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ROAG (Reaching Over A Grid): Dataset of Human Reaching Motions Over a Discretely Sampled Workspace

<p>Joint absence in people with upper limb differences leads to compensatory motions. Such compensation has long been a topic of study, but typically only for a single object/user layout, which is unlikely to generalise across a workspace.<br><br>To better understand how arm motion and compensatory movements vary over the workspace, we created the&nbsp;<strong>ROAG</strong> dataset.&nbsp;ROAG is pronounced 'Rogue' and stands for <strong>R</strong>eaching <strong>O</strong>ver <strong>A</strong> <strong>G</strong>rid. The dataset was recorded at the GRAB Lab of Yale University (USA) and has since been processed at the Manipulation and Touch Lab of Imperial College London (UK).&nbsp;<br><br>ROAG is a motion capture dataset involving arm and torso pose during reach-to-grasp actions for 49 equally spaced cylindrical targets, orientated horizontally or vertically. The data is collected from seven able-bodied participants and two transradial amputees who use prosthetic devices. In the case of able-bodied participants, different bracing systems were applied to the arm to immobilise wrist joints and simulate transradial limb loss, leading to compensatory motions. In total, the dataset consists of 2450 reaching trajectories. This resource hosts the collected dataset and the related MATLAB analysis files.<br><br>The dataset has been the basis of the following publications:</p> <ul> <li>A. J. Spiers, Y. Gloumakov and A. M. Dollar, "Transradial Amputee Reaching: Compensatory Motion Quantification Versus Unaffected Individuals Including Bracing," in&nbsp;<em>IEEE Transactions on Medical Robotics and Bionics</em>, vol. 6, no. 2, pp. 706-717, May 2024, <a href="https://doi.org/10.1109/TMRB.2024.3381339" target="_blank" rel="noopener">https://doi.org/10.1109/TMRB.2024.3381339</a></li> <li>Qihan Yang, Yuri Gloumakov, and Adam J. Spiers. "Multi-feature Compensatory Motion Analysis for Reaching Motions Over a Discretely Sampled Workspace." in <em>IEEE RAS EMBS 10th International Conference on Biomedical Robotics and Biomechatronics (BioRob 2024),</em>&nbsp;<a href="https://doi.org/10.48550/arXiv.2409.05871" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2409.05871</a></li> <li>Adam J Spiers, Yuri Gloumakov, Aaron M Dollar, "Examining the impact of wrist mobility on reaching motion compensation across a discretely sampled workspace", in IEEE&nbsp;<em>7th International Conference on Biomedical Robotics and Biomechatronics (BioRob 2018),&nbsp;</em><a href="https://doi.org/10.1109/BIOROB.2018.8487871" target="_blank" rel="noopener">https://doi.org/10.1109/BIOROB.2018.8487871</a><br><br></li> </ul>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
12
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0

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