3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality
Bianca Lento, Effie Ségas, Vincent Leconte, Emilie Doat, Frédéric Danion, Renaud Péteri, Jenny Benois‐Pineau, Aymar de Rugy
- Year
- 2024
- Citations
- 4
- Access
- Open access
Abstract
ABSTRACT 3D-ARM-Gaze is a public dataset designed to provide natural arm movements together with visual and gaze information when reaching objects in a wide reachable space from a precisely controlled, comfortably seated posture. Participants were involved in picking and placing objects in various positions and orientations in a virtual environment, whereby a specific procedure maximized the workspace explored while ensuring a consistent seated posture. The dataset regroups more than 2.5 million samples recorded from 20 healthy participants performing 14 000 single pick-and-place movements (700 per participant). While initially designed to explore novel prosthesis control strategies based on natural eye-hand and arm coordination, this dataset will also be useful to researchers interested in core sensorimotor control, humanoid robotics, human-robot interactions, as well as for the development and testing of associated solutions in gaze-guided computer vision.
Keywords
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