基于BPSO的生理信号的情感状态识别
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摘要
情感在人类的感知、决策等过程中扮演着重要角色。长期以来情感智能研究只存在于心理学和认知科学领域,近年来随着人工智能的发展,情感智能与计算机技术结合产生了情感计算这一崭新的研究课题,这将大大地促进计算机技术的发展。情感识别是情感计算的一个关键问题,是建立和谐人机环境的基础之一,其目的是为正确选择情感信号提供理论与实验的依据,为情感的理解和表达提供可靠的原始数据,它的应用极其广泛。目前,情感识别的方法多采用面部表情、身体姿态和语音信号分析法,以及心理学上常用的问卷调查法,其结果一般受被试和主试的主观因素影响,而生理变化只受人的自主神经系统和内分泌系统支配,不受人的主观控制,因而应用生理信号测量法,所得数据更客观。情感生理反应特异性问题一直存在着争议。Ekman等人通过所做的一系列实验得出的结论表明,至少对某些情感来说,其生理反应是特异的。Picard教授带领的MIT媒体实验小组证明应用生理信号对情感识别的方法是可行的。
在情感状态识别中,大量无关或冗余的特征往往会影响识别的速度和准确率,因此需要特征选择。特征选择问题实质上是特征搜索问题,已经被证明是NP难问题,虽然有一些学者提出了许多搜索算法,但是到目前为止还没有公认有效的搜索算法。离散二进制粒子群(BinaryParticle Swarm Optimization,BPSO)算法是一种基于群智能的全局优化算法,主要用于解决组合优化问题,且具有编码简单、个体数目少、计算速度快、易于理解、易于实现等特点。因此,论文研究将BPSO算法应用于情感生理信号的特征选择问题,以提高情感状态的识别率。
论文在现有研究成果的基础上,主要做了以下三个方面的工作:
(1)针对多生理信号情感识别中的特征冗余问题,研究将计算智能的思想引入到情感生理信号的特征选择中,以期证明能否提高情感状态的正确识别率。采用BPSO方法进行情感生理信号的特征选择,对单一生理信号识别单一情感及多种情感进行了研究,同时,在单一生理信号的基础上,研究了多种生理信号识别单一情感及多种情感;
(2)针对BPSO后期搜索易停滞现象,利用改进的BPSO算法进行情感生理信号的特征选择,以提高了算法的适应能力;研究了单一生理信号识别多种情感及情感识别率随粒子变异率的变化关系。
(3)为了研究情感与生理信号之间的关系,采用几种不同的特征选择方法及分类器进行情感状态识别。
论文通过大量仿真实验证实了上述工作的正确性,取得了如下几方面的研究成果:
(1)从四种情感状态对应的四种生理信号中提取了193个原始特征,采用BPSO方法进行特征选择,四种情感的总体识别率最高达到86%。四种情感状态中,心电信号和皮电信号识别高兴的效果较好,分别达到88%和72%;肌电信号和呼吸信号识别愤怒的效果较好,分别达到80%和100%;四种生理信号中,呼吸信号识别四种情感的正确率最高,达到69.86%。
(2)通过仿真实验可知:当变异维数为2时,四种情感的平均识别率效果最好,由原来的66%提高到81.35%,用KNN分类器,四种生理信号的平均识别率最高可达到82.2%。
(3)由SFS,SFFS与KNN,LDF等方法的识别结果发现,采用四种生理信号进行情感状态识别时,Joy和Anger的识别效果较好,Pleasure的识别效果较差。
通过本文的研究可以看出,BPSO方法是一种较好的情感生理信号特征选择方法,通过使用该方法可以有效的提高情感状态识别率,为以后情感识别的实际应用奠定理论基础。
Emotions play a significant role in human perception and decision making. For a long time, research on emotion intelligence has been done in the fields of psychology and cognitive science. Along with the development of artificial intelligence these years, the combination of emotion intelligence and computer technology brings the novel research area named affective computing. This combination will greatly advance the development of the computer technology. Emotion recognition is one of the key technologies of affective computing for it is the foundations of harmonious human-machine interaction. The target of emotion recognition is supply theory and experiment foundations for correct select emotion physiological signals, and with reliable original data to support the comprehension and expression of emotion. Its application is widely. At present, the methods of emotion recognition mostly based on facial expressions, gestures and analysis of vocal signals, as well as questionnaires which are frequently used in psychology, the results of these methods are often influenced by subjects. However, we can continuously gather information about the users' emotional changes while they are connected to biosensors, since they are directly controlled by the human autonomous nervous system and incretion system, not by subjects. Therefore, the data will be more subjective obtained through recording physiological signals. Whether there is uniquely map between physiological patterns and specific emotion types, is dispute for a long time. But Ekman and colleagues had done a series of experiments and concluded that there exist some relations between physiological signal and specific emotions. Picard and colleagues at MIT Media Laboratory had testified that it is feasible to recognize emotion from physiological signals.
In emotion recognition, too many irrelevant or redundant features also affect the recognition speed and accuracy, so feature selection is necessary. In fact, the feature selection problem is a feature searching problem. It has been proved to be a NP-hard problem. Although there are many proposed searching algorithms, no one is far superior to the other. Discrete binary particle swarm optimization(BPSO) is one of the swarm intelligent global optimal algorithms. The algorithm is famous for its simple code, small population and parameters, easy to understand and realize. Therefore, this paper tries to propose with discrete binary particle swarm optimization to select useful features from a large of emotion physiological signal features, and expects it can improve the correct classification rate of emotion state.
This paper has mainly finished three research jobs based on existent research results:
(1) To remove the redundant features in emotion recognition of physiological signals, proposed that introduce the idea of computational intelligence to the feature selection of emotional physiological signals, try to improve the correct recognition rate of emotion. And then taking BPSO feature selection methods to select useful features from emotional physiological signals; studied with single signal to recognize single emotion and multiple emotions, moreover, studied multiple physiological signals to recognize single and multiple emotions based on single physiological signal.
(2) Aimed at improving the particle swarm optimization algorithms converges and escape from the local optima during search, proposed improved BPSO algorithm to select useful physiological features of emotion, improved the adaptability of BPSO, and then studied single physiological signal recognize multiple emotions and the relations between mutation ratio of particles and the correct recognition rate.
(3) Taking different feature selection methods and classifiers to recognize emotion for exploring the relationship between emotion and physiological signals.
This paper did many simulative experiments, verified the feasibility and correctness of the above job, and obtained some corresponding results:
(1) Overall 193 features were extracted from four physiological signals, these features were extracted from four different emotions corresponding physiological signals. The feature set was selected by BPSO, nearest neighbor method is applied to classify the emotion classes, the whole correct recognition rate of four emotions is up to 86%. Among four emotion types, the recognition results of Joy based on ECG and SC are better, correct recognition rate are up to 88% and 72% separately; and the recognition results of Anger based on EMG and RSP are better, correct recognition rate are up to 80% and 100% separately. With single physiological signal to recognize four emotions, the RSP signal recognition result is best during four physiological signals, is up to 69.86%.
(2) From many simulative experiments' results we can conclude that four emotions recognition rate is better when the mutation of particles' dimension is two, raised from 66% up to 81.35%. If take k-nearest neighbor as classifier, the whole average correct recognition rate of four emotions is up to 82.2%.
(3) From the recognition results of SFS、SFFS feature selection combined with KNN、LDF methods, we know that the recognition rate of Joy and Anger are better, and Pleasure is worse.
When the feature selection from physiological signal was regarded as a combinatorial optimization problem, this paper adopted BPSO as feature selection method. Experimental results demonstrate that BPSO is an effective way to emotion physiological signals feature selection. This work will put foundation for the application of emotion recognition.
在情感状态识别中,大量无关或冗余的特征往往会影响识别的速度和准确率,因此需要特征选择。特征选择问题实质上是特征搜索问题,已经被证明是NP难问题,虽然有一些学者提出了许多搜索算法,但是到目前为止还没有公认有效的搜索算法。离散二进制粒子群(BinaryParticle Swarm Optimization,BPSO)算法是一种基于群智能的全局优化算法,主要用于解决组合优化问题,且具有编码简单、个体数目少、计算速度快、易于理解、易于实现等特点。因此,论文研究将BPSO算法应用于情感生理信号的特征选择问题,以提高情感状态的识别率。
论文在现有研究成果的基础上,主要做了以下三个方面的工作:
(1)针对多生理信号情感识别中的特征冗余问题,研究将计算智能的思想引入到情感生理信号的特征选择中,以期证明能否提高情感状态的正确识别率。采用BPSO方法进行情感生理信号的特征选择,对单一生理信号识别单一情感及多种情感进行了研究,同时,在单一生理信号的基础上,研究了多种生理信号识别单一情感及多种情感;
(2)针对BPSO后期搜索易停滞现象,利用改进的BPSO算法进行情感生理信号的特征选择,以提高了算法的适应能力;研究了单一生理信号识别多种情感及情感识别率随粒子变异率的变化关系。
(3)为了研究情感与生理信号之间的关系,采用几种不同的特征选择方法及分类器进行情感状态识别。
论文通过大量仿真实验证实了上述工作的正确性,取得了如下几方面的研究成果:
(1)从四种情感状态对应的四种生理信号中提取了193个原始特征,采用BPSO方法进行特征选择,四种情感的总体识别率最高达到86%。四种情感状态中,心电信号和皮电信号识别高兴的效果较好,分别达到88%和72%;肌电信号和呼吸信号识别愤怒的效果较好,分别达到80%和100%;四种生理信号中,呼吸信号识别四种情感的正确率最高,达到69.86%。
(2)通过仿真实验可知:当变异维数为2时,四种情感的平均识别率效果最好,由原来的66%提高到81.35%,用KNN分类器,四种生理信号的平均识别率最高可达到82.2%。
(3)由SFS,SFFS与KNN,LDF等方法的识别结果发现,采用四种生理信号进行情感状态识别时,Joy和Anger的识别效果较好,Pleasure的识别效果较差。
通过本文的研究可以看出,BPSO方法是一种较好的情感生理信号特征选择方法,通过使用该方法可以有效的提高情感状态识别率,为以后情感识别的实际应用奠定理论基础。
Emotions play a significant role in human perception and decision making. For a long time, research on emotion intelligence has been done in the fields of psychology and cognitive science. Along with the development of artificial intelligence these years, the combination of emotion intelligence and computer technology brings the novel research area named affective computing. This combination will greatly advance the development of the computer technology. Emotion recognition is one of the key technologies of affective computing for it is the foundations of harmonious human-machine interaction. The target of emotion recognition is supply theory and experiment foundations for correct select emotion physiological signals, and with reliable original data to support the comprehension and expression of emotion. Its application is widely. At present, the methods of emotion recognition mostly based on facial expressions, gestures and analysis of vocal signals, as well as questionnaires which are frequently used in psychology, the results of these methods are often influenced by subjects. However, we can continuously gather information about the users' emotional changes while they are connected to biosensors, since they are directly controlled by the human autonomous nervous system and incretion system, not by subjects. Therefore, the data will be more subjective obtained through recording physiological signals. Whether there is uniquely map between physiological patterns and specific emotion types, is dispute for a long time. But Ekman and colleagues had done a series of experiments and concluded that there exist some relations between physiological signal and specific emotions. Picard and colleagues at MIT Media Laboratory had testified that it is feasible to recognize emotion from physiological signals.
In emotion recognition, too many irrelevant or redundant features also affect the recognition speed and accuracy, so feature selection is necessary. In fact, the feature selection problem is a feature searching problem. It has been proved to be a NP-hard problem. Although there are many proposed searching algorithms, no one is far superior to the other. Discrete binary particle swarm optimization(BPSO) is one of the swarm intelligent global optimal algorithms. The algorithm is famous for its simple code, small population and parameters, easy to understand and realize. Therefore, this paper tries to propose with discrete binary particle swarm optimization to select useful features from a large of emotion physiological signal features, and expects it can improve the correct classification rate of emotion state.
This paper has mainly finished three research jobs based on existent research results:
(1) To remove the redundant features in emotion recognition of physiological signals, proposed that introduce the idea of computational intelligence to the feature selection of emotional physiological signals, try to improve the correct recognition rate of emotion. And then taking BPSO feature selection methods to select useful features from emotional physiological signals; studied with single signal to recognize single emotion and multiple emotions, moreover, studied multiple physiological signals to recognize single and multiple emotions based on single physiological signal.
(2) Aimed at improving the particle swarm optimization algorithms converges and escape from the local optima during search, proposed improved BPSO algorithm to select useful physiological features of emotion, improved the adaptability of BPSO, and then studied single physiological signal recognize multiple emotions and the relations between mutation ratio of particles and the correct recognition rate.
(3) Taking different feature selection methods and classifiers to recognize emotion for exploring the relationship between emotion and physiological signals.
This paper did many simulative experiments, verified the feasibility and correctness of the above job, and obtained some corresponding results:
(1) Overall 193 features were extracted from four physiological signals, these features were extracted from four different emotions corresponding physiological signals. The feature set was selected by BPSO, nearest neighbor method is applied to classify the emotion classes, the whole correct recognition rate of four emotions is up to 86%. Among four emotion types, the recognition results of Joy based on ECG and SC are better, correct recognition rate are up to 88% and 72% separately; and the recognition results of Anger based on EMG and RSP are better, correct recognition rate are up to 80% and 100% separately. With single physiological signal to recognize four emotions, the RSP signal recognition result is best during four physiological signals, is up to 69.86%.
(2) From many simulative experiments' results we can conclude that four emotions recognition rate is better when the mutation of particles' dimension is two, raised from 66% up to 81.35%. If take k-nearest neighbor as classifier, the whole average correct recognition rate of four emotions is up to 82.2%.
(3) From the recognition results of SFS、SFFS feature selection combined with KNN、LDF methods, we know that the recognition rate of Joy and Anger are better, and Pleasure is worse.
When the feature selection from physiological signal was regarded as a combinatorial optimization problem, this paper adopted BPSO as feature selection method. Experimental results demonstrate that BPSO is an effective way to emotion physiological signals feature selection. This work will put foundation for the application of emotion recognition.
引文
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[76]http://mm-werkstatt.informatik.uni-augsburg.de/team_details.php?id=3
[77]杨瑞请,刘光远.基于BPSO的四种生理信号的情感状态识别[J].计算机科学,2008,35(3):137-138.
[78]Rui-Qing Yang and Guangyuan Liu.Emotion Feature Selection from Physiological Signal Based on BPSO[A].Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering(ISKE2007)[C].Chengdu.2007,1467-1470.(ISTP检索).
[79]杨瑞请,刘光远.基于变异BPSO方法的情感生理信号的特征选择[A].第四届智能CAD与数字娱乐学术会议[C].上海.2007,69-72.
[80]杨瑞请,刘光远.变异BPSO方法在情感生理信号特征选择中的应用.(已撰写).
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[70]吕振肃,候志荣.自适应变异的粒子群优化算法[J].电子学报 2004,32(3):416-420.
[71]李宁,孙德宝等.带变异算子的粒子群优化算法[J].计算机工程与应用.2004,(17):12-14.
[72]李爱国.多粒子群协同优化算法[J].复旦学报(自然科学版).2004,43(5):923-925.
[73]彭宇.群智能优化算法及理论研究.[博士学位论文].哈尔滨工业大学.2004,6.
[74]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.
[75]乔立岩,彭喜元,彭宇.基于微粒群算法和支持向量机的特征子集选择方[J].电子学报.2006,34:496-498.
[76]http://mm-werkstatt.informatik.uni-augsburg.de/team_details.php?id=3
[77]杨瑞请,刘光远.基于BPSO的四种生理信号的情感状态识别[J].计算机科学,2008,35(3):137-138.
[78]Rui-Qing Yang and Guangyuan Liu.Emotion Feature Selection from Physiological Signal Based on BPSO[A].Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering(ISKE2007)[C].Chengdu.2007,1467-1470.(ISTP检索).
[79]杨瑞请,刘光远.基于变异BPSO方法的情感生理信号的特征选择[A].第四届智能CAD与数字娱乐学术会议[C].上海.2007,69-72.
[80]杨瑞请,刘光远.变异BPSO方法在情感生理信号特征选择中的应用.(已撰写).