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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

GazeRobotic armWorkspaceArtificial intelligenceComputer visionComputer scienceVirtual realityHuman–computer interactionRoboticsRobot

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