Estimación en la Intención de Agarres: Cilíndrico, Esférico y Gancho Utilizando Redes Neuronales Profundas
Estimación en la Intención de Agarres: Cilíndrico, Esférico y Gancho Utilizando Redes Neuronales Profundas
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评估 Agarres 的意图：Cilíndrico、Esférico 和 Gancho Utilizando Redes Neuronales Profundas
DOI：
10.17488/rmib.41.1.9
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
A. I. Botello
中科院分区：
文献类型：
--
作者：
L. H. Rascón;M. A. Sinecio;J. Mejía;J. D. Díaz;I. Canales;A. I. Botello
上肢截肢可使截肢者产生不同程度的残疾，尤其是在积极的工作生活中。因此，出于这个原因，它是社会重要性的研究假肢和算法，帮助更好地控制这些由用户。在这项研究中，我们提出了一种基于递归神经网络的架构，称为长短期记忆，以及用于肌电信号分类的卷积神经网络，并应用于手部假肢控制。建议的网络分类三种类型的运动由手：圆柱形，球形和钩握。该模型的效率（准确度）为89%，而基于完全连接层的人工神经网络在预测手部动作时的效率仅为80%。目前的工作仅限于评估与肌电输入的网络，手假肢的控制系统没有实现。因此，用于控制可以用受试者的信号训练的手部假体的卷积网络的架构。
Upper extremities amputations can produce different disability degrees in the amputated person, this is acerbated even more, when it happens during active working life. So, for this reason, it is of social importance the study of prostheses and algorithms that help a better control of these by the user. In this research, we propose an architecture based on recurrent neural networks, called Long Short-Term Memory, and convolutional neural networks for classification of electromyographic signals, with applications for hand prosthesis control. The proposed network classifies three types of movements made by the hand: cylindrical, spherical and hook grips. The proposed model showed an efficiency (accuracy) of 89%, in contrast to an artificial neural network based on completely connected layers that only obtained an efficiency of 80% in the prediction of the hand movements. The present work is limited to evaluate the network with an electromyogram input, the control system for hand prosthesis was not implemented. Thus, an architecture of convolutional networks for the control of hand prostheses that can be trained with the signals of the subject.