CAJ | 학술논문

压力能诱发兴奋、厌烦、恐惧等多种不同的情绪,不同程度的某种压力能诱发不同程度的情绪.本文通过设计情绪诱发实验,分别诱发出被试平静、兴奋、厌烦、恐惧情绪以及低度、中度、高度三种紧张情绪程度.基于这些情绪状态下被试的心率、呼吸率等六种生理信号,去除基线预处理后进行特征提取,结合粒子群优化算法对特征进行选择,采用k近邻算法对压力状态下的不同情绪及紧张情绪程度进行分类.实验结果表明,通过基线去除及粒子群特征选择优化后k近邻分类,与传统k近邻分类相比,具有更好的识别效果.
압력능유발흥강、염번、공구등다충불동적정서,불동정도적모충압력능유발불동정도적정서.본문통과설계정서유발실험,분별유발출피시평정、흥강、염번、공구정서이급저도、중도、고도삼충긴장정서정도.기우저사정서상태하피시적심솔、호흡솔등륙충생리신호,거제기선예처리후진행특정제취,결합입자군우화산법대특정진행선택,채용k근린산법대압력상태하적불동정서급긴장정서정도진행분류.실험결과표명,통과기선거제급입자군특정선택우화후k근린분류,여전통k근린분류상비,구유경호적식별효과.
@@@@In this paper, experiments were designed for inducing neutral, terrified, excited, annoying emotions, and also low, middle, high, three levels of tension emotions of stress state, respectively. Based on the multi physiological signals generated by the subjects in emotions, such as heart rate and respiration rate and so on, we extracted features from these data which had been eliminated the baseline. Then the Particle Swarm Optimization method was adopted to optimize the features selection from the features of multi physiological signals, and combined with k-Nearest Neighbor algorithm, different emotions and varying degree tensions were classified. The result shows that the classification accuracy of the kNN method with SPO and baseline eliminated is better than the traditional kNN method.