# Deep Learning for Intelligent Human-Computer Interaction
## 2 Adoption Status of Deep Learning in Intelligent HCI  
HCI mainly analyzes the exchange of information between human actions and computers. HCI is a comprehensive research field associated with cognitive psychology, ergonomics, multimedia, and VR [28]. The information exchange of HCI relies on interactive devices, including human-computer interactive devices and computer-human interactive devices [29; 30; 31]. HCI devices include the keyboard, mouse, joystick, joystick, data suit, position tracker, data glove, and pressure pen. Computer-human interaction devices include printers, plotters, monitors, helmet-mounted monitors, and speakers. The progression process of HCI involves voice interaction technology, image recognition, Augmented Reality (AR), and VR, as well as somatosensory interaction technology, which has become popular in recent years [32; 33; 34]. Among the four types of technologies, voice interaction is the one with the highest input efficiency and the most natural interaction mode, which can easily broaden the adoption scenarios of products. Image recognition is applied in the field of automatic driving and security for road condition recognition and human face recognition. AR and VR provide immersive experiences, not only for interaction but also for display and movement [35; 36]. People can directly use their body movements to interact with the surrounding devices or environment via motion sensing without using any complex control devices so that people can interact in an immersive way. HCI is changing with the progression of science and technology (Figure 1). Figure 1: The history of HCI. Deep learning is a new research direction in machine learning. In recent years, it has achieved relatively successful development in image recognition and retrieval, language information processing, and speech recognition [37; 38; 39]. Other approaches include context-aware systems, behavior synthesis in user modeling studies or embedded dialog agents, and natural speech processing, all of which rely on deep learning to support human interactions with intelligent systems. Deep learning adoptions are based on building models that mimic the neural connections of the human brain, which processes images, sound, and text signals when data features are described hierarchically through multiple transformation stages, and then data interpretation is provided [40]. It is a technology that enables machine learning, which today is mostly neural networks. Neural networks are inspired by the human brain and are the interconnections between neurons [41; 42; 43]. The adoption of deep learning in HCI can not only improve the accuracy of speech recognition and image recognition but also enhance the realism of interaction [44]. Language understanding is a technique that explores the language problem of HCI. Unlike speech recognition, which converts speech signals into text or corresponding commands, language understanding involves making machines understand human language. People use the computer in the language they are most accustomed to and no longer need to spend time and energy learning various computer languages, therefore language understanding technology is relatively difficult. Sensors have gradually been widely used due to the trend of environmental digitization brought by the Internet of Things (IoT) technology [45; 46; 47]. Content media, objects in the environment, and humans themselves are all going through a digitalized process. Interaction design is particularly important and how to create and form a natural HCI will become an important proposition. Whether it is system adoptions, intelligent devices, and scenes in the environment, it will tend to be a more natural, easy, and humanized HCI, which appears when the user has an appropriate help guide and therefore does not require the user to have rigid memory function or immediate operation understanding.