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计量学报  2024, Vol. 45 Issue (1): 121-127    DOI: 10.3969/j.issn.1000-1158.2024.01.17
  电离辐射、化学计量与生物计量 本期目录 | 过刊浏览 | 高级检索 |
基于GA-BLS方法的手势识别研究
杜义浩,曹添福,范强,王孝冉
燕山大学 电气工程学院河北省测试计量技术及仪器重点实验室河北省智能康复及神经调控重点实验室,河北 秦皇岛066004
Research on Gesture Recognition Based on GA-BLS
DU Yihao,CAO Tianfu,FAN Qiang,WANG Xiaoran
Key Lab of Measurement Technology and Instrumentation of Hebei Province, Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
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摘要 为进一步提升人机交互领域中手势识别的精度和速度,探究肌肉疲劳对手势识别的影响规律,提出了改进的GA-BLS方法,利用遗传算法(genetic algorithms, GA)优化宽度学习(broad learning system, BLS)模型参数,并使用弹性网络回归改进传统的BLS模型。利用所提模型对8种手势下的A型超声信号和肌电信号进行手势识别分析,并与SVM、KNN、RF、LDA等方法进行对比,以验证所研究方法的有效性;将长时间段下的A型超声信号和肌电信号切分成4个数据段,发现随着肌肉疲劳程度的增加,手势识别的准确率均呈现出明显下降的趋势,而且A型超声信号相较于肌电信号具有更好的抗疲劳特性。
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杜义浩
曹添福
范强
王孝冉
关键词 手势识别;生理信号遗传算法;宽度学习;肌肉疲劳弹性网络回归    
Abstract:To further improve the accuracy and speed of gesture recognition in the field of human-computer interaction, and explore the influence of muscle fatigue on gesture recognition, an improved GA-BLS method was proposed, genetic algorithms (GA) were used to optimize the parameters of the broad learning system (BLS) model, and elastic network regression was used to improve the traditional BLS model. The proposed model was used to analyze the A-mode ultrasound signal and EMG signal under eight kinds of gestures for gesture recognition, and compared with SVM, KNN, RF, LDA and other methods to verify the effectiveness of the research methods. Furthermore, the A-mode ultrasound and EMG in a long period of time were divided into four data segments. It was found that with the increase of muscle fatigue, the accuracy of gesture recognition showed a significant downward trend, and A-mode ultrasound signal had better fatigue resistance than EMG signal.
Key wordsgesture recognition    physiological signals    genetic algorithms    broad learning system    muscle fatigue    elastic network regression
收稿日期: 2023-04-28      发布日期: 2024-01-22
PACS:  TB99  
  TB973  
基金资助:河北省自然科学基金(C2020203012);河北省创新能力提升计划项目(22567619H)
作者简介: 杜义浩( 1983- ),河北沧州人,燕山大学副教授,主要从事康复机器人生物反馈控制等方面的研究。Email:duyihao@126.com
引用本文:   
杜义浩,曹添福,范强,王孝冉. 基于GA-BLS方法的手势识别研究[J]. 计量学报, 2024, 45(1): 121-127.
DU Yihao,CAO Tianfu,FAN Qiang,WANG Xiaoran. Research on Gesture Recognition Based on GA-BLS. Acta Metrologica Sinica, 2024, 45(1): 121-127.
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