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作 者:景甜甜 李昊 高婷 董必春 JING Tiantian;LI Hao;GAO Ting;DONG Bichun(School of Mechanical and Electrical Engineering,Anhui Jianzhu University,230601,Hefei,Anhui,China)
机构地区:[1]安徽建筑大学机械与电气工程学院,安徽合肥230601
出 处:《淮北师范大学学报(自然科学版)》2025年第1期51-55,共5页Journal of Huaibei Normal University:Natural Sciences
基 金:安徽省高校省级自然科学研究项目(KJ2021JD23);安徽省高校杰出青年科学研究项目(2022AH020025);安徽建筑大学科学研究项目(KJ223013);安徽省建筑声环境重点实验室开放课题/主任基金项目(AAE2021ZR01)。
摘 要:为提高人体手腕动作识别准确率,提出一种基于粒子群算法优化极限学习机动作模式识别新方法。通过虚拟仪器采集人体手腕内翻、外翻、握拳、展拳4种动作对应肌电信号,通过小波分析方法构造其特征矢量,然后利用特征矢量对极限学习机进行训练,结合粒子群优化算法强大寻优能力,优化调整极限学习机模型主要参数,最后采用优化后极限学习机模型对4种手腕动作对应测试集数据进行模式识别。结果表明,采用粒子群优化算法优化极限学习机模型有着更高手腕动作识别率,验证该方法可行性。In order to improve the accuracy of human wrist motion recognition,a new method for motion pattern recognition based on particle swarm optimization of extreme learning machines was proposed.The electromyographic signals corresponding to the four movements of human wrist inversion,eversion,clenching,and stretching ware collected through virtual instruments.The feature vectors ware constructed using wavelet analysis method,and then the extreme learning machine was trained using the feature vectors.Combined with the powerful optimization ability of particle swarm optimization algorithm,the main parameters of the extreme learning machine model were optimized and adjusted.Finally,the optimized extreme learning machine model was used to recognize the pattern of the test set data corresponding to the four wrist movements.The recognition results indicate that the extreme learning machine model optimized by particle swarm optimization algorithm has a higher recognition rate of wrist movements,which verifies the feasibility of this method.
分 类 号:R318.0[医药卫生—生物医学工程]
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