Vejpustková J, Vilímek M, Sochor M
Department of Mechanics, Faculty of Mechanical Engineering, CTU in Prague, Technická 4, 16607 Prague 6, Czech Republic.
Technol Health Care. 2006;14(4-5):215-8.
Today artificial neural networks can be trained to solve problems that are difficult for conventional computers or human beings. The big advantage of an artificial neural network is results obtained without knowledge of the algorithm procedure or without full and exact information. Therefore an artificial neural network was used to predict the muscle forces. The aim of the study was to simplify prediction of muscle forces which are difficult to determine, because many muscles act cooperatively. However, orthopeadists, biomechanical engineers and physical therapists need to take muscle forces into consideration because joint contact forces, as well as muscle forces, need to be estimated in order to understand the joint and bone loading. In terms of sensitivity of the muscle parameters to the results from the proposed neural network object, the muscle force prediction was simplified.
如今,可以训练人工神经网络来解决传统计算机或人类难以解决的问题。人工神经网络的一大优势在于,无需了解算法过程或完整准确的信息就能获得结果。因此,使用人工神经网络来预测肌肉力量。该研究的目的是简化难以确定的肌肉力量的预测,因为许多肌肉协同作用。然而,骨科医生、生物力学工程师和物理治疗师需要考虑肌肉力量,因为为了了解关节和骨骼负荷,需要估计关节接触力以及肌肉力量。就肌肉参数对所提出的神经网络对象的结果的敏感性而言,肌肉力量预测得到了简化。