Real-time hand gesture recognition based on multi-connect architecture associative memory in human computer interaction

What is it about?

Human-computer interaction relies heavily on the ability to understand and respond to human signs, and the interpretation of hand gestures is a fundamental part of this. It's because there's a strong need to make interactions between people and machines feel more natural. This is due to the intense desire to make communication between humans and computers or other devices natural. This recognition makes it possible for computers to capture and understand hand motions. Recognizing hand gestures under computer vision is crucial for facilitating communication between the deaf and dumb community and normal people, as well as between the elderly who cannot wear hand gloves or sensors and their caregivers. As a result, there is an urgent need for a system with accuracy and the ability to recognize these gestures, as the problem of inaccurately recognizing hand gestures may hurt the human community that relies on gestures to transmit their desires. Given the importance of applications for hand gesture recognition, a completely new method has been developed for recognizing hand gestures and applying it to the hand gesture data set. The study's goal is to illuminate the most crucial steps in the hand gesture detection process. which is the process of detection and recognition of hand gestures by using hand landmark points for tracking hands and using the multi-connected architecture of associative memory as a new trend in the recognition phase. The proposed method gave high accuracy in real-time and showed promising outcomes, with accuracy for American, Chinese, and Arabic sign language and numbers at 95.42%, 92.13%, 93.55%, and 94%, respectively.

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Why is it important?

The aim of this research is to use associative memory to recognize hand gestures in real time. The main research question is: "What is the best and most efficient technique for recognizing hand gestures in real-time as a new trend?" To answer this question, a machine learning approach has been applied in the hand tracking and segmentation stage using two modules to determine the hand landmark points. The method of detecting the skeleton of the hand gave the best results. In addition to the new method, it used Multi-Connect Architecture (MCA) associative memory for classifying the hand gesture. As an interesting trend, associative memory was used for the first time with the recognition of hand gestures.