Wei Pengna, Zhang Jinhua, Wei Pingping, Wang Baozeng, Hong Jun
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1002-1006. doi: 10.1109/EMBC44109.2020.9175655.
This research focuses on the gait phase recognition using different sEMG and EEG features. Seven healthy volunteers, 23-26 years old, were enrolled in this experiment. Seven phases of gait were divided by three-dimensional trajectory of lower limbs during treadmill walking and classified by Library for Support Vector Machines (LIBSVM). These gait phases include loading response, mid-stance, terminal Stance, pre-swing, initial swing, mid-swing, and terminal swing. Different sEMG and EEG features were assessed in this study. Gait phases of three kinds of walking speed were analyzed. Results showed that the slope sign change (SSC) and mean power frequency (MPF) of sEMG signals and SSC of EEG signals achieved higher accuracy of gait phase recognition than other features, and the accuracy are 95.58% (1.4 km/h), 97.63% (2.0 km/h) and 98.10% (2.6 km/h) respectively. Furthermore, the accuracy of gait phase recognition in the speed of 2.6 km/h is better than other walking speeds.
本研究聚焦于利用不同的表面肌电图（sEMG）和脑电图（EEG）特征进行步态阶段识别。七名年龄在23至26岁之间的健康志愿者参与了该实验。在跑步机行走过程中，通过下肢的三维轨迹划分出七个步态阶段，并使用支持向量机库（LIBSVM）进行分类。这些步态阶段包括负重反应、站立中期、站立末期、摆动前期、摆动初期、摆动中期和摆动末期。本研究评估了不同的sEMG和EEG特征。分析了三种步行速度下的步态阶段。结果表明，sEMG信号的斜率符号变化（SSC）和平均功率频率（MPF）以及EEG信号的SSC在步态阶段识别中比其他特征具有更高的准确率，其准确率分别为95.58%（1.4公里/小时）、97.63%（2.0公里/小时）和98.10%（2.6公里/小时）。此外，2.6公里/小时速度下的步态阶段识别准确率优于其他步行速度。
