#gesture-recognition News & Analysis

6 articles tagged with #gesture-recognition. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · Jun 236/10

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SignVLA: Real-Time Sign Language-Guided Robotic Manipulation via Attention LSTM and Vision-Language-Action Models

Researchers introduce SignVLA, a real-time framework enabling robots to understand and execute manipulation tasks through sign language instructions. The system combines hand-landmark extraction, attention-enhanced LSTM networks, and vision-language-action models to create an accessible human-robot interaction interface for deaf and speech-impaired users.

AINeutralarXiv – CS AI · Jun 26/10

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MyoSem: Aligning Electromyography to Natural-Language Action Semantics for Hand Action Understanding

MyoSem is a new framework that aligns electromyography (EMG) signals with natural language descriptions to enable semantic understanding of hand actions. Rather than classifying gestures into fixed categories, the system allows bidirectional retrieval between EMG signals and text queries, demonstrating strong generalization across users and action types.

AINeutralarXiv – CS AI · May 116/10

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UNCOM: Zero-shot Context-Aware Command Understanding for Tabletop Scenarios

UNCOM is a zero-shot framework that enables robots to understand natural human commands in tabletop environments by integrating speech, gestures, and scene context without requiring task-specific training data. The system achieves 82.39% success rate on real-world interaction scenarios, demonstrating practical viability for general-purpose domestic robotics applications.

AIBullisharXiv – CS AI · Feb 276/103

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SignVLA: A Gloss-Free Vision-Language-Action Framework for Real-Time Sign Language-Guided Robotic Manipulation

Researchers have developed SignVLA, the first sign language-driven Vision-Language-Action framework for human-robot interaction that directly translates sign gestures into robotic commands without requiring intermediate gloss annotations. The system currently focuses on real-time alphabet-level finger-spelling for robotic control and is designed to support future expansion to word and sentence-level understanding.

AIBullishApple Machine Learning · Mar 35/102

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EMBridge: Enhancing Gesture Generalization from EMG Signals through Cross-Modal Representation Learning

EMBridge is a new AI framework that enhances gesture recognition from EMG biosignals by aligning them with high-quality structured data from videos and images. The technology enables zero-shot gesture generalization on low-power wearable devices, potentially advancing human-computer interaction applications.

AINeutralarXiv – CS AI · Mar 24/106

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Interpretable Multimodal Gesture Recognition for Drone and Mobile Robot Teleoperation via Log-Likelihood Ratio Fusion

Researchers developed a multimodal gesture recognition system using Apple Watch sensors and custom gloves for hands-free drone and robot control in hazardous environments. The framework achieves performance comparable to vision-based systems while being more computationally efficient and robust to environmental conditions.