David Leserri
Papers
1
Total Citations
5
H-Index
1
About
David Leserri is a researcher at the forefront of human-robot interaction, specializing in the use of surface electromyography (sEMG) for intuitive control of wearable robotic systems. His work focuses on decoding neural intent to enable seamless control of active orthoses and exoskeletons, with a particular emphasis on limb movement prediction. In his highly cited 2022 study, Leserri conducted a rigorous evaluation of sEMG signal features and segmentation parameters, demonstrating how feedforward neural networks can effectively predict limb movements by capturing early information about motion onset and execution. This foundational research has garnered significant attention, with 5 citations, and is critical for advancing non-invasive, intuitive interfaces that allow users to control assistive devices naturally. Leserri’s contributions are paving the way for more responsive and adaptive wearable robots, directly impacting rehabilitation engineering and assistive technology. His work stands out for its systematic approach to optimizing signal processing and machine learning parameters, offering a blueprint for future developments in neural-machine interfaces.
Research Focus
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Top Papers
- 1