# Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review
## 2 Materials and Methods
### Hand State Detection Technology  
As an important communication tool for people, the human hand contains rich contents, and people can understand and transmit information to each other conveniently, intuitively, and naturally. In recent years, with the development and widespread application of computer technology, human-computer interaction technology has thus been rapidly developed and advanced and has become a hot research issue in computer vision.  
Hand state detection technology plays a crucial role in the field of hand rehabilitation robotics. Hand state detection technology can effectively verify that the device designed by the researcher is consistent with the biology of the human hand and the kinematics of the hand to ensure that it will not cause secondary damage to the affected hand. Another application of hand state detection technology, which can be used to drive active conscious hand rehabilitation in the bad hand by performing gesture recognition in the good hand, is both convenient and effective. However, because of the complexity and diversity of hand gestures in time and space, coupled with the fact that the hand is a part of the human body with numerous complex deformations, gesture recognition is an extremely challenging and difficult research topic.  
The generalized hand gesture recognition flow chart is shown in Figure 13. When the user's hand motion signal is obtained, the user's hand is first detected and motion tracked, then feature extraction and analysis are performed by hand trajectory extraction, hand shape feature extraction, and gesture modeling, respectively, and finally gesture recognition is performed. Among them, the part of hand motion analysis within the dashed box is the core content of gesture recognition, so how to get accurate motion analysis is the key to a gesture recognition system.  
Hand motion analysis techniques have been researched and explored by numerous scientists and have led to great achievements and applications. At the beginning of the research, hand motion analysis was conducted through hardware devices interacting with computers, and many devices and sensors were used to study hand motion, such as common optical cameras. Such devices are simple and inexpensive, but the limitations of two-dimensional images make it difficult to locate and segment targets quickly from complex and variable backgrounds. Other kinds of wearable sensor devices, such as electronic gloves, can provide high-precision position information and hand movements that can identify many complex behaviors, but such devices require a lot of precise debugging before use and are not convenient for ordinary people to use and operate. In addition, such devices need to be worn on the body, hindering the natural interaction between people and machines. This, coupled with their expensive price makes ordinary people discouraged. In order to get rid of the reliance on sensors and other devices and give the ordinary a better experience, a large number of researchers began to study how to achieve efficient and friendly natural human-machine interaction without contact.  
According to different technical criteria, hand motion analysis techniques can be divided into different categories. According to the different definitions of recognition target object form, hand gesture recognition can be divided into static hand recognition and dynamic hand recognition; according to the different ways of hand image acquisition, we can divide hand recognition into data glove-based methods and computer vision-based methods. The computer vision-based methods can be divided into recognition methods based on ordinary optical cameras and recognition methods based on depth cameras.