Enhancing Chinese Sign Language Learning through Real-Time Feedback and Immersive Mixed Reality Experiences

Khái niệm cốt lõi

This study proposes an innovative mixed-reality-based Chinese Sign Language teaching and feedback system that utilizes real-time pose reconstruction, action similarity evaluation, and immersive 3D teaching environments to provide learners with an enhanced and effective learning experience.

Tóm tắt

The study presents a comprehensive system for teaching and evaluating Chinese Sign Language using mixed reality (MR) technology. The key components of the system are:

1. 3D Sign Language Reconstruction and Presentation:

   • Extracts spatiotemporal data from real sign language movements and models them to preserve semantic information.
   • Enhances a one-stage 3D human body reconstruction method, focusing on sign language, to achieve real-time and accurate pose estimation.

2. 3D Sign Language Teaching:

   • Utilizes standard sign language data from professional instructors to generate 3D teaching scenes with virtual avatars conducting sign language lessons.
   • Employs pose redirection to drive the avatars, allowing students to interact with them on MR devices.

3. Sign Language Teaching Feedback System:

   • Accepts standard sign language input from teachers and student movements captured in the first stage.
   • Provides feedback through a ternary system evaluation algorithm that assesses action confusion, smoothness, and alignment.
   • The feedback system aims to give students meaningful information to improve their sign language skills.

The study conducted experiments to evaluate the real-time performance and accuracy of the sign language reconstruction algorithm, the credibility and effectiveness of the action feedback algorithm, and the overall effectiveness of the system through user experience testing. The results demonstrate the system's ability to provide an immersive and effective learning experience for Chinese Sign Language.

Thống kê

"The average score for accuracy was 9.32 and the average score for fluency was 8.78, reflecting the advantage of our algorithm in capturing sign language semantics and simulating natural movements."
"The computational results showed an average Spearman correlation coefficient of 0.86 between the expert and platform ratings for the 15 test sets, indicating that the rating algorithm of the platform is highly consistent with the expert ratings of sign language movements."
"Group B participants using the teaching system with evaluation feedback experienced the most significant improvement in learning effectiveness, with an average score increase of 53.67."

Trích dẫn

"This study proposes an innovative sign language teaching model that uses technological means to overcome these limitations."
"Through these innovative methods, this study aims to provide a more efficient and effective new model for sign language learning."
"Our system can robustly assess the sign language skills of users and provide accurate and objective evaluations."

Thông tin chi tiết chính được chắt lọc từ

Teaching Chinese Sign Language with Feedback in Mixed Reality

by Hongli Wen,Y... lúc arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10490.pdf

Yêu cầu sâu hơn

How can the proposed system be further enhanced to provide personalized and adaptive feedback to learners based on their individual progress and needs?

The proposed system can be enhanced to provide personalized and adaptive feedback by incorporating machine learning algorithms that can analyze individual learning patterns and progress. By collecting data on each learner's performance, the system can adapt its feedback mechanisms to cater to the specific needs of each user. This could involve creating personalized learning paths, identifying areas of improvement, and offering targeted feedback to address individual weaknesses. Additionally, integrating natural language processing capabilities can enable the system to understand and respond to user queries and provide tailored feedback based on the user's unique learning style and preferences. By leveraging data analytics and AI technologies, the system can continuously adapt and evolve to meet the diverse needs of learners, ultimately enhancing the effectiveness of the teaching and feedback process.

What are the potential applications of the mixed-reality-based sign language teaching and feedback system beyond the educational context, such as in rehabilitation or accessibility-focused scenarios?

The mixed-reality-based sign language teaching and feedback system has significant potential applications beyond the educational context. In rehabilitation settings, the system can be utilized to assist individuals with speech and hearing impairments in improving their communication skills. By providing real-time feedback on sign language gestures and movements, the system can support speech therapy sessions and help patients enhance their ability to express themselves through sign language. Additionally, in accessibility-focused scenarios, the system can be integrated into public spaces, such as airports or hospitals, to facilitate communication between individuals with hearing impairments and hearing individuals. By using mixed reality technology to display sign language translations or provide interactive sign language tutorials, the system can bridge communication gaps and promote inclusivity in various environments. Overall, the system's applications extend to areas where effective communication through sign language is essential for interaction and engagement.

How can the system's underlying technologies, such as the pose reconstruction and action similarity evaluation algorithms, be leveraged to improve sign language recognition and understanding in broader applications?

The system's underlying technologies, including the pose reconstruction and action similarity evaluation algorithms, can be leveraged to enhance sign language recognition and understanding in broader applications by integrating them into existing sign language recognition systems. The pose reconstruction algorithm, which accurately captures hand movements and gestures, can improve the precision and reliability of sign language recognition software. By incorporating this algorithm into sign language translation apps or devices, users can benefit from more accurate and efficient translation of sign language into text or speech. Additionally, the action similarity evaluation algorithm, which assesses the coherence and accuracy of sign language movements, can be utilized to develop interactive sign language learning tools that provide detailed feedback on users' signing proficiency. This can be particularly useful in language learning apps or virtual reality environments designed to teach sign language to a wide audience. Overall, by leveraging these advanced technologies, sign language recognition systems can be enhanced to better serve the needs of individuals with hearing impairments and promote effective communication through sign language in various applications.