Artificial Intelligence-Oriented User Interface Design and Human Behavior Recognition based on Human–Computer Nature Interaction

计算机科学 卷积神经网络 人工智能 精确性和召回率 残余物 集合(抽象数据类型) 试验装置 背景(考古学) 深度学习 空间语境意识 模式识别(心理学) 机器学习 算法 古生物学 生物 程序设计语言
作者
Xiao Han,Dong Huang,Sang Eun-Lee,Jong Hoon-Yang
出处
期刊:International Journal of Humanoid Robotics [World Scientific]
卷期号:20 (06) 被引量:1
标识
DOI:10.1142/s0219843622500207
摘要

This work is to explore the application of intelligent algorithms based on deep learning in human–computer interaction systems, hoping to promote the development of human–computer interaction systems in the field of behavior recognition. Firstly, the design scheme of the human–computer interaction system is presented, and the establishment of the robot visual positioning system is emphasized. Then, the fast-region convolutional neural networks (fast-RCNN) algorithm is introduced, and it is combined with deep convolutional residual network (ResNet101). A candidate region extraction algorithm based on ResNet and long short-term memory network is proposed, and a residual network (ResNet) for spatial context memory is proposed. Both algorithms are employed in human–computer interaction systems. Finally, the performance of the algorithm and the human–computer interaction system are analyzed and characterized. The results show that the proposed candidate region extraction algorithm can significantly reduce the loss value of training set and test set after training. In addition, the corresponding accuracy, recall, and F-value of the model are all above 0.98, which proves that the model has a good detection accuracy. Spatial context memory ResNet shows good accuracy in speech expression detection. The detection accuracy of single attribute, double attribute, and multi-attribute speech expression is above 89%, and the detection accuracy is good. In summary, the human–computer interaction system shows good performance in capturing target objects, even for unlabeled objects, the corresponding grasping success rate is 95%. Therefore, this work provides a theoretical basis and reference for the application of intelligent optimization algorithm in human–computer interaction system.
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