Segas Effie, Leconte Vincent, Doat Emilie, Cattaert Daniel, de Rugy Aymar
University of Bordeaux, CNRS, INCIA, UMR, 5287 Bordeaux, France.
Biomimetics (Basel). 2024 Sep 4;9(9):532. doi: 10.3390/biomimetics9090532.
Traditional myoelectric controls of trans-humeral prostheses fail to provide intuitive coordination of the necessary degrees of freedom. We previously showed that by using artificial neural network predictions to reconstruct distal joints, based on the shoulder posture and movement goals (i.e., position and orientation of the targeted object), participants were able to position and orient an avatar hand to grasp objects with natural arm performances. However, this control involved rapid and unintended prosthesis movements at each modification of the movement goal, impractical for real-life scenarios. Here, we eliminate this abrupt change using novel methods based on an angular trajectory, determined from the speed of stump movement and the gap between the current and the 'goal' distal configurations. These new controls are tested offline and online (i.e., involving participants-in-the-loop) and compared to performances obtained with a natural control. Despite a slight increase in movement time, the new controls allowed twelve valid participants and six participants with trans-humeral limb loss to reach objects at various positions and orientations without prior training. Furthermore, no usability or workload degradation was perceived by participants with upper limb disabilities. The good performances achieved highlight the potential acceptability and effectiveness of those controls for our target population.
传统的经肱骨假肢肌电控制无法实现对必要自由度的直观协调。我们之前表明，通过使用人工神经网络预测，基于肩部姿势和运动目标（即目标物体的位置和方向）来重建远端关节，参与者能够以自然的手臂动作定位和定向虚拟手来抓取物体。然而，这种控制在每次运动目标改变时都会导致假肢快速且意外的移动，在现实场景中不实用。在此，我们基于由残肢运动速度以及当前与“目标”远端构型之间的差距所确定的角轨迹，使用新方法消除了这种突然变化。这些新控制方法经过离线和在线测试（即让参与者参与其中），并与自然控制下的表现进行比较。尽管运动时间略有增加，但新控制方法使12名健全参与者和6名经肱骨截肢参与者无需预先训练就能在不同位置和方向抓取物体。此外，上肢残疾参与者并未感觉到可用性或工作量有所下降。所取得的良好表现突出了这些控制方法对于我们目标人群的潜在可接受性和有效性。
