Abstract：
With the accuracy and convenience improvement of electromyographic (EMG) acquired by wearable devices, EMG is gradually used to evaluate muscle force signal, a non-invasive evaluation method. However, the relationship between EMG and force is a complex nonlinear relationship, even which will change with different movements and different muscle states. Therefore, it is difficult to evaluate this nonlinear EMG-force relationship, especially when the muscle state gradually transits from non-fatigue to deep fatigue. For more accurate values of force in human fatigue state, this paper proposes a dual-input Laguerre-Volterra network (LVN) model based on ant colony optimization. First, the changes in 19 EMG features are discussed with increasing fatigue. We also consider two non-Gaussian features: kurtosis and negentropy in the 19 features. Later, 11 EMG fatigue features are picked out according to the fatigue test. Then, the preprocessed EMG and a composite signal of the 11 fatigue features are simultaneously input into the LVN model. Subsequently, the ant colony optimization algorithm is selected to train the model parameters. At the same time, a penalty term that we defined is introduced into the model cost function to adjust the weight of each feature adaptively. Finally, some experiments prove that the LVN model could quick fit the accurate force signal in five fatigue stages, such as non-fatigue, slight fatigue, mild fatigue, severe fatigue, and extreme fatigue. This LVN model can quickly transform EMG into strength signal in real time, which is suitable for people to observe muscle strength by a wearable device and makes it easy to detect the muscle current state. This model has good stability and can remain effective for a long time with training once, which provides convenience for the users of wearable devices.
摘要：
随着可穿戴设备获取肌电图(EMG)的准确性和便捷性的提高,肌电图逐渐用于评估肌肉力信号，一种非侵入性评估方法。然而,肌电图与力的关系是一种复杂的非线性关系,即使它们会随着不同的运动和不同的肌肉状态而变化。因此,很难评估这种非线性EMG-力关系，特别是当肌肉状态逐渐从非疲劳过渡到深度疲劳时。为了在人体疲劳状态下获得更准确的力值，提出了一种基于蚁群优化的双输入Laguerre-Volterra网络(LVN)模型。首先,随着疲劳的增加，讨论了19个肌电图特征的变化。我们还考虑了19个特征中的两个非高斯特征：峰度和负熵。稍后，根据疲劳试验挑选出11个肌电图疲劳特征。然后，预处理的肌电图和11个疲劳特征的复合信号被同时输入到LVN模型中。随后，选择蚁群优化算法对模型参数进行训练。同时,在模型成本函数中引入我们定义的惩罚项,自适应调整各特征的权重。最后,一些实验证明，LVN模型可以快速拟合五个疲劳阶段的精确力信号，如非疲劳，轻微疲劳,轻度疲劳,严重疲劳,极度疲劳。该LVN模型可以快速将肌电图实时转换为强度信号，适合人们通过可穿戴设备观察肌肉力量，并易于检测肌肉当前状态。该模型具有较好的稳定性,经一次训练,可以长期保持有效,为可穿戴设备的用户提供便利。
