# Deep Learning for Intelligent Human-Computer Interaction
## 5 Human Gesture Recognition Based on Deep Learning  
A gesture is a form of non-verbal communication that can be used in several fields, such as communication between deaf and mute people, robot control, HCI, home automation, and medical applications. Gesture-based studies employ a number of different techniques, including those based on instrumented sensor technology and computer vision. In other words, gestures are divided into many directions, such as posture and gesture, as well as dynamic and static, or a mixture of both. [PERSON] et al. (2020) [66] listed the performance of these methods, computer vision technology to deal with similarities and differences, hand segmentation technology used, classification algorithm and shortcomings, number and types of gestures, datasets used, and detection range and camera types used. The use of gesture recognition contains many complex technical difficulties. Gestures are often used by people to convey their thoughts and feelings. For example, hearing-impaired groups always rely on sign language to communicate with each other. However, most normal people do not understand the language and face difficulties in communicating with deaf and mute people. Therefore, the development of automated sign language recognition systems can help facilitate this communication and close the gap. The structured style of sign language gestures helps facilitate non-verbal communication between the deaf and the hearing impaired. Sign language recognition problems are classified into two categories, namely, static gesture recognition, which focuses on finger spelling, and dynamic recognition, which is related to the recognition of isolated words and continuous sentences. Many continuous sign language recognition systems utilize extended versions of the isolated word framework to recognize entire sentences. With the rapid development of computer science and related fields, the way humans interact with computers has evolved into a more natural and ubiquitous form. Various techniques were developed to capture a user's facial expressions as well as body movements and postures to serve two types of applications. The captured information becomes a \"snapshot\" of the user so that the computer can better understand the user's intention or emotional state. The user applies natural motion instead of using dedicated input devices to send commands for system control or to interact with digital content in a virtual environment. Gesture interpretation must be carried out quickly and with high accuracy in the vision-based gesture interaction between humans and computers. Human posture estimation is a challenging task in computer vision. It refers to the process of inferring posture in an image, aiming to determine the position or spatial position of a person's body key points from a given image or video. The estimation principle is shown in Figure 4.  
There has been significant progress in addressing the problems and challenges related to human pose estimation aided by deep learning and publicly available datasets. In this survey, [PERSON] and [PERSON] (2020) [67] discussed the characteristics of various machine learning and deep learning techniques of HAR and the public datasets used for HAR and revealed the advantages and disadvantages of action representation, reduction and action analysis methods, as well as the challenges and future directions of HAR. [PERSON] et al. (2020) [68] described the methods used in human pose estimation and then listed some applications and the drawbacks faced in pose estimation. This has established a new research idea for gesture interaction understanding. Therefore, an increasing number of scholars are committed to studying how to optimize human gesture recognition using deep learning, artificial intelligence, and other related technologies in the application process of HCI systems.