Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
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负载变化对表面肌电信号步态识别准确性的影响
DOI：
10.3390/app8091462
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发表时间：
2018-08
影响因子：
2.7
通讯作者：
Tang Zhichuan
中科院分区：
文献类型：
--
作者：
Zhang Xianfu;Sun Shouqian;Li Chao;Tang Zhichuan
随着下肢外骨骼和假肢向智能化和人机协作方向发展，准确的步态识别对于实现实时控制至关重要。许多研究者选择表面肌电信号（sEMG）来识别步态和控制下肢外骨骼（或假肢）。然而，几个因素仍然影响其适用性，其中负载的变化是一个重要的因素。本研究旨在（1）研究负载变化对步态识别的影响;（2）讨论由不同负载的表面肌电信号训练的下肢外骨骼控制系统在多负载应用中是否工作良好。本实验选取10名男大学生，分别以3种不同的速度（V3 = 3 km/h，V5 = 5 km/h，V7 = 7 km/h）和4种不同的负荷（L0 = 0，L20 = 20%，L30 = 30%，L40 = 40%体重）在跑步机上进行步行，共进行50个步态周期。采用反向传播神经网络（BPNNs）进行步态识别，并与支持向量机（SVM）和k-近邻（k-NN）进行比较。结果表明：（1）在三种速度下，当载荷在L0、L20、L30、L40范围内变化时，载荷变化对步态识别的准确性有显著影响（p <0. 05），而载荷在L0、L20、L30范围内变化时，对步态识别的准确性没有显著影响。事后最小显著性差异（LSD）可以探索所有可能的成对比较，这些比较包括使用多重t检验的等效因子的平均值，表明L40负荷与其他三个负荷（L0、L20、L30）之间存在显著差异，但在L0、L20和L30负荷之间未发现显著差异。步态识别的总平均正确率为91.81%，内载荷和间载荷分别为69.42%。(2)当训练数据来自更多类型的负载时，在每个速度下获得更高的步态识别准确率，并且统计分析表明训练集中的负载类型对步态识别准确率有显著影响（p < 0.001）。可以得出结论，在单一负载或负载的部分中训练的外骨骼（或假体）控制系统在面对多负载应用时是不足的。
As lower-limb exoskeleton and prostheses are developed to become smarter and to deploy man-machine collaboration, accurate gait recognition is crucial, as it contributes to the realization of real-time control. Many researchers choose surface electromyogram (sEMG) signals to recognize the gait and control the lower-limb exoskeleton (or prostheses). However, several factors still affect its applicability, of which variation in the loads is an essential one. This study aims to (1) investigate the effect of load variation on gait recognition; and to (2) discuss whether a lower-limb exoskeleton control system trained by sEMG from different loads works well in multi-load applications. In our experiment, 10 male college students were selected to walk on a treadmill at three different speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h) with four different loads (L0 = 0, L20 = 20%, L30 = 30%, L40 = 40% of body weight, respectively), and 50 gait cycles were performed. Back propagation neural networks (BPNNs) were used for gait recognition, and a support vector machine (SVM) and k-nearest neighbor (k-NN) were used for comparison. The result showed that (1) load variation has significant effects on the accuracy of gait recognition (p < 0.05) under the three speeds when the loads range in L0, L20, L30, or L40, but no significant impact is found when the loads range in L0, L20, or L30. The least significant difference (LSD) post hoc, which can explore all possible pair-wise comparisons of means that comprise a factor using the equivalent of multiple t-tests, reveals that there is a significant difference between the L40 load and the other three loads (L0, L20, L30), but no significant difference was found among the L0, L20, and L30 loads. The total mean accuracy of gait recognition of the intra-loads and inter-loads was 91.81%, and 69.42%, respectively. (2) When the training data was taken from more types of loads, a higher accuracy in gait recognition was obtained at each speed, and the statistical analysis shows that there was a substantial influence for the kinds of loads in the training set on the gait recognition accuracy (p < 0.001). It can be concluded that an exoskeleton (or prosthesis) control system that is trained in a single load or the parts of loads is insufficient in the face of multi-load applications.
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影响因子：
2.4
作者：
den Otter, AR;Geurts, ACH;Duysens, J
通讯作者：
Duysens, J
影响因子：
5.1
作者：
Mengarelli, Alessandro;Maranesi, Elvira;Di Nardo, Francesco
通讯作者：
Di Nardo, Francesco
DOI：
10.1109/tnsre.2005.848628
发表时间：
2005-09-01
影响因子：
4.9
作者：
Riener, R;Lünenburger, L;Dietz, V
通讯作者：
Dietz, V
DOI：
10.1109/tnsre.2015.2502663
发表时间：
2016-12-01
影响因子：
4.9
作者：
Tang, Zhichuan;Yu, Hongnian;Cang, Shuang
通讯作者：
Cang, Shuang
DOI：
10.1109/wcica.2006.1713839
发表时间：
2006-10
期刊：
2006 6th World Congress on Intelligent Control and Automation
影响因子：
--
作者：
Peng Yang;Lingling Chen;Xin Guo;Xitai Wang;Lifeng Li
通讯作者：
Peng Yang;Lingling Chen;Xin Guo;Xitai Wang;Lifeng Li