Myoelectric upper limb prostheses are controlled using information from the electrical activity of residual muscles (i.e. the electromyogram, EMG). EMG patterns at the onset of a contraction (transient phase) have shown predictive information about upcoming grasps. However, decoding this information for the estimation of the grasp force was so far overlooked. In a previous offline study, we proved that the transient phase of the EMG indeed contains information about the grasp force and determined the best algorithm to extract this information. Here we translated those findings into an online platform to be tested with both non-amputees and amputees. The platform was tested during a pick and lift task (tri-digital grasp) with light objects (200 g – 1 kg), for which fine control of the grasp force is more important. Results show that, during this task, it is possible to estimate the target grasp force with an absolute error of 2.06 (1.32) % and 2.04 (0.49) % the maximum voluntary force for non-amputee and amputees, respectively, using information from the transient phase of the EMG. This approach would allow for a biomimetic regulation of the grasp force of a prosthetic hand. Indeed, the users could contract their muscles only once before the grasp begins with no need to modulate the grasp force for the whole duration of the grasp, as required with continuous classifiers. These results pave the way to fast, intuitive and robust myoelectric controllers of limb prostheses.

中文翻译：

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瞬态肌电图在线抓力估算

使用来自残余肌肉电活动的信息（即肌电图，EMG）来控制肌电上肢假体。收缩开始（过渡期）时的EMG模式显示了有关即将到来的抓紧力的预测信息。但是，到目前为止，为了估计抓握力而解码该信息被忽略了。在先前的离线研究中，我们证明了EMG的过渡阶段确实包含有关抓力的信息，并确定了提取该信息的最佳算法。在这里，我们将这些发现转换成在线平台，供非截肢者和被截肢者测试。在轻重（200 g – 1 kg）的拾取和提升任务（三指抓取）中对平台进行了测试，对于抓紧力的精细控制更为重要。结果表明，在此任务期间，可以使用来自非被截肢者和被截肢者的最大自愿力，分别以绝对误差2.06（1.32）％和2.04（0.49）％的绝对误差估算目标抓握力。 EMG。这种方法将允许仿生调节假手的抓握力。实际上，使用者可以在抓握开始之前仅使肌肉收缩一次，而无需按照连续分类器的要求在抓握的整个过程中调节抓握力。这些结果为快速，直观和强大的肢体假体肌电控制器铺平了道路。使用来自EMG过渡阶段的信息。这种方法将允许仿生调节假手的抓握力。实际上，使用者可以在抓握开始之前仅使肌肉收缩一次，而无需按照连续分类器的要求在抓握的整个过程中调节抓握力。这些结果为快速，直观和强大的肢体假体肌电控制器铺平了道路。使用来自EMG过渡阶段的信息。这种方法将允许仿生调节假手的抓握力。实际上，使用者可以在抓握开始之前仅使肌肉收缩一次，而无需按照连续分类器的要求在抓握的整个过程中调节抓握力。这些结果为快速，直观和强大的肢体假体肌电控制器铺平了道路。