AIMC Topic: Gestures

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Machine Learning-Enabled Environmentally Adaptable Skin-Electronic Sensor for Human Gesture Recognition.

ACS applied materials & interfaces
Stretchable sensors have been widely investigated and developed for the purpose of human motion detection, touch sensors, and healthcare monitoring, typically converting mechanical/structural deformation into electrical signals. The viscoelastic stra...

The research of touch screen usability in civil aircraft cockpit.

PloS one
With the advancement of touch screen technology, the application of touch screens in civil aircraft cockpits has become increasingly popular. However, further analysis and research are required to fully promote its applications. The paper researched ...

The Role of Coherent Robot Behavior and Embodiment in Emotion Perception and Recognition During Human-Robot Interaction: Experimental Study.

JMIR human factors
BACKGROUND: Social robots are becoming increasingly important as companions in our daily lives. Consequently, humans expect to interact with them using the same mental models applied to human-human interactions, including the use of cospeech gestures...

Integrated block-wise neural network with auto-learning search framework for finger gesture recognition using sEMG signals.

Artificial intelligence in medicine
Accurate finger gesture recognition with surface electromyography (sEMG) is essential and long-challenge in the muscle-computer interface, and many high-performance deep learning models have been developed to predict gestures. For these models, probl...

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...

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) ...

Exploring the impact of human-robot interaction on workers' mental stress in collaborative assembly tasks.

Applied ergonomics
Advances in robotics have contributed to the prevalence of human-robot collaboration (HRC). However, working and interacting with collaborative robots in close proximity can be psychologically stressful. Therefore, understanding the impacts of human-...

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...

Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi-Analytic Hierarchy Process.

Sensors (Basel, Switzerland)
Existing methods for scoring student presentations predominantly rely on computer-based implementations and do not incorporate a robotic multi-classification model. This limitation can result in potential misclassification issues as these approaches ...

Glove-Based Hand Gesture Recognition for Diver Communication.

IEEE transactions on neural networks and learning systems
We have developed a smart dive glove that recognizes 13 static hand gestures used in diving communication. The smart glove employs five dielectric elastomer sensors to capture finger motion and implements a machine learning classifier in the onboard ...