Alessandro Mengarelli

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About

Alessandro Mengarelli’s research lies at the intersection of biomedical signal processing, human–machine interaction, and rehabilitation robotics, with a particular focus on surface electromyography (sEMG). His work has advanced the characterization of muscle activity for intuitive control of assistive devices, such as exoskeletons. One of his most cited contributions, a 2020 study on hand gesture recognition, systematically compared fuzzy entropy and permutation entropy for sEMG-based gesture classification—demonstrating how careful parameter selection can significantly improve reliability (24 citations). Mengarelli has also been a pioneer in understanding how gender influences myoelectric signals, conducting two complementary studies on shank and thigh muscles (2014). These works revealed that sEMG patterns vary substantially between male and female subjects, a critical insight for designing inclusive, subject-adaptive robotic controllers. By highlighting these physiological differences, his research underscores the need for personalized calibration in assistive technologies. With a growing citation footprint, Mengarelli’s work continues to inform both the engineering of robust biosignal interfaces and the broader effort to make human–robot collaboration more responsive to individual variability.