Department of Mechanical and Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
Institute of Advanced Technology, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2022 Nov 6;22(21):8551. doi: 10.3390/s22218551.
Multi-source information fusion technology is a kind of information processing technology which comprehensively processes and utilizes multi-source uncertain information. It is an effective scheme to solve complex pattern recognition and improve classification performance. This study aims to improve the accuracy and robustness of exoskeleton gait pattern transition recognition in complex environments. Based on the theory of multi-source information fusion, this paper explored a multi-source information fusion model for exoskeleton gait pattern transition recognition in terms of two aspects of multi-source information fusion strategy and multi-classifier fusion. For eight common gait pattern transitions (between level and stair walking and between level and ramp walking), we proposed a hybrid fusion strategy of multi-source information at the feature level and decision level. We first selected an optimal feature subset through correlation feature extraction and feature selection algorithm, followed by the feature fusion through the classifier. We then studied the construction of a multi-classifier fusion model with a focus on the selection of base classifier and multi-classifier fusion algorithm. By analyzing the classification performance and robustness of the multi-classifier fusion model integrating multiple classifier combinations with a number of multi-classifier fusion algorithms, we finally constructed a multi-classifier fusion model based on D-S evidence theory and the combination of three SVM classifiers with different kernel functions (linear, RBF, polynomial). Such multi-source information fusion model improved the anti-interference and fault tolerance of the model through the hybrid fusion strategy of feature level and decision level and had higher accuracy and robustness in the gait pattern transition recognition, whose average recognition accuracy for eight gait pattern transitions reached 99.70%, which increased by 0.15% compared with the highest average recognition accuracy of the single classifier. Moreover, the average recognition accuracy in the absence of different feature data reached 97.47% with good robustness.
多源信息融合技术是一种综合处理和利用多源不确定信息的信息处理技术。它是解决复杂模式识别问题、提高分类性能的有效方案。本研究旨在提高复杂环境下外骨骼步态模式转换识别的准确性和鲁棒性。基于多源信息融合理论，本文从多源信息融合策略和多分类器融合两个方面探讨了一种外骨骼步态模式转换识别的多源信息融合模型。针对八种常见的步态模式转换（水平与楼梯行走之间、水平与斜坡行走之间），我们提出了一种特征级和决策级多源信息混合融合策略。我们首先通过相关特征提取和特征选择算法选择最优特征子集，然后通过分类器进行特征融合。然后，我们研究了构建多分类器融合模型的问题，重点关注基分类器的选择和多分类器融合算法。通过分析集成多个分类器组合和多个多分类器融合算法的多分类器融合模型的分类性能和鲁棒性，最终构建了基于 D-S 证据理论和三个具有不同核函数（线性、RBF、多项式）的 SVM 分类器组合的多分类器融合模型。这种多源信息融合模型通过特征级和决策级的混合融合策略提高了模型的抗干扰和容错能力，在步态模式转换识别中具有更高的准确性和鲁棒性，对八种步态模式转换的平均识别准确率达到 99.70%，比单个分类器的最高平均识别准确率提高了 0.15%。此外，在不同特征数据缺失的情况下，平均识别准确率达到 97.47%，具有良好的鲁棒性。
