基于FGNN算法的sEMG肌肉疲劳分类方法

Muscle Fatigue Classification Method of sEMG Signal Based on FGNN Algorithm

为了防止果蝇优化算法的局部最优约束,提高肌肉疲劳分类的准确率,本研究提出了一种基于肌电信号的肌肉疲劳分类方法:果蝇-遗传优化算法,实现了肌肉疲劳的准确检测和分类。在改进的果蝇优化算法基础上把遗传算法的交叉变异和果蝇优化算法混合,并与神经网络结合对肌肉疲劳进行识别。相较于果蝇优化算法,改进后的算法有更强的跳出局部最优的能力。与神经网络结合后对于疲劳状态识别具有更好的效果。本研究提出的肌肉疲劳分类方法,可以防止运动员过度疲劳引起的肌肉损伤,实现准确的肌肉疲劳检测和分类。一共招募了10名健康的年轻参与者(6名男性和4名女性)进行疲劳测试。首先根据主观评测法对疲劳等级进行划分。然后将采集到的肌电信号数据进行预处理、提取特征后作为神经网络,遗传算法-神经网络,果蝇优化算法-神经网络,果蝇-遗传算法-神经网络的输入。经比较果蝇-遗传优化算法-神经网络的准确率为94.3%,优于其他方法。

In order to prevent the local optimal constraints of the fruit fly optimization algorithm and improve the accuracy of muscle fatigue classification,a muscle fatigue classification method on EMG signals was proposed.Fruit fly-genetic optimization algorithm,which realized the accurate detection and classification of muscle fatigue.On the basis of the improved fruit fly optimization algorithm,the step cross and mutation of the genetic algorithm and the fruit fly optimization algorithm concentration were mixed,and combined with the neural network to identify muscle fatigue.Compared with the fruit fly optimization algorithm,the improved algorithm had a stronger ability to jump out of the local optimum and maintain vitality.Combined with the neural network,it had a better effect on fatigue state recognition.The muscle fatigue classification method proposed could prevent muscle damage caused by athletes’fatigue and achieve accurate muscle fatigue detection and classification.A total of 10 healthy young participants(6 men and 4 women)were recruited for fatigue testing.First,the fatigue level was divided according to the subjective evaluation method.Then,the collected EMG signal data was preprocessed and features were extracted as the input of neural network,the genetic algorithm-neural network,the fruit fly optimization algorithm-neural network,fruit fly-genetic algorithm-neural network.By comparison,the accuracy rate of fruit fly-genetic optimization algorithm-neural network was 94.3%,which is better than other methods.