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Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication
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Perspective and Evolution of Gesture Recognition for Sign Language: A Review.
Jesús Galván-Ruiz1,2, Carlos M Travieso-González1,2, Acaymo Tejera-Fettmilch1
1IDeTIC, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de G.C., Spain.
Sensors (Basel, Switzerland)
|July 1, 2020
Summary
This review analyzes gesture recognition systems, highlighting the Leap Motion sensor's potential for sign language due to its 3D hand tracking capabilities.
Area of Science:
- Computer Science
- Human-Computer Interaction
- Biomedical Engineering
Background:
- Gesture recognition systems are crucial for intuitive human-computer interaction.
- Advancements in sensor technology have enabled more sophisticated gesture analysis.
- Sign language recognition remains a challenging but important application area.
Purpose of the Study:
- To review and analyze various gesture recognition technologies over time.
- To identify key features and performance metrics of different systems.
- To evaluate the suitability of the Leap Motion sensor for sign language applications.
Main Methods:
- A comprehensive literature review of gesture recognition systems.
- Timeline analysis of technological evolution in gesture recognition.
- Detailed examination of Leap Motion sensor capabilities and specifications.
Main Results:
- Identified key features and recognition rates across different gesture recognition technologies.
- Highlighted the Leap Motion sensor's strengths for gesture capture.
- Confirmed the sensor's ability to provide essential 3D hand data.
Conclusions:
- The Leap Motion sensor shows significant promise for sign language recognition.
- Its 3D hand tracking is a critical feature for accurate sign language interpretation.
- Further research into Leap Motion for sign language applications is warranted.
