2022
Allemang-Trivalle, Arnaud; Eden, Jonathan; Huang, Yanpei; Ivanova, Ekaterina; Burdet, Etienne
Comparison of human trimanual performance between independent and dependent multiple-limb training modes Proceedings Article
In: 2022 9th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), pp. 1–6, 2022, (ISSN: 2155-1782).
Abstract | Links | BibTeX | Tags: Biomechatronics, Dynamics, Navigation, Robot kinematics, Task analysis, Training, Virtual reality
@inproceedings{allemang-trivalle_comparison_2022,
title = {Comparison of human trimanual performance between independent and dependent multiple-limb training modes},
author = { Arnaud Allemang-Trivalle and Jonathan Eden and Yanpei Huang and Ekaterina Ivanova and Etienne Burdet},
doi = {10.1109/BioRob52689.2022.9925417},
year = {2022},
date = {2022-08-01},
booktitle = {2022 9th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)},
pages = {1–6},
abstract = {Human movement augmentation with a third robotic hand can extend human capability allowing a single user to perform three-hand tasks that would typically require cooperation with other people. However, as trimanual control is not typical in everyday activities, it is still unknown how to train people to acquire this capability efficiently. We conducted an experimental study to evaluate two different trimanual training modes with 24 subjects. This investigated how the different modes impact the transfer of learning of the acquired trimanual capability to another task. Two groups of twelve subjects were each trained in virtual reality for five weeks using either independent or dependent trimanual task repetitions. The training was evaluated by comparing performance before and after training in a gamified trimanual task. The results show that both groups of subjects improved their trimanual capabilities after training. However, this improvement appeared independent of training scheme.},
note = {ISSN: 2155-1782},
keywords = {Biomechatronics, Dynamics, Navigation, Robot kinematics, Task analysis, Training, Virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Human movement augmentation with a third robotic hand can extend human capability allowing a single user to perform three-hand tasks that would typically require cooperation with other people. However, as trimanual control is not typical in everyday activities, it is still unknown how to train people to acquire this capability efficiently. We conducted an experimental study to evaluate two different trimanual training modes with 24 subjects. This investigated how the different modes impact the transfer of learning of the acquired trimanual capability to another task. Two groups of twelve subjects were each trained in virtual reality for five weeks using either independent or dependent trimanual task repetitions. The training was evaluated by comparing performance before and after training in a gamified trimanual task. The results show that both groups of subjects improved their trimanual capabilities after training. However, this improvement appeared independent of training scheme.