As human activity and gesture recognition has become one of the growing domains of ambient intelligence, the authors proposed convolutional neural networks (CNN) and the recurrent …

Fig. 1: Block diagram of the procedure followed in this CNN technique The data-glove based strategies use sensor gadgets for digitizing hand and finger movements into multi-parametric …

Gestural communication is a type of nonverbal communication in which visible body gestures are utilised to communicate vital messages, either in place of speech

In this project, the use of convolutional neural networks (CNNs) in an algorithm for real-time hand gesture recognition has been proposed in this paper. On a dataset of thirty-six hand gestures …

This project implements multiple deep learning models to recognize hand gestures using a dataset of grayscale images. The supported architectures include CNN, LeNet-5, AlexNet, and …

Mar 13, 2021 · The framework involves using a miniature sensor that captures Doppler signatures of 14 types of hand gestures which are further classified using a deep convolutional neural …

A large-scale image classification dataset, and fine-tunes it on a smaller sEMG hand gesture dataset. The authors show that the pre-trained CNN can learn robust feature representations …

ABSTRACT and computers, gesture recognit ion does play a critical role. By using modified convolutional neural network (CNN) and modified Recurrent Neural Networks (RNN) models …

Block diagram of the CNN. [...] We propose a hand gesture recognition technique using a convolutional neural network applied to radar echo I-Q plot trajectories. The proposed …

ect gestures and (2) a classifier which is a deep CNN to classify the detected gestures. In order to evaluate the single-time activations of the detected gestures, we propose to use Levenshtein …