Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (15): 277-283.DOI: 10.3778/j.issn.1002-8331.2305-0311

• Engineering and Applications • Previous Articles     Next Articles

Facial Pain Assessment Algorithm Fusing VIT and CNN Attention Mechanism

GUO Shijie, LU Shijie, GENG Yanli, GU Bowen, SUN Hao   

  1. 1.School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
    2.Engineering Research Center of Intelligent Rehabilitation Device Detection Technology, Ministry of Education, Hebei University of Technology, Tianjin 300401, China
    3.School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
  • Online:2024-08-01 Published:2024-07-30

融合VIT与CNN注意力机制的面部疼痛评估算法研究

郭士杰,卢世杰,耿艳利,顾博文,孙浩   

  1. 1.河北工业大学 机械工程学院,天津 300401
    2.河北工业大学 智能康复装置与检测技术教育部工程研究中心,天津 300401
    3.河北工业大学 人工智能与数据科学学院,天津 300401

Abstract: Accurate pain assessment can provide valuable guidance for patient analgesia. To address the problems of low efficiency, time-consuming, and laborious nature of traditional artificial pain assessment, a facial pain assessment algorithm is proposed based on VIT and CNN attention mechanisms to predict pain at multiple levels. Firstly, a facial pain expression acquisition platform is built to extract the video frame sequence and preprocess the data, establishing a pain expression database. Secondly, an improved multi-scale channel attention module is proposed to focus on key feature information. CNN and VIT are connected in parallel as the backbone network to extract more advanced facial local-global features, which are then fed to the long short-term memory network (LSTM) in a temporal manner for pain assessment. Finally, the model performance is verified on the pain expression database. The experimental results show that the precision, recall, F1-score and accuracy of the algorithm are 96.8%, 96.7%, 0.97 and 96.8%, respectively. Compared with other deep learning models, it can more effectively identify pain and make a certain contribution to pain assessment research in the field of rehabilitation.

Key words: pain assessment, facial expression, VIT network, convolutional neural network, attention mechanism

摘要: 准确的疼痛评估可以为病人镇痛提供一定指导,为解决传统人工疼痛评估效率低、费时费力等问题,提出一种基于VIT与CNN注意力机制的面部疼痛评估算法,对疼痛进行多级预测。搭建面部疼痛表情采集平台,提取视频帧序列并进行数据预处理,建立疼痛表情数据库;提出一种改进的多尺度通道注意力模块关注关键特征信息,将CNN和VIT作为主干网络并行连接,提取更高级的面部局部-全局特征,以时序方式输入长短期记忆网络(LSTM)进行疼痛评估;在疼痛表情数据库上进行模型性能验证,实验结果表明,该算法在精确率、召回率、F1分数、准确率指标方面分别达到96.8%、96.7%、0.97、96.8%,与其他深度学习模型相比可更有效识别疼痛,为康复领域疼痛评估研究做出一定贡献。

关键词: 疼痛评估, 面部表情, VIT网络, 卷积神经网络, 注意力机制