Yuan Yaoyao, Cao Dianguo, Li Cong, Liu Chengyu
College of Engineering, Qufu Normal University, Rizhao, Shandong 276826, P.R.China.
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Dec 25;37(6):1056-1064. doi: 10.7507/1001-5515.201907053.
In the process of lower limb rehabilitation training, fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury. However, most of the existing methods only consider surface electromyography (sEMG) features but ignore electrocardiogram (ECG) features when performing in fatigue estimation, which leads to the low and unstable recognition efficiency. Aiming at this problem, a method that uses the fusion features of ECG and sEMG signal to estimate the fatigue during lower limb rehabilitation was proposed, and an improved particle swarm optimization-support vector machine classifier (improved PSO-SVM) was proposed and used to identify the fusion feature vector. Finally, the accurate recognition of the three states of relax, transition and fatigue was achieved, and the recognition rates were 98.5%, 93.5%, and 95.5%, respectively. Comparative experiments showed that the average recognition rate of this method was 4.50% higher than that of sEMG features alone, and 13.66% higher than that of the combined features of ECG and sEMG without feature fusion. It is proved that the feature fusion of ECG and sEMG signals in the process of lower limb rehabilitation training can be used for recognizing fatigue more accurately.
在下肢康复训练过程中，疲劳估计对于提高意图识别的准确性和避免二次损伤具有重要意义。然而，现有的大多数方法在进行疲劳估计时仅考虑表面肌电图（sEMG）特征，而忽略了心电图（ECG）特征，这导致识别效率低下且不稳定。针对这一问题，提出了一种利用心电图和表面肌电图信号的融合特征来估计下肢康复过程中疲劳的方法，并提出了一种改进的粒子群优化支持向量机分类器（改进的PSO-SVM）用于识别融合特征向量。最后，实现了对放松、过渡和疲劳三种状态的准确识别，识别率分别为98.5%、93.5%和95.5%。对比实验表明，该方法的平均识别率比单独使用表面肌电图特征高4.50%，比未进行特征融合的心电图和表面肌电图组合特征高13.66%。证明了在下肢康复训练过程中心电图和表面肌电图信号的特征融合可用于更准确地识别疲劳。
