ArticleScientific data2024
3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- HLM-MOCAP: A motion capture dataset of context-dependent human arm motion for human-like robot motion generation.Data in brief · 2026Article
- Infants' spontaneous movements explore arm dynamics.Communications biology · 2026Article
- Hand Motion Catalog of Human Center-Out Transport Trajectories Measured Redundantly in 3D Task-Space.Scientific data · 2025Article
- CKATool: A clinical kinematic analysis toolbox for upper limb rehabilitation.Computational and structural biotechnology journal · 2025Article
- Movement-Based Prosthesis Control with Angular Trajectory Is Getting Closer to Natural Arm Coordination.Biomimetics (Basel, Switzerland) · 2024Article
- 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality.Scientific data · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.