| 采用鱼群算法进化极限学习机的假肢步态识别 |
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| 引用本文： | 刘磊,陈增强,杨鹏,刘作军.采用鱼群算法进化极限学习机的假肢步态识别[J].振动．测试与诊断,2019,39(6):1225-1231. |
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| 作者姓名： | 刘磊 陈增强 杨鹏 刘作军 |
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| 作者单位： | (1. 郑州轻工业大学建筑环境工程学院 郑州，450002）（2. 南开大学人工智能学院 天津，300350）（3. 河北工业大学人工智能与数据科学学院 天津，300130） |
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| 基金项目： | 国家自然科学基金资助项目（61773151，61703135）;河南省高等学校重点科研资助项目（18A413014）;郑州轻工业大学博士基金资助项目（0314-13501050006）;郑州轻工业大学校级青年骨干教师资助项目（0314-13502010001） |
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| 摘 要： | 为提高下肢假肢步态识别的准确性,提出一种基于鱼群(fish swarm,简称FA)算法优化极限学习机(extreme learning machine,简称ELM)的模式识别方法。首先,提取张量投影特征,分析了特征值选取的合理性;其次,采用主成分分析法降维;最后,利用鱼群算法进化极限学习机分类识别平地行走、上楼、下楼、上坡及下坡5种步态,识别准确率达到97.45%。通过实验比较了该算法与极限学习机等分类器在假肢步态分类上的识别准确率与识别时间,结果表明,FA-ELM方法识别准确率优于其他方法。
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| 关 键 词： | 表面肌电信号 极限学习机 鱼群算法 步态识别 智能假肢 |
Locomotion-Mode Recognition Based on Fish Swarm Algorithm and Extreme Learning Machine |
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| LIU Lei,CHEN Zengqiang,YANG Peng,LIU Zuojun.Locomotion-Mode Recognition Based on Fish Swarm Algorithm and Extreme Learning Machine[J].Journal of Vibration,Measurement & Diagnosis,2019,39(6):1225-1231. |
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| Authors: | LIU Lei CHEN Zengqiang YANG Peng LIU Zuojun |
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| Affiliation: | (1. School of Building Environmental Engineering, Zhengzhou College of Light Industry Zhengzhou, 450002, China)(2. College of Artificial Intelligence, Nankai University Tianjin, 300350, China)(3. School of Artificial Intelligence and Data Science, Hebei University of Technology Tianjin, 300130, China) |
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| Abstract: | In light of the accuracy and timeliness of locomotion-mode recognition of intelligent prosthesis, a pattern recognition method based on the fish swarm (FA) algorithm is proposed to optimize the extreme learning machine (ELM). First, the features of tensor projection are extracted, and the rationality of the selection of features is analyzed. Then, principal component analysis is used to reduce the dimension. Finally, treads of walking on-ground, upstairs, downstairs, uphill, and downhill are recognized using the learning machine of FA-ELM. The recognition accuracy is 97.45%. The comparison with ELM and other classifiers shows that FA-ELM recognizes more accurately. |
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| Keywords: | Surface electromyography(sEMG) extreme learning machine(ELM) fish swarm algorithm(FA) locomotion-mode recognition intelligent prosthesis |
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