Construction of a Digital Recognition Framework for TCM Emotions Based on Facial Expression Recognition Technology
10.19879/j.cnki.1005-5304.202409629
- VernacularTitle:基于面部表情识别技术的中医情志数字化识别框架构建
- Author:
Yuyi TANG
1
;
Ziqiang SHEN
;
Junfeng YAN
;
Yang LI
;
Guoying ZHAO
Author Information
1. 湖南中医药大学,湖南 长沙 410208;湖南省智慧中医工程技术研究中心,湖南 长沙 410208
- Publication Type:Journal Article
- Keywords:
facial expression recognition;
TCM emotions;
TCM observation and diagnosis;
emotion recognition application;
emotion digitalization
- From:
Chinese Journal of Information on Traditional Chinese Medicine
2025;32(6):18-23
- CountryChina
- Language:Chinese
-
Abstract:
This article is based on modern psychology and TCM emotional theory,combined with facial expression recognition technology,to apply deep learning methods to the digital research of TCM emotions to more accurately capture and analyze patients'emotional states.A cross-disciplinary framework was established by synthesizing facial expression-emotion correlations from psychological and TCM perspectives.The methodology included:Data annotation of TCM-defined emotional expressions using standardized coding systems;Facial expression acquisition,spatiotemporal feature extraction and emotion classification through a 3D convolutional neural network(3D CNN).The framework achieved 91.43%accuracy in video-based emotion classification.High-arousal emotional states demonstrated superior recognition performance,with anger showing optimal recall(1.000 0)and F1-score(0.946 3),while surprise attained the highest precision(0.976 0).These findings aligned with TCM pathological characteristic of"anger induces qi ascending,surprise disrupts qi flow".The digital recognition framework for TCM emotional quantification based on facial expression recognition technology exhibits strong alignment with TCM observation diagnosis,providing clinicians with an objective tool to assess the"seven emotions"and elucidating emotion-facial correlations in classical TCM theory.Future research should focus on longitudinal validation across diverse populations and establish development pathways for AI-assisted TCM diagnostic systems.
