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基于遗传算法的生理信号情感识别 被引量：2

Emotion Recognition with Physiological Signals Based on Genetic Algorithm
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摘要 针对用生理信号来识别情感状态中的最优情感特征组合选择这一组合优化问题,用遗传算法来选择最能代表相应情感状态的最优特征组合,以最近邻法的分类正确率作为当前搜索到的最优特征组合评价准则,将两者结合用于joy,anger,pleasure,sadness 4种情感状态的识别,得到了较好的情感识别效果.仿真实验表明,该方法是有效的. Taking into account the combinational optimization probl logical signals, the genetic algorithm was used to search for the o exactly the relevant affective states, and the classified accuracy of t ation criterion in the search of the optimal feature subset. Combin joy, anger, pleasure and sadness, the two acquired a good emotion suhs have shown the method to be effective. em of emotion recognition with physio-logical signals,the genetic algorithm was used to search for the optimal feature subset which represents he nearest neighbor is used as an evalued to recognize the emotional states of recognition effect. The experiment results have shown the method to be effective.
作者 牛晓伟 刘光远
机构地区 西南大学电子信息工程学院
出处 《西南大学学报（自然科学版）》 CAS CSCD 北大核心 2007年第9期134-138,共5页 Journal of Southwest University(Natural Science Edition)
基金 重庆市科委资助项目(CSTC 2006BB2028)
关键词 遗传算法 最近邻分类 最优情感特征组合 genetic algorithm the nearest neighbor classification optimal emotion feature subset
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献8

  • 1[1]Picard R W.Affective Computing[M].Cambridge:MIT Press,1997:1-27.
  • 2[2]Elias Vyzas,Rosalind W.Picard.Affective Pattern Classification[EB/OL].http://affect.media.mit.edu/pub-lications.php,2006-04-20.
  • 3[3]Rosalind Picard,Jennifer Healey.Digital Processing of Affective Signals[EB/OL].http://affect.media.mit.edu/publications.php,2006-04-20.
  • 4[4]Picard R W,Vyzas E,Healey J.Toward Machine Emotional Intelligence,Analysis of Affective Physiological State[J].IEEE Transactions Pattern Analysis and Machine Intelligence,2001,23(10):1175-1191.
  • 5[5]Haag A,Goronzy S,Schaich P,Williams J.Emotion Recognition Using Bio-Sensors,First Step Towards an Automatic System[A].Affective Dialogue Systems,Tutorial and Research Workshop[C].Kloster Irsee:IEEE,2004:36-48.
  • 6[6]Johannes Wagner,Jonghwa Kim,Elisabeth André.From Physiological Signals to Emotions,Implementing and Comparing Selected Methods for Feature Extraction and Classification[A].In IEEE International Conference on Multimedia & Expo(ICME 2005)[C].Augsburg:IEEE,2005:940-943.
  • 7乔立岩,彭喜元,马云彤.基于遗传算法和支持向量机的特征子集选择方法[J].电子测量与仪器学报,2006,20(1):1-5. 被引量：24
  • 8[9]Johannes Wagner.From Physiological Signals to Emotions,Implementing and Comparing Selected Methods for Feature Extraction and Classification[DB/OL].http://emotion-research.net/,2006-02-14.

二级参考文献11

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共引文献23

同被引文献27

  • 1严新平,张晖,吴超仲,毛喆,雷虎.道路交通驾驶行为研究进展及其展望[J].交通信息与安全,2013,31(1):45-51. 被引量：41
  • 2黄志剑,王魏芳,杨健梅.紧张状态下优秀运动员动态心理特征分析——情绪反应变化视角[J].武汉体育学院学报,2006,40(3):33-37. 被引量：4
  • 3Parker tt. Stress Management[M]. Delhi, IND: Global Media, 2007.
  • 4Kiecolt-Glaser JK, McGuire L, Robles TF, et al. Psychoneuroim- munology: psychological influences on immune function and health[J]. J Consult Clin Psychol, 2002, 70(3): 537.
  • 5Lazarus RS. Stress and emotion: A new synthesis[M] New York: Springer Publishing Company, 2006.
  • 6Cowie R, Douglas-Cowie E, Tsapatsoulis N, et al. Emotion recognition in human-computer interaction[J]. IEEE Signal Proc Mag, 2001, 18(l):32-80.
  • 7Kreibig SD. Autonomic nervous system activity in emotion: A review[J]. Biot Psychol, 2010, 84(3): 394-421.
  • 8Picard RW. Affective computing[M]. Cambridge: The MIT Press, 1997.
  • 9Picard RW, Vyzas E, Healey J. Toward machine emotionalintelligence: Analysis of affective physiological state[J]. IEEE q- Pattern Anal Mach Intell, 2001, 23(10): 1175-1191.
  • 10Nasoz F, Alvarez K, Lisetti CL, et al. Emotion recognition t~om physiological signals using wireless sensors for presence technologies[J]. Cogn Technol Work, 2004, 6(1): 4-14.

引证文献2

二级引证文献14

西南大学学报（自然科学版）
2007年 第9期

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