融合VIT与CNN注意力机制的面部疼痛评估算法研究

Facial Pain Assessment Algorithm Fusing VIT and CNN Attention Mechanism

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。

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 algo-rithm 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 preci-sion,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.