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Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...
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Real-Time American Sign Language Interpretation Using Deep Learning and Keypoint Tracking.

Bader Alsharif1,2, Easa Alalwany3, Ali Ibrahim1

  • 1Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA.

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Summary
This summary is machine-generated.

This study introduces a real-time American Sign Language (ASL) interpretation system using AI. The technology enhances communication accessibility for the Deaf and Hard-of-Hearing (DHH) community.

Keywords:
AI for accessibilityMediaPipeYOLO11assistive technologydeep learninghuman–computer interactionreal-Time ASL recognitiontransfer learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Communication barriers significantly impact the Deaf and Hard-of-Hearing (DHH) community.
  • Assistive technologies, particularly those using AI and deep learning, are crucial for bridging these communication gaps.
  • Existing solutions often face challenges with accuracy, real-time processing, and environmental adaptability.

Purpose of the Study:

  • To develop and evaluate a real-time American Sign Language (ASL) interpretation system.
  • To enhance communication accessibility and inclusivity for the DHH community.
  • To leverage deep learning and keypoint tracking for accurate ASL recognition.

Main Methods:

  • Integration of the YOLOv11 model for robust gesture recognition.
  • Utilized MediaPipe for precise hand and keypoint tracking.
  • Developed a system for real-time identification of ASL alphabet letters and spelling.

Main Results:

  • Achieved high accuracy in real-time ASL alphabet recognition.
  • Demonstrated a mean Average Precision (mAP@0.5) of 98.2%.
  • Optimized inference speed for practical, real-world deployment.

Conclusions:

  • AI-driven assistive technology plays a vital role in empowering the DHH community.
  • The developed system offers a practical solution for seamless communication.
  • This research promotes greater inclusivity through advanced interpretation technology.