Adaptive Control and Signal Processing for Biomedical and Intelligent Systems

Dear Colleagues,

In recent years, adaptive control and advanced signal processing have become key enabling technologies for modern biomedical and intelligent systems, including wearable sensors, brain–computer interfaces, medical robots, and autonomous assistive devices. These systems operate in highly variable and uncertain environments, where patient-specific characteristics, changing operating conditions, and complex physiological dynamics require controllers and algorithms that can adjust their behaviour in real time. Adaptive methods that integrate domain knowledge with data-driven models are therefore essential to ensure robustness, safety, and high performance in demanding clinical and engineering scenarios.

This Special Issue aims to gather high-quality contributions on adaptive control strategies and signal processing methods that address the challenges of modelling, estimation, and decision-making in biomedical and intelligent systems. We particularly welcome works that demonstrate how adaptive algorithms can enhance reliability, interpretability, and real-time performance in applications such as monitoring, diagnosis, therapy, rehabilitation, and human–machine interaction, in line with the applied, interdisciplinary scope of Applied Sciences.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following: adaptive control schemes for biomedical devices and therapeutic systems; adaptive and learning-based signal processing for physiological signals (e.g., EEG, ECG, EMG, PPG, and multimodal biosignals); brain–computer interfaces and neuroengineering applications; adaptive filtering, artifact reduction, and feature extraction for clinical and wearable monitoring; intelligent sensing and decision-support systems in healthcare; adaptive and robust control in medical and rehabilitation robotics; real-time implementation of adaptive algorithms on embedded platforms and edge devices; and machine learning–based adaptive methods for intelligent cyber–physical and human-in-the-loop systems.

We look forward to receiving your contributions.

Dr. Krzysztof Hryniów
Dr. Grzegorz Sarwas
Guest Editors

Manuscript Submission Information

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