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Gestures

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Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review.

Sensors (Basel, Switzerland)
Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human-Computer Interaction (HCI) has been developed to overcome the communication barriers bet...

Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification fa...

Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network.

Sensors (Basel, Switzerland)
Dynamic hand gesture recognition is one of the most significant tools for human-computer interaction. In order to improve the accuracy of the dynamic hand gesture recognition, in this paper, a two-layer Bidirectional Recurrent Neural Network for the ...

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG.

Sensors (Basel, Switzerland)
Electromyography (EMG) is a measure of electrical activity generated by the contraction of muscles. Non-invasive surface EMG (sEMG)-based pattern recognition methods have shown the potential for upper limb prosthesis control. However, it is still ins...

High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network.

Sensors (Basel, Switzerland)
High-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant value...

Convolutional and recurrent neural network for human activity recognition: Application on American sign language.

PloS one
Human activity recognition is an important and difficult topic to study because of the important variability between tasks repeated several times by a subject and between subjects. This work is motivated by providing time-series signal classification...

Hand Gesture Recognition Using Compact CNN Via Surface Electromyography Signals.

Sensors (Basel, Switzerland)
By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers of...

An Optimal Electrical Impedance Tomography Drive Pattern for Human-Computer Interaction Applications.

IEEE transactions on biomedical circuits and systems
In this article, we presented an optimal Electrical Impedance Tomography (EIT) drive pattern based on feature selection and model explanation, and proposed a portable EIT system for applications in human-computer interaction for gesture recognition a...

Finger Gesture Spotting from Long Sequences Based on Multi-Stream Recurrent Neural Networks.

Sensors (Basel, Switzerland)
Gesture spotting is an essential task for recognizing finger gestures used to control in-car touchless interfaces. Automated methods to achieve this task require to detect video segments where gestures are observed, to discard natural behaviors of us...

Improving gesture-based interaction between an assistive bathing robot and older adults via user training on the gestural commands.

Archives of gerontology and geriatrics
BACKGROUND: Gesture-based human-robot interaction (HRI) depends on the technical performance of the robot-integrated gesture recognition system (GRS) and on the gestural performance of the robot user, which has been shown to be rather low in older ad...