Leslie Castelino
Papers
1
Total Citations
18
H-Index
1
About
Leslie Castelino is a researcher whose work sits at the intersection of biomechanics, neural control, and human-machine interaction. Her primary research focuses on understanding and modeling the complex, multivariable dynamics of the human ankle—a critical joint for balance and locomotion. In her most-cited work, she developed a novel method to estimate ankle impedance in two degrees of freedom (dorsi-plantarflexion and inversion-eversion) by fusing electromyographic (EMG) signals with artificial neural networks. This contribution is significant because it moves beyond traditional single-axis models, offering a more realistic, data-driven framework for characterizing how the neuromuscular system adapts joint stiffness in real time. With 18 citations, this paper has provided a foundational tool for researchers in rehabilitation robotics, prosthetic design, and exoskeleton control, enabling more intuitive and responsive assistive devices. Castelino's work demonstrates a keen ability to bridge physiological signal processing with machine learning, paving the way for smarter, human-aware technologies that can interpret and augment natural movement.
Research Focus
Key Achievements
Top Papers
- 1