Bianca Lento
Papers
2
Total Citations
14
H-Index
2
About
Bianca Lento is a rising researcher at the intersection of human movement science and virtual reality, with a core focus on understanding natural reaching behaviors through multimodal data. Her most significant contribution is the creation of the **3D-ARM-Gaze dataset**, a publicly available resource that captures 3D arm reaching movements synchronized with gaze information in immersive VR environments. This dataset, which has already garnered over 10 citations since its 2024 release, provides precisely controlled recordings of seated participants picking and placing objects across a wide reachable space. By integrating visual attention data with kinematic arm trajectories, Lento’s work enables researchers to study how gaze guides natural motor planning and execution—a critical step for advancing rehabilitation robotics, human-computer interaction, and assistive technologies. Her achievement lies not only in the technical rigor of the data collection but also in making this resource openly available to the scientific community, fostering reproducibility and collaboration. As an early-career scholar, Lento’s dataset is already shaping how we model the coordination between vision and action in ecological VR settings, marking her as a promising voice in embodied cognition and motor control research.
Research Focus
Key Achievements
Top Papers
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- 2