DEEP LEARNING - BASED METHOD FOR RECOGNIZING GESTURES
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Abstract
Human-computer interaction is becoming increasingly prevalent around us as a result of the swift advancement of science and technology. A new branch of study called human motion analysis and recognition based on attitude sensors has significant advantages and practical improvements over motion recognition based on video. In this study, we provide a brand-new approach based on temporal gesture recognition. The characteristics of gestures are retrieved and categorised using recurrent neural networks and their variation networks by examining the kinematics of gestures. Over 98% accuracy was attained using the procedures across 16 experimenters. The outcomes demonstrate the algorithm's speedy and precise ability to recognise motions
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