# Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review
## 2 Materials and Methods
### Hand State Detection Technology
#### 2.5.2 Data Glove-Based Approach and Computer Vision-Based Approach  
Regarding the research related to hand motion recognition technology, the initial research mainly focused on inventing a special hardware device for interacting with computers, such as data gloves, i.e., the user needs to wear a pair of sensors with a shape similar to ordinary gloves, and the computer uses this device and position tracking technology to measure the trajectory and timing information of hand motion in three-dimensional space and obtain rich hand position, finger bending degree and other hand motion information. The advantages of the data glove-based hand motion recognition system are the high recognition rate of the system and the ease of implementation of the technology. Many studies have been conducted using typical sensing device approaches, such as data gloves. For example, [PERSON] et al. at National Taiwan University used a single VPL company's data glove as an input device and technically recognized 250 basic words in a Taiwanese gesture textbook with a recognition rate of 90.5% [120]. [PERSON] and [PERSON] et al. at Carnegie I Mellon University completed a gesture control system that can manipulate robots using gestures using data gloves in 1995 [121]. [PERSON] used PowerGloves as a hand input device to recognize word sets consisting of 95 isolated words with a correct rate of 80% [122]. [PERSON] and [PERSON] studied the recognition of 53 sign languages with a probability of 89.9% by combining data gloves and gesture recognition, using a position locator and three cameras perpendicular to each other as input devices [123]. As the above experiments and applications require the user to bring a special glove device, which hinders the natural interaction between people and machines, coupled with its high price to discourage ordinary people, after that, scientists are committed to marking hand research, that is, by placing specific markings on the hand, such as in the wrist and fingers stickers or painted with special color stripes, the computer recognizes the movement of such colors to identify the corresponding action. For example, [PERSON] and [PERSON] used colored gloves with highlighted markings between the fingers as input to a hand motion recognition system, resulting in the recognition of seven different hand motions [124]. While this approach brought breakthroughs and convenience in recognition technology, it also caused a great deal of trouble for the user and hindered truly natural human-computer interaction. Finally, scientists finally focused their attention and attention on the natural hand, and through dedicated hardware devices detached from the human hand and offline training, some researchers succeeded in studying a vision-based hand motion recognition system. In terms of general optical camera-based hand motion recognition methods, some of the more representative research results include [PERSON] et al. in which American gestures in which 40 short sentences with lexical words are randomly composed with a final recognition rate of up to 99.2% [125]. [PERSON] and [PERSON] recognized 262 isolated words by extracting features from video footage and then using Hidden Markov ([PERSON] and [PERSON] et al. proposed a hand motion recognition system based on directional histograms, and Canesta, Inc. of San Jose, CA, USA, introduced a personal handheld computer (PDA) in 2004, which uses a 3D image above the keyboard to recognize human movements on the keyboard to control the input of the machine [126]. Currently, there has been much research on hand motion analysis methods in the field of human-computer interaction, but due to the complex multilateral nature of human hands, how to achieve effective and accurate hand segmentation and fingertip recognition is still a problem to be improved and solved.