Andrea Tigrini

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About

Andrea Tigrini is a leading researcher in biomedical signal processing and human–machine interaction, with a primary focus on the analysis of surface electromyography (sEMG) for advanced hand gesture recognition. Her most cited work, “On the Use of Fuzzy and Permutation Entropy in Hand Gesture Characterization from EMG Signals,” has garnered 24 citations and represents a pivotal contribution to the field. In this study, Tigrini systematically investigated the application of fuzzy entropy (FEn) and permutation entropy (PEn) as complexity measures for sEMG-based gesture characterization, providing critical insights into parameter selection and comparative performance. Her research addresses a fundamental challenge in prosthetics and rehabilitation technology: improving the reliability and robustness of gesture classification by optimizing feature extraction from noisy, non-stationary biosignals. By demonstrating how entropy-based features can enhance the discrimination of subtle hand movements, Tigrini’s work has significant implications for the development of more intuitive and responsive myoelectric control systems. Her contributions are widely recognized for bridging theoretical signal processing with practical applications in assistive technology, making her a key figure in the advancement of non-invasive neural interfaces.