Deciphering Hand Movements in Individuals with Limited Mobility Using Neural Networks

摘要

The identification and detection of hand motions is the focus of this project. Using a web camera, hand gesture photographs are captured. These images are then compared to database images, with the best match returned. In order to create user-friendly interfaces, gesture recognition is one of the most important strategies. For instance, a robot that can identify hand gestures can accept commands from people. Similarly, a robot that can understand sign language would enable people who are deaf or hard of hearing to communicate with it. Recognition of hand gestures may make it possible to use a controller-free application to interact with the system by gestures rather than words. Such an algorithm must be more resilient to consider the plethora of alternative hand locations in three-dimensional space. Using a webcam and computer vision technologies, such as image processing, that can recognize multiple movements for use in computer interface interaction, this research proposes a method for developing a real-time hand gesture recognition system based on “Vision-Based.” Real-time hand gesture recognition has a wide range of practical applications since it can be utilized practically wherever that computer is used. We can open various programs in this project, including word processing and notepad. We used the convolutional neural network approach based on finger curves to invoke apps error-free.