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基于经验公式的连续手势动作表面肌电信号识别方法
引用本文:朱旭鹏,陈香,李云,赵璋炎. 基于经验公式的连续手势动作表面肌电信号识别方法[J]. 北京生物医学工程, 2012, 31(2): 117-124
作者姓名:朱旭鹏  陈香  李云  赵璋炎
作者单位:中国科学技术大学,合肥,230027;中国科学技术大学,合肥,230027;中国科学技术大学,合肥,230027;中国科学技术大学,合肥,230027
基金项目:国家自然科学基金,中央高校基本科研业务费专项资金
摘    要:目的实现连续手势动作表面肌电信号(surface electromyography,sEMG)的简单有效识别。方法首先推导出测试信号属于手势动作模板的概率密度经验公式,通过数据处理实验确定公式参数,最后设计连续手势识别实验以测试该经验公式用于动作sEMG识别的效果。结果推导出的经验公式在连续手势识别中获得了较好的识别结果,验证了该经验公式用于连续手势动作sEMG信号识别的有效性。结论基于经验公式的方法为实现基于sEMG信号的连续手势识别提供了一种可行的解决方案。

关 键 词:表面肌电信号  连续手势  手势识别  经验公式

Continuous hand gesture surface electromyography recognition method based on empirical formula
ZHU Xupeng,CHEN Xiang,LI Yun,ZHAO Zhangyan. Continuous hand gesture surface electromyography recognition method based on empirical formula[J]. Beijing Biomedical Engineering, 2012, 31(2): 117-124
Authors:ZHU Xupeng  CHEN Xiang  LI Yun  ZHAO Zhangyan
Affiliation:School of Information Science and Technology, University of Science and Technology of China, Hefei 230027
Abstract:Objective To realize the continuous hand gesture recognition using surface electro myography (sEMG). Methods A method for continuous hand gesture recognition based on an empirical formula is proposed. This method consists of three steps. First, a formulato describe the probability of testing samples belonging to each hand gesture class is derived from the hand gesture features. Next ,the empirical coefficients for the formula are determined by a data processing experiment. Finally,the performance of sEMG classification based on the proposed empirical formula is quantified via the experiment on continuous hand gesture recognition. Results The empirical coefficients for the formula are able to be determined by experimental method effectively, and promising results on the EMG-based continuous hand gesture recognition can be achieved through the proposed empirical formula. The experimental results demonstrate the effectiveness of applying such empirical formula on sEMG-based hand gesture recognition. Conclusions The proposed method using empirical formula provides a practical solution to sEMG-based real-time continuous hand gesture recognition.
Keywords:surface electromyography  continuous gesture  gesture recognition  empirical formula
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