Joefrinaldo

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Total Citations

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About

Joefrinaldo is a researcher at the forefront of biomedical engineering and human-robot interaction, with a primary focus on electromyography (EMG)-based control systems. His most cited work, "Robot mobile control based on three EMG signals using an artificial neural network" (2019), introduces an innovative approach to assistive robotics by harnessing EMG signals from three distinct muscle groups—the jaw, right arm, and left arm. By extracting peak values from these muscle contractions and feeding them into an artificial neural network, Joefrinaldo demonstrated a reliable method for translating human physiological signals into precise robot navigation commands. This contribution holds significant promise for developing intuitive, hands-free control interfaces for individuals with motor impairments. Though his citation count is currently modest at 1, the foundational nature of his work positions it as a stepping stone for future advances in non-invasive neural interfaces. Joefrinaldo’s research bridges the gap between human biology and machine intelligence, offering a glimpse into a future where assistive technologies respond seamlessly to the body’s natural signals. His dedication to this niche yet impactful field underscores his potential to shape the next generation of adaptive robotic systems.