Stefano Cardarelli

Papers

Total Citations

H-Index

About

Stefano Cardarelli is a leading researcher in biomedical signal processing, with a primary focus on the analysis of surface electromyography (sEMG) for advanced human-machine interaction. His work centers on developing robust methodologies for hand gesture characterization, a critical area for prosthetics and rehabilitation technologies. Cardarelli’s major contribution lies in systematically investigating and comparing complexity measures—specifically fuzzy entropy (FEn) and permutation entropy (PEn)—to enhance the reliability of gesture recognition from sEMG signals. His most-cited paper (2020, 24 citations) provides a rigorous framework for parameter selection in these entropy-based features, demonstrating how careful tuning can significantly improve classification accuracy. This foundational study has influenced subsequent research in non-linear signal analysis for biomedical applications. By bridging the gap between theoretical complexity metrics and practical implementation, Cardarelli’s work supports the development of more intuitive and responsive prosthetic control systems. His research continues to shape how entropy-based features are optimized for real-world biomedical engineering challenges, making him a notable figure in the field of biosignal processing and rehabilitation technology.