Papers
18
Total Citations
324
H-Index
6
About
Virginia Ruiz Garate is a robotics researcher whose work spans wearable robotics, bioinspired control systems, human-robot interaction, and robotic manipulation. She has made significant contributions to the field of assistive technology, most notably through her highly cited development of an oscillator-based framework for real-time gait phase estimation in wearable robots, which has accumulated 137 citations and represents a foundational tool for lower-limb exoskeleton control. Complementing this, her bioinspired locomotion framework leveraging motor primitives for cooperative exoskeleton assistance demonstrates her ability to translate biological principles into practical rehabilitation engineering solutions. Ruiz Garate has also advanced robotic hand control, pioneering grasp stiffness regulation techniques inspired by human motor behavior, including work on common-mode and configuration-dependent stiffness that bridges neuroscience and engineering. Her research portfolio extends into multi-robot teleoperation, shared autonomy for mobile platforms, and clinical applications such as a transformer-based robotic system for hospital delirium detection, reflecting a growing interest in healthcare robotics. More recently, she has explored machine learning approaches to predict movement trajectories in stroke patients, pointing toward adaptive rehabilitation systems. With over 290 total citations, her interdisciplinary research consistently bridges human motor science, assistive technology, and intelligent robotic control.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3A Scalable Framework for Multi-Robot Tele-Impedance Control32 citations · 2021
- 4
- 5A Bio-inspired Grasp Stiffness Control for Robotic Hands10 citations · 2018
- 6
- 7Transformers and Human-robot Interaction for Delirium Detection6 citations · 2023
- 8
- 9
- 10A Probabilistic Shared-Control Framework for Mobile Robots5 citations · 2020