Univ. Bordeaux, CNRS, INCIA, UMR 5287, Bordeaux, France.
ISIR UMR 7222, Sorbonne Université, CNRS, Inserm, Paris, France.
Elife. 2023 Oct 17;12:RP87317. doi: 10.7554/eLife.87317.
Impressive progress is being made in bionic limbs design and control. Yet, controlling the numerous joints of a prosthetic arm necessary to place the hand at a correct position and orientation to grasp objects remains challenging. Here, we designed an intuitive, movement-based prosthesis control that leverages natural arm coordination to predict distal joints missing in people with transhumeral limb loss based on proximal residual limb motion and knowledge of the movement goal. This control was validated on 29 participants, including seven with above-elbow limb loss, who picked and placed bottles in a wide range of locations in virtual reality, with median success rates over 99% and movement times identical to those of natural movements. This control also enabled 15 participants, including three with limb differences, to reach and grasp real objects with a robotic arm operated according to the same principle. Remarkably, this was achieved without any prior training, indicating that this control is intuitive and instantaneously usable. It could be used for phantom limb pain management in virtual reality, or to augment the reaching capabilities of invasive neural interfaces usually more focused on hand and grasp control.
在仿生肢体设计和控制方面正在取得令人瞩目的进展。然而，控制假肢手臂的众多关节，使其手部处于正确的位置和方向以抓取物体仍然具有挑战性。在这里，我们设计了一种直观的、基于运动的假肢控制方法，利用自然手臂协调，根据近端残肢运动和运动目标的知识，预测失去上肢的人缺失的远端关节。该控制方法在 29 名参与者（包括 7 名上肢截肢者）中进行了验证，他们在虚拟现实中以各种位置捡起和放置瓶子，成功率中位数超过 99%，运动时间与自然运动相同。该控制方法还使 15 名参与者（包括 3 名肢体差异者）能够根据相同的原理操作机器人手臂来触及和抓取真实物体。值得注意的是，这是在没有任何预先训练的情况下实现的，表明这种控制是直观的，可即时使用。它可以用于虚拟现实中的幻肢痛管理，或增强通常更专注于手和抓握控制的侵入性神经接口的可达范围。
