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Gestures

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A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition.

Sensors (Basel, Switzerland)
Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be ...

Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Automatically segmenting and classifying surgical activities is an important prerequisite to providing automated, targeted assessment and feedback during surgical training. Prior work has focused almost exclusively on recognizing gestures, o...

Surface-Electromyography-Based Gesture Recognition by Multi-View Deep Learning.

IEEE transactions on bio-medical engineering
Gesture recognition using sparse multichannel surface electromyography (sEMG) is a challenging problem, and the solutions are far from optimal from the point of view of muscle-computer interface. In this paper, we address this problem from the contex...

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture reco...

Who is a better teacher for children with autism? Comparison of learning outcomes between robot-based and human-based interventions in gestural production and recognition.

Research in developmental disabilities
BACKGROUND: Individuals with autism spectrum disorder (ASD) tend to show deficits in engaging with humans. Previous findings have shown that robot-based training improves the gestural recognition and production of children with ASD. It is not known w...

A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition.

PloS one
The surface electromyography (sEMG)-based gesture recognition with deep learning approach plays an increasingly important role in human-computer interaction. Existing deep learning architectures are mainly based on Convolutional Neural Network (CNN) ...

Social Touch Gesture Recognition Using Convolutional Neural Network.

Computational intelligence and neuroscience
Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implemen...

Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery.

International journal of computer assisted radiology and surgery
PURPOSE: With the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work requir...

Smartwatch User Interface Implementation Using CNN-Based Gesture Pattern Recognition.

Sensors (Basel, Switzerland)
In recent years, with an increase in the use of smartwatches among wearable devices, various applications for the device have been developed. However, the realization of a user interface is limited by the size and volume of the smartwatch. This study...

Robot-based intervention may reduce delay in the production of intransitive gestures in Chinese-speaking preschoolers with autism spectrum disorder.

Molecular autism
BACKGROUND: Past studies have shown that robot-based intervention was effective in improving gestural use in children with autism spectrum disorders (ASD). The present study examined whether children with ASD could catch up to the level of gestural p...