Objective: Despite numerous recent advances in the field of rehabilitation robotics, simultaneous, and proportional control of hand and/or wrist prostheses is still unsolved. In this work we concentrate on myocontrol of combined actions, for instance power grasping while rotating the wrist, by only using training data gathered from single actions. This is highly desirable since gathering data for all possible combined actions would be unfeasibly long and demanding for the amputee.

Approach: We first investigated physiologically feasible limits for muscle activation during combined actions. Using these limits we involved 12 intact participants and one amputee in a Target Achievement Control test, showing that tactile myography, i.e., high-density force myography, solves the problem of combined actions to a remarkable extent using simple linear regression. Since real-time usage of many sensors can be computationally demanding, we compare this approach with another one using a reduced feature set. These reduced features are obtained using a fast, spatial first-order approximation of the sensor values.

Main results: By using the training data of single actions only, i.e., power grasp or wrist movements, subjects achieved an average success rate of 70.0% in the target achievement test using ridge regression. When combining wrist actions, e.g., pronating and flexing the wrist simultaneously, similar results were obtained with an average of 68.1%. If a power grasp is added to the pool of actions, combined actions are much more difficult to achieve (36.1%).

Significance: To the best of our knowledge, for the first time, the effectiveness of tactile myography on single and combined actions is evaluated in a target achievement test. The present study includes 3 DoFs control instead of the two generally used in the literature. Additionally, we define a set of physiologically plausible muscle activation limits valid for most experiments of this kind.

客观的：尽管康复机器人领域最近取得了许多进展，但手和/或腕假肢的同步和比例控制仍然没有解决。在这项工作中，我们仅使用从单个动作收集的训练数据来专注于组合动作的肌肉控制，例如旋转手腕时的力量抓握。这是非常可取的，因为收集所有可能的组合动作的数据对于截肢者来说将是不可行的长且要求高。

方法：我们首先研究了联合动作期间肌肉激活的生理上可行的限制。利用这些限制，我们让 12 名完整的参与者和一名截肢者参与了目标达成控制测试，结果表明触觉肌动描记法（即高密度力肌动描记法）使用简单线性回归在很大程度上解决了组合动作的问题。由于许多传感器的实时使用可能对计算要求很高，因此我们将此方法与使用简化特征集的另一种方法进行比较。这些减少的特征是使用传感器值的快速空间一阶近似来获得的。

主要结果：仅使用单一动作（即力量抓握或手腕运动）的训练数据，受试者在使用岭回归的目标达成测试中获得了 70.0% 的平均成功率。当结合手腕动作时，例如同时内旋和弯曲手腕，获得了类似的结果，平均为 68.1%。如果将力量掌握添加到动作池中，则组合动作将更难以实现（36.1%）。

意义：据我们所知，首次在目标达成测试中评估了触觉肌动描记法对单一和组合动作的有效性。本研究包括 3 个自由度控制，而不是文献中通常使用的两个自由度控制。此外，我们定义了一组对大多数此类实验有效的生理上合理的肌肉激活限制。