西安理工大学学报2024，Vol.40Issue(2)：253-259,290,8.DOI:10.19322/j.cnki.issn.1006-4710.2024.02.011
基于CNN-BiLSTM-SA网络的人类活动识别
Human activity identification based on CNN-BiLSTM-SA network
摘要
Abstract
In view of the problem that the traditional neural network is not accurate in recognizing human activities,this paper proposes a hybrid network model based on the two-channel mechanism of convolu-tional neural network superimposed with bi-directional long short-term memory network and self-atten-tion(CNN-BiLSTM-SA).First,the acceleration and angular velocity data in the data set are used as the two inputs of the network,and then the system is built by using the convolution neural network to over-lay the bidirectional short-term and short-term memory network;finally,the self-attention mechanism is introduced to enhance the classification ability of the system.The experimental results show that in the UCI-HAR data set,the average F1 score of this network is 98.6％,and that the average accuracy is 98.4％,which is faster than the convolutional neural network-long short-term memory(CNN-LSTM)convergence speed with the accuracy increased by 4.2％,and having a broader application prospect in human activity recognition.关键词
人类活动识别/传感器/CNN-BiLSTM/自注意力机制Key words
human activity recognition/sensor/CNN-BiLSTM/self attention分类
信息技术与安全科学引用本文复制引用
王赛,张立新,陈乃源,阚希,王军昂,吴凯枫..基于CNN-BiLSTM-SA网络的人类活动识别[J].西安理工大学学报,2024,40(2):253-259,290,8.基金项目
国家自然科学基金资助项目(42105143) （42105143）
江苏省教育厅,江苏省高等学校基础科学(自然科学)研究资助项目(580221016) （自然科学）
