The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.

中文翻译：

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基于表面肌电信号的下肢运动特征提取与分类评价


运动分类的实时性和准确性对于老年人或体弱者的日常活动起着至关重要的作用。本研究旨在确定日常生活活动（ADL）的最佳特征提取和分类方法。实验中，我们收集了下肢大腿半腱肌、大腿外侧肌肉和小腿腓肠肌的表面肌电（sEMG）信号，对水平行走、跨越障碍物、站立、下楼梯和上楼梯进行分类。首先，我们分析了时域、频域、时频域、熵等11种特征提取方法。此外，提出了一种特征评估方法，并计算了11种特征提取算法的可分离性。然后，结合11种特征算法，计算了55种分类方法的分类精度和时间。结果表明，采用WAMP的高斯核线性判别分析（GK-LDA）具有最高的分类准确率（96%），并且计算时间低于80 ms。本文对特征提取和分类方法进行定量比较分析，有利于可穿戴式肌电传感器系统在ADL中的应用。