
- AI-enhanced sensor fusion
- wearable biosensors for cognitive monitoring
- Edge AI for real-time health monitoring
- brain–computer interface (BCI) sensors
- personalized machine learning in healthcare
- energy-efficient sensor architectures
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