AIMC Topic: Gestures

Clear Filters Showing 31 to 40 of 264 articles

YOLOv8-G2F: A portable gesture recognition optimization algorithm.

Neural networks : the official journal of the International Neural Network Society
Hand gesture recognition (HGR) is a significant research area with applications in human-computer interaction, artificial intelligence, and more. In the early stage of development of HGR, there are high hardware costs and large usage requirements. To...

sEMG-Based Gesture Recognition via Multi-Feature Fusion Network.

IEEE journal of biomedical and health informatics
The sparse surface electromyography-based gesture recognition suffers from the problems of feature information not richness and poor generalization to small sample data. Therefore, a multi-feature fusion network (MFF-Net) model is proposed in this pa...

Real-Time American Sign Language Interpretation Using Deep Learning and Keypoint Tracking.

Sensors (Basel, Switzerland)
Communication barriers pose significant challenges for the Deaf and Hard-of-Hearing (DHH) community, limiting their access to essential services, social interactions, and professional opportunities. To bridge this gap, assistive technologies leveragi...

Innovative hand pose based sign language recognition using hybrid metaheuristic optimization algorithms with deep learning model for hearing impaired persons.

Scientific reports
Sign language (SL) is an effective mode of communication, which uses visual-physical methods like hand signals, expressions, and body actions to communicate between the difficulty of hearing and the deaf community, produce opinions, and carry signifi...

Gesture Recognition Achieved by Utilizing LoRa Signals and Deep Learning.

Sensors (Basel, Switzerland)
This study proposes a novel gesture recognition system based on LoRa technology, integrating advanced signal preprocessing, adaptive segmentation algorithms, and an improved SS-ResNet50 deep learning model. Through the combination of residual learnin...

Eye-gesture control of computer systems via artificial intelligence.

F1000Research
BACKGROUND: Artificial Intelligence (AI) offers transformative potential for human-computer interaction, particularly through eye-gesture recognition, enabling intuitive control for users and accessibility for individuals with physical impairments.

IoT-driven smart assistive communication system for the hearing impaired with hybrid deep learning models for sign language recognition.

Scientific reports
Deaf and hard-of-hearing people utilize sign language recognition (SLR) to interconnect. Sign language (SL) is vital for hard-of-hearing and deaf individuals to communicate. SL uses varied hand gestures to speak words, sentences, or letters. It aids ...

Convolutional neural network for gesture recognition human-computer interaction system design.

PloS one
Gesture interaction applications have garnered significant attention from researchers in the field of human-computer interaction due to their inherent convenience and intuitiveness. Addressing the challenge posed by the insufficient feature extractio...

SignFormer-GCN: Continuous sign language translation using spatio-temporal graph convolutional networks.

PloS one
Sign language is a complex visual language system that uses hand gestures, facial expressions, and body movements to convey meaning. It is the primary means of communication for millions of deaf and hard-of-hearing individuals worldwide. Tracking phy...

A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning.

Sensors (Basel, Switzerland)
Causal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focu...