Abstract
Gazing at the target is a necessary part of human-machine interaction, providing the basic scene information, such as watching videos, handling documents, manufacturing and assembling workpieces. In these situations, for accomplishing a concurrent-task (such as getting a drink, taking a file, picking up a tool), a peripheral-visual-guided grasp is needed. Compared with the natural grasp without visual restrictions, the peripheral-visual-guiding will limit the grasp performance. However, few studies focus on the quantitative investigation of human concurrent-task operation ability. In this paper, we built a general concurrent-task scene: peripheral-visual-guided grasp under gazing at a target. Ten voluntary participants were required to keep their eyes gazing at the screen target and accomplish the concurrent grasp task in three restrictions: (1) keep both head and trunk still, (2) keep trunk still while the head can turn, (3) both trunk and head can turn. The maximum range of object placement able to accomplish accurate grasp, is defined as the workspace in each restriction. The rotation angle of head and trunk, and grasped object position, were collected using IMU and VICON. Three precision grasp workspace were obtained within the range of 44.631 \(\pm \) 1.348º, 67.315 \(\pm \) 2.075º, and 80.835 \(\pm \) 4.360º, corresponding to each restriction. Besides, the workspace division results were further verified by the head and trunk peak rotation angles. The results can be applied to provide a design guideline of assistive devices to enhance human concurrent-task operation ability.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (51905375), the China Post-doctoral Science Foundation Funded Project (2019M651033), Foundation of State Key Laboratory of Robotics and System (HIT) (SKLRS-2019-KF-06), and Peiyang Elite Scholar Program of Tianjin University (2020XRG-0023).
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Liu, Y., Zhang, W., Zeng, B., Zhang, K., Cheng, Q., Ming, D. (2021). The Analysis of Concurrent-Task Operation Ability: Peripheral-Visual-Guided Grasp Performance Under the Gaze. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13014. Springer, Cham. https://doi.org/10.1007/978-3-030-89098-8_47
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DOI: https://doi.org/10.1007/978-3-030-89098-8_47
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