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
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 by guiding participants to a predetermined neutral posture via visual feedback from the trunk and shoulders. These experimental settings enabled to capture natural arm movements with high median success rates (>98% objects reached) and minimal compensatory movements. 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.