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Gestures

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LSTM-MSA: A Novel Deep Learning Model With Dual-Stage Attention Mechanisms Forearm EMG-Based Hand Gesture Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper introduces the Long Short-Term Memory with Dual-Stage Attention (LSTM-MSA) model, an approach for analyzing electromyography (EMG) signals. EMG signals are crucial in applications like prosthetic control, rehabilitation, and human-computer...

Liquid Metal Flexible EMG Gel Electrodes for Gesture Recognition.

Biosensors
Gesture recognition has been playing an increasingly important role in the field of intelligent control and human-computer interaction. Gesture recognition technology based on electromyography (EMG) with high accuracy has been widely applied. However...

Deep Learning Technology to Recognize American Sign Language Alphabet.

Sensors (Basel, Switzerland)
Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. However, advancements in modern technology have paved the way for the development of various tools and software ...

A Unified Multimodal Interface for the RELAX High-Payload Collaborative Robot.

Sensors (Basel, Switzerland)
This manuscript introduces a mobile cobot equipped with a custom-designed high payload arm called RELAX combined with a novel unified multimodal interface that facilitates Human-Robot Collaboration (HRC) tasks requiring high-level interaction forces ...

Novel Wearable HD-EMG Sensor With Shift-Robust Gesture Recognition Using Deep Learning.

IEEE transactions on biomedical circuits and systems
In this work, we present a hardware-software solution to improve the robustness of hand gesture recognition to confounding factors in myoelectric control. The solution includes a novel, full-circumference, flexible, 64-channel high-density electromyo...

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot.

Sensors (Basel, Switzerland)
This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to devel...

Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
With the goal of promoting the development of myoelectric control technology, this paper focuses on exploring graph neural network (GNN) based robust electromyography (EMG) pattern recognition solutions. Given that high-density surface EMG (HD-sEMG) ...

From Forearm to Wrist: Deep Learning for Surface Electromyography-Based Gesture Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Though the forearm is the focus of the prostheses, myoelectric control with the electrodes on the wrist is more comfortable for general consumers because of its unobtrusiveness and incorporation with the existing wrist-based wearables. Recently, deep...

Continuous Gesture Control of a Robot Arm: Performance Is Robust to a Variety of Hand-to-Robot Maps.

IEEE transactions on bio-medical engineering
OBJECTIVE: Despite advances in human-machine-interface design, we lack the ability to give people precise and fast control over high degree of freedom (DOF) systems, like robotic limbs. Attempts to improve control often focus on the static map that l...

Seeking a Hierarchical Prototype for Multimodal Gesture Recognition.

IEEE transactions on neural networks and learning systems
Gesture recognition has drawn considerable attention from many researchers owing to its wide range of applications. Although significant progress has been made in this field, previous works always focus on how to distinguish between different gesture...