Earticle

초록

영어

We investigate Autism Spectrum Disorder (ASD), which is typified by deficits in social interaction, repetitive behaviors, limited vocabulary, and cognitive delays. Traditional diagnostic methodologies, reliant on expert evaluations, frequently result in deferred detection and intervention, particularly in South Korea, where there is a dearth of qualified professionals and limited public awareness. In this study, we employ advanced deep learning algorithms to enhance early ASD screening through automated video analysis. Utilizing architectures such as Convolutional Long Short-Term Memory (ConvLSTM), Long-term Recurrent Convolutional Network (LRCN), and Convolutional Neural Networks with Gated Recurrent Units (CNN+GRU), we analyze video data from platforms like YouTube and TikTok to identify stereotypic behaviors (arm flapping, head banging, spinning). Our results indicate that the LRCN model exhibited superior performance with 79.61% accuracy on the augmented platform video dataset and 79.37% on the original SSBD dataset. The ConvLSTM and CNN+GRU models also achieved higher accuracy than the original SSBD dataset. Through this research, we underscore AI's potential in early ASD detection by automating the identification of stereotypic behaviors, thereby enabling timely intervention. We also emphasize the significance of utilizing expanded datasets from social media platform videos in augmenting model accuracy and robustness, thus paving the way for more accessible diagnostic methods.

목차

Abstract
1. INTRODUCTION
2. LITERATURE REVIEW
3. METHOD
3.1 ConvLSTM Approach
3.2 LRCN Approach
3.3 CNN+GRU Approach
4. DATESET/EXPERIMENT
4.1 Data
4.2 Preprocessing
4.3 Experiment
5. DISCUSSON
6. CONCLUSION
REFERENCES

키워드

저자

• Seongwoo Park [ Department of Global Entrepreneurship and Information Communication Technology, Handong Global University, Pohang, Korea ]
• Sukbeom Chang [ Department of Global Entrepreneurship and Information Communication Technology, Handong Global University, Pohang, Korea ]
• JooHee Oh [ Department of Management and Economics, Handong Global University, Pohang, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700