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.

目的：尽管在康复机器人技术领域中有许多最新进展，但是手和/或腕关节假体的同时和比例控制仍未解决。在这项工作中，我们仅通过使用从单个动作中收集到的训练数据，专注于组合动作的肌力控制，例如在旋转腕部时掌握力量。这是非常可取的，因为为所有可能的联合行动收集数据的时间过长，对截肢者的要求很高。

方法：我们首先研究了在联合动作过程中肌肉激活的生理可行极限。使用这些限制，我们在目标成就控制测试中涉及12名完整参与者和1名截肢者，这表明触觉肌成像，即高密度力肌成像，可以通过简单的线性回归极大地解决了联合动作的问题。由于许多传感器的实时使用可能需要计算，因此我们将此方法与使用简化功能集的另一方法进行了比较。这些缩小的特征是使用传感器值的快速，空间一阶近似获得的。

主要结果：通过仅使用单个动作（即力量掌握或腕部动作）的训练数据，受试者就可以使用岭回归在目标成就测试中获得70.0％的平均成功率。当结合腕部动作（例如同时使腕部俯卧和弯曲）时，获得了相似的结果，平均值为68.1％。如果将权力掌握添加到行动池中，则联合行动很难实现（36.1％）。

意义：据我们所知，首次在目标成就测试中评估了触觉肌腱对单个动作和组合动作的有效性。本研究包括3个DoF控件，而不是文献中通常使用的两个。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极限。