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首页> 外文期刊>Journal of Neuroscience Methods >Prediction of dynamic tendon forces from electromyographic signals: an artificial neural network approach.
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Prediction of dynamic tendon forces from electromyographic signals: an artificial neural network approach.

机译：从肌电信号预测动态肌腱力：一种人工神经网络方法。

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Artificial neural networks (ANN) with a backpropagation algorithm were used to predict dynamic tendon forces from electromyographic (EMG) signals. To achieve this goal, tendon forces and EMG-signals were recorded simultaneously in the gastrocnemius muscle of three cats while walking and trotting at different speeds on a motor-driven treadmill. The quality of the tendon force predictions were evaluated for three levels of generalization. First, at the intrasession level, tendon force predictions were made for step cycles from the same experimental session as the step cycles which were used to train the ANN. At this level of generalization very good results were obtained. Second, at the intrasubject level, tendon force predictions were made for one cat walking at a given speed while the ANN was trained with data from the same animal walking at different speeds. For the intrasubject predictions, the quality of the results depended on the walking speed for which the predictions were made: for the speeds atthe low and high extremes, the predictions were worse than for the intermediate speeds. The cross-correlation coefficients between predicted and actual force time histories ranged from 0.78 to 0.91. Third, at the intersubject level, tendon forces were predicted for one animal walking at a given speed while the ANN was trained with data from the remaining two animals walking at the corresponding speed. The cross-correlation coefficients between predicted and actual force time histories ranged from 0.72 to 0.98. It was concluded that the ANN-approach is a powerful technique to predict dynamic tendon forces from EMG-signals.
机译：具有反向传播算法的人工神经网络（ANN）用于根据肌电图（EMG）信号预测动态肌腱力。为了实现此目标，在三只猫的腓肠肌中，同时在电动跑步机上以不同速度行走和小跑时，同时记录了肌腱力和EMG信号。对肌腱力预测的质量进行了三个概括级别的评估。首先，在训练过程中，从与训练ANN的步骤循环相同的实验步骤中对步骤循环进行肌腱力预测。在此概括级别上，可以获得很好的结果。其次，在受试者内的水平上，对以给定速度行走的一只猫进行了肌腱力预测，而对ANN进行了以同一动物以不同速度行走的数据进行了训练。对于受试者内部的预测，结果的质量取决于做出预测的步行速度：对于低端和高端的速度，预测要比中等速度差。预计和实际作用力时间历史之间的互相关系数在0.78至0.91之间。第三，在受试者间水平上，预测了以给定速度行走的一只动物的肌腱力，而使用来自其余两只动物以相应速度行走的数据训练了ANN。预计和实际作用力时间历史之间的互相关系数在0.72至0.98之间。结论是，ANN方法是一种从EMG信号预测动态肌腱力的强大技术。

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