Hand Posture and Gesture Recognition TechnologyThis section discusses the requirements for hand posture and gesture recognition. It describes the two main solutions for collecting the data needed for recognition, the glove-based solution and the camera or vision-based solution, and discusses the advantages and disadvantages of each.Collecting data on postures and movements hand gesturesThe first step in using posture and hand gestures in computer applications collects raw data. This raw data is then analyzed using various recognition algorithms (see Section 3) to extract meaning or context from the data to perform tasks in the application. Raw data is collected in three ways. The first is to use input devices carried by the user. This setup typically consists of one or two instrumented gloves that measure the different joint angles of the hand and a six degrees of freedom (6DOF) tracking device that collects the position and orientation data of the hand. hand. The second way to collect raw hand data is to use a computer vision-based approach whereby one or more cameras collect images of the user's hands. The cameras capture an arbitrary number of frames per second and send them to image processing routines to perform posture and gesture recognition as well as 3D triangulation to find the position of the hands in space. The third way to collect raw manual data is to combine the previous two methods in a hybrid approach in hopes of achieving a more accurate level of recognition using both data streams to reduce errors from each. Very little work has been done on hybrid tracking of hand posture and gesture recognition, but this type of tracking has seen success in augmented reality systems like Auer[7] and State[98]. and might well be......paper means......tunes the voltage output from each sensor and then changes the value using a linear calibration function. This function uses gain and offset values ​​to represent the slope and intercept of the linear equation. This equation allows for software calibration of the glove and thus makes it more robust for a variety of hand sizes. The author's personal experience and an assessment by Kessler et al. [50] suggest that the CyberGlove is accurate to within one degree of flexion. This works well for recognizing simple and complex postures and gestures (Wexelblat[116] and Fels[35] verify this assertion). The only downside to the CyberGlove is its price; the 18-sensor model is available for $9,800 and the 22-sensor model for $14,500. But even though the glove is expensive, it is the best glove-based technology available for accurate and robust hand posture and gesture recognition..