Voluntary human motion is the product of muscle activity that results from upstream motion planning of the motor cortical areas. We show that muscle activity can be artificially generated based on motion features such as position, velocity, and acceleration. For this purpose, we specifically develop an approach based on recurrent neural network that is trained in a supervised learning session; additional neural network architectures are considered and evaluated. The performance is evaluated by a new score called the zero-line score. The latter adaptively rescales the loss function of the generated signal for all channels comparing the overall range of muscle activity and thus dynamically evaluates similarities between both signals. The model achieves remarkable precision for previously trained movements and maintains significantly high precision for new movements that have not been previously trained. Further, these models are trained on multiple subjects and thus are able to generalize across individuals. In addition, we distinguish between a general model that has been trained on several subjects, a subject-specific model, and a specific pre-trained model that uses the general model as a basis and is adapted to a specific subject afterward. The subject-specific generation of muscle activity can be further used to improve the rehabilitation of neuromuscular diseases with myoelectric prostheses and functional electric stimulation.
翻译:人体自愿运动是运动皮层区域上游运动规划产生的肌肉活动的产物。我们显示肌肉活动可以根据运动特征,如位置、速度和加速度等,人为地产生肌肉活动。为此,我们专门开发一种基于经常性神经网络的方法,在监督的学习课程中培训;考虑和评估更多的神经网络结构。性能由称为零线分的新分来评价。后一种通过适应性调整,重新测定所有频道生成信号的损失功能,以比较肌肉活动的总体范围,从而动态地评估两种信号之间的相似性。该模型对以前训练过的运动具有惊人的精确性,对以前没有训练过的新运动保持很高的精确性。此外,这些模型接受过多种科目的培训,从而能够对个人进行普及。此外,我们区分了一种在几个主题上受过培训的一般模型,即一个特定主题模型,以及一种使用一般模型作为基础并随后适应特定主题的具体的预先训练模型。可进一步使用特定对象的肌肉活动组来改进神经功能性神经病的恢复,以及用我制的电动器进行。