训练集对伸手够取目标时从肩部轨迹预测肘部轨迹的影响。

The effects of training set on prediction of elbow trajectory from shoulder trajectory during reaching to targets.

作者信息

Kaliki Rahul R, Davoodi Rahman, Loeb Gerald E

机构信息

Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90033, USA.

出版信息

Conf Proc IEEE Eng Med Biol Soc. 2006;2006:5483-6. doi: 10.1109/IEMBS.2006.260058.

DOI:10.1109/IEMBS.2006.260058

PMID:17946704

Abstract

Patients with transhumeral amputations and C5/C6 quadriplegia may be able to use voluntary shoulder motion as command signals for powered prostheses and functional electrical stimulation, respectively. Spatio-temporal synergies exist for goal oriented reaching movements between the shoulder and elbow joints in able bodied subjects. We are using a multi-layer perceptron neural network to discover and embody these synergies. Such a network could be used as a high level functional electrical stimulation (FES) controller that could predict elbow joint kinematics from the voluntary movements of the shoulder joint. Counter-intuitively, a well-chosen reduced data set for training the network resulted in better performance than use of the whole data set against which the predictions of the network were evaluated.

摘要

经肱骨截肢患者和C5/C6四肢瘫痪患者或许能够分别将自主肩部运动用作动力假肢和功能性电刺激的指令信号。在健全受试者中，肩肘之间的目标导向性够物运动存在时空协同作用。我们正在使用多层感知器神经网络来发现并体现这些协同作用。这样的网络可用作高级功能性电刺激（FES）控制器，它能够根据肩关节的自主运动预测肘关节运动学。与直觉相反的是，精心挑选的用于训练网络的精简数据集比使用用于评估网络预测的完整数据集能产生更好的性能。