Sensing Technologies and Machine Learning for Cognitive and Physiological Monitoring

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:

1. Embedded systems for physiological monitoring applications.
2. AI-enhanced sensor fusion for cognitive state detection.
3. Advanced signal processing for wearable biosensors.
4. Advanced sensor technologies for brain–computer interface (BCI) systems.
5. Emerging sensors for real-time brain-computer interface applications
6. The fusion of sensor data and machine learning for real-time cognitive state assessment.
7. Energy-efficient sensor architectures for continuous health monitoring.
8. Low-power machine learning architectures for wearable physiological sensors.
9. Personalized machine learning models for physiological and cognitive monitoring.
10. Generative machine learning for cognitive and physiological monitoring.
11. Edge AI for cognitive and physiological monitoring.
12. Advanced sensor technologies for multi-modal physiological monitoring.

Dr. Carlos Valderrama
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.