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Psychological stability is crucial for well-being and cognitive function. With rising mental health concerns, automated systems are needed to assess stability effectively. This study presents an AI-driven framework analyzing behavioral and physiological factors using machine learning. The system processes data like speech patterns, facial expressions, and biometric indicators to detect distress. By integrating feature extraction and classification models, it enhances assessment accuracy. Applications include workplace stress monitoring and healthcare diagnostics, supporting mental health professionals through AI-driven analysis.

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Designing a System to Detect Psychological Stability Status Using AI-Driven Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Dharna Shravani,
  • J. Ashok Kumar,
  • N. Parimala,
  • Kudupudi Suchitha,
  • P. Repudi Nehemiah

摘要

Psychological stability is crucial for well-being and cognitive function. With rising mental health concerns, automated systems are needed to assess stability effectively. This study presents an AI-driven framework analyzing behavioral and physiological factors using machine learning. The system processes data like speech patterns, facial expressions, and biometric indicators to detect distress. By integrating feature extraction and classification models, it enhances assessment accuracy. Applications include workplace stress monitoring and healthcare diagnostics, supporting mental health professionals through AI-driven analysis.