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Pubmed for Handhelds

PUBMED FOR HANDHELDS

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  • Title: Human adaptation to interaction forces in visuo-motor coordination.
    Author: Huang FC, Gillespie RB, Kuo AD.
    Journal: IEEE Trans Neural Syst Rehabil Eng; 2006 Sep; 14(3):390-7. PubMed ID: 17009499.
    Abstract:
    We tested whether humans can learn to sense and compensate for interaction forces in contact tasks. Many tasks, such as use of hand tools, involve significant interaction forces between hand and environment. One control strategy would be to use high hand impedance to reduce sensitivity to these forces. But an alternative would be to learn feedback compensation for the extrinsic dynamics and associated interaction forces, with the potential for lower control effort. We observed subjects as they learned control of a ball-and-beam system, a visuo-motor task where the goal was to quickly position a ball rolling atop a rotating beam, through manual rotation of the beam alone. We devised a ball-and-beam apparatus that could be operated in a real mode, where a physical ball was present; or in a virtual training mode, where the ball's dynamics were simulated in real time. The apparatus presented the same visual feedback in all cases, and optionally produced haptic feedback of the interaction forces associated with the ball's motion. Two healthy adult subject groups, vision-only and vision-haptics (each n= 10), both trained for 80 trials on the simulated system, and then were evaluated on the real system to test for skill transfer effects. If humans incorporate interaction forces in their learning, the vision-haptics group would be expected to exhibit a smoother transfer, as quantified by changes in completion time of a ball-positioning task. During training, both groups adapted well to the task, with reductions of 64%-70% in completion time. At skill transfer to the real system, the vision-only group had a significant 35% increase in completion time (p < 0.05). There was no significant change in the vision-haptics group, indicating that subjects had learned to compensate for interaction forces. These forces could potentially be incorporated in virtual environments to assist with motor training or rehabilitation.
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