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A deep learning method for contactless emotion recognition from ballistocardiogram

文献类型：期刊论文

作者Yu, Xianya6,7; Zou, Yonggang6,7; Mou, Xiuying6,7; Li, Siying6,7; Bai, Zhongrui4; Du, Lidong6,7; Li, Zhenfeng7; Wang, Peng7; Chen, Xianxiang6,7; Li, Xiaoran3
刊名BIOMEDICAL SIGNAL PROCESSING AND CONTROL
出版日期2025
卷号99页码:10
关键词Ballistocardiogram Emotion recognition Contactless technology
ISSN号1746-8094
DOI10.1016/j.bspc.2024.106891
通讯作者Chen, Xianxiang(chenxx@aircas.ac.cn) ; Li, Xiaoran(lixiaoran30@163.com) ; Li, Fenghua(lifh@psych.ac.cn) ; Li, Huaiyong(leehy1991@163.com) ; Fang, Zhen(zfang@mail.ie.ac.cn)
英文摘要Emotion recognition is a major research point in the field of affective computing. Existing research on the application of physiological signals to emotion recognition mainly focuses on the processing of contact signals. However, there are issues with contact signal acquisition equipment, such as limited portability and poor user compliance, which make it difficult to promote its use. To explore a new method for emotion recognition based on contactless ballistocardiogram (BCG), we proposed a SE-CNN model with a multi-class focal loss function. To construct the dataset, we used audio-visual stimuli to evoke the subjects' emotions and collected data on the subjects' three discrete emotions, positive, neutral, and negative, through our established BCG signal acquisition system based on a piezoelectric ceramics sensor. Root mean square filter and thresholding were used to detect and eliminate motion artifacts of BCG signals. We did two kinds of preprocessing on BCG signals: wavelet transform and bandpass filtering, to explore the effect of different components of BCG on emotion recognition. Subsequently, we verified the model's performance and cross-time working ability through traditional K-Fold and our proposed K-Session cross-validation methods. The results showed that the band-pass filtering method was more beneficial to the current classification task. Under K-Fold cross-validation, the model's accuracy, precision, and recall were 97.21%, 97.00%, and 97.11%. Under K-Session cross-validation, the model's accuracy, precision, and recall were 94.66%, 93.92%, and 94.86%, respectively, all of which were better than the classification effect of synchronous ECG. The reliability of BCG in contactless emotion recognition was proved.
收录类别SCI
资助项目National Key Research and Development Project[2020YFC2003703] ; National Key Research and Development Project[2021YFC3002204] ; National Key Research and Development Project[2020YFC1512304] ; National Natural Science Foundation of China[62071451] ; CAMS Innovation Fund for Medical Sciences[2019-I2M-5-019]
WOS研究方向Engineering
语种英语
WOS记录号WOS:001316856800001
出版者ELSEVIER SCI LTD
资助机构National Key Research and Development Project ; National Natural Science Foundation of China ; CAMS Innovation Fund for Medical Sciences
源URL[http://ir.psych.ac.cn/handle/311026/48984]  
专题心理研究所_认知与发展心理学研究室
通讯作者Chen, Xianxiang; Li, Xiaoran; Li, Fenghua; Li, Huaiyong; Fang, Zhen
作者单位1.Peoples Liberat Army Gen Hosp, Med Ctr 6, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China
3.Capital Med Univ, Beijing Friendship Hosp, Beijing, Peoples R China
4.Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai, Peoples R China
5.Chinese Acad Med Sci, Personalized Management Chron Resp Dis, Beijing, Peoples R China
6.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing, Peoples R China
7.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Xianya,Zou, Yonggang,Mou, Xiuying,et al. A deep learning method for contactless emotion recognition from ballistocardiogram[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2025,99:10.
APA Yu, Xianya.,Zou, Yonggang.,Mou, Xiuying.,Li, Siying.,Bai, Zhongrui.,...&Fang, Zhen.(2025).A deep learning method for contactless emotion recognition from ballistocardiogram.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,99,10.
MLA Yu, Xianya,et al."A deep learning method for contactless emotion recognition from ballistocardiogram".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 99(2025):10.

入库方式： OAI收割

来源：心理研究所

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