การประยุกต์ใช้ปัญญาประดิษฐ์ในการตรวจจับการกระตุกของกล้ามเนื้อใบหน้า

์Napathorn Punpain

Authors

์Napathorn Punpain Durakit bundit university

Keywords:

Artificial intelligence; Facial muscle twitching; Computer vision; Neuromuscular screening; Deep learning

Abstract

Facial muscle twitching is a subtle physiological signal characterized by short-duration and low-amplitude movements, making it difficult to detect using conventional visual observation. This study aims to systematically review the application of artificial intelligence in facial movement analysis and propose an engineering-based framework for neuromuscular screening systems. PRISMA methodology was employed to screen 320 studies, of which 35 were selected for detailed analysis.

The results indicate that Convolutional Neural Networks (CNN) are the most widely used models (60%), followed by Long Short-Term Memory (LSTM) models (25%) and hybrid CNN-LSTM models (15%). Their average accuracy ranges were 82–86%, 78–84%, and 88–92%, respectively. More than 70% of the reviewed studies still relied on micro-expression datasets such as CASME II, while only a limited number were validated in real-world environments. A six-stage screening framework is proposed to support early detection of neuromuscular abnormalities.

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