Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences, University of Messina, Messina, ME, Italy.
Department of Biomedical, Dental, Morphological and Functional Imaging Sciences, University of Messina, Messina, ME, Italy.
J Neural Eng. 2022 Jan 24;19(1). doi: 10.1088/1741-2552/ac47db.
Muscle activation patterns in the muscle-to-force null space, i.e. patterns that do not generate task-relevant forces, may provide an opportunity for motor augmentation by allowing to control additional end-effectors simultaneously to natural limbs. Here we tested the feasibility of muscular null space control for augmentation by assessing simultaneous control of natural and extra degrees of freedom.We instructed eight participants to control translation and rotation of a virtual 3D end-effector by simultaneous generation of isometric force at the hand and null space activity extracted in real-time from the electromyographic signals recorded from 15 shoulder and arm muscles. First, we identified the null space components that each participant could control more naturally by voluntary co-contraction. Then, participants performed several blocks of a reaching and holding task. They displaced an ellipsoidal cursor to reach one of nine targets by generating force, and simultaneously rotated the cursor to match the target orientation by activating null space components. We developed an information-theoretic metric, an index of difficulty defined as the sum of a spatial and a temporal term, to assess individual null space control ability for both reaching and holding.On average, participants could reach the targets in most trials already in the first block (72%) and they improved with practice (maximum 93%) but holding performance remained lower (maximum 43%). As there was a high inter-individual variability in performance, we performed a simulation with different spatial and temporal task conditions to estimate those for which each individual participants would have performed best.Muscular null space control is feasible and may be used to control additional virtual or robotics end-effectors. However, decoding of motor commands must be optimized according to individual null space control ability.
肌肉在力零空间中的激活模式，即不会产生与任务相关的力的模式，可能为运动增强提供机会，允许同时控制额外的末端效应器和自然肢体。在这里，我们通过评估同时控制自然和额外自由度的能力来测试肌肉零空间控制增强的可行性。我们要求八名参与者通过在手处同时产生等长力并从记录的 15 个肩部和手臂肌肉的肌电图信号中实时提取零空间活动，来控制虚拟 3D 末端效应器的平移和旋转。首先，我们通过自愿共收缩确定每个参与者可以更自然地控制的零空间分量。然后，参与者执行了几个到达和保持任务的块。他们通过生成力来使椭圆形光标移位以到达九个目标中的一个，并通过激活零空间分量同时旋转光标以匹配目标方向。我们开发了一种信息论度量，即难度指数，定义为空间项和时间项的总和，以评估个人在到达和保持方面的零空间控制能力。平均而言，参与者在大多数试验中已经能够在第一个块中到达目标（72%），并且他们随着练习而提高（最高 93%），但保持性能仍然较低（最高 43%）。由于表现存在很大的个体差异，我们进行了模拟，使用不同的空间和时间任务条件来估计每个参与者表现最佳的条件。肌肉零空间控制是可行的，可以用于控制额外的虚拟或机器人末端效应器。然而，运动指令的解码必须根据个人的零空间控制能力进行优化。
