Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...

The scope of this paper is the interpretation of a user’s intention via a video camera and a speech recognizer. In comparison to previous work which only takes into account gesture recognition, we demonstrate that by including speech, system comprehension increases. For the gesture recognition, the user must wear a colored glove, then we extract the velocity of the center of gravity of the hand...

In human-machine interaction scenarios, low latency recognition and reproduction is crucial for successful communication. For reproduction of general gesture classes it is important to realize a representation that is insensitive with respect to the variation of performer specific speed development along gesture trajectories. Here, we present an approach to learning of speed-invariant gesture m...

This paper introduces a framework for gesture and action recognition based on the evolution of temporal gesture primitives, or subgestures. Our work is inspired on the principle of producing genetic variations within a population of gesture subsequences, with the goal of obtaining a set of gesture units that enhance the generalization capability of standard gesture recognition approaches. In ou...

Although many three-dimensional pointing gesture recognition methods have been proposed, the problem of self-occlusion has not been considered. Furthermore, because almost all pointing gesture recognition methods use a wide-angle camera, additional sensors or cameras are required to concurrently perform finger gesture recognition. In this paper, we propose a method for performing both pointing ...

The hand is an important part of the body used to express information through gestures, and its movements can be used in dynamic gesture recognition systems based on computer vision with practical applications, such as medical, games and sign language. Although depth sensors have led to great progress in gesture recognition, hand gesture recognition still is an open problem because of its compl...

In order to incorporate naturalness in the design of Human Computer Interfaces (HCI), it is desirable to develop recognition techniques capable of handling continuous natural gesture and speech inputs. Hidden Markov Models (HMMs) provide a good framework for continuous gesture recognition and also for multimodal fusion [11]. Many different researchers [13, 12, 2], have reported high recognition...

Gesture recognition is a challenging task for extracting meaningful gesture from continuous hand motion. In this paper, we propose an automatic system that recognizes isolated gesture, in addition meaningful gesture from continuous hand motion for Arabic numbers from 0 to 9 in real-time based on Hidden Markov Models (HMM). In order to handle isolated gesture, HMM using Ergodic, Left-Right (LR) ...

We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-ofVisual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is in...

The learning method for hand gesture recognition that compute a space of eigenvectors by Principal Component Analysis(PCA) traditionally require a batch computation step, in which the only way to update the subspace is to rebuild the subspace by the scratch when it comes to new samples. In this paper, we introduce a new approach to gesture recognition based on online PCA algorithm with adaptive...