Interpretable Video based Stress Detection with Self-Refine Chain-of-thought Reasoning

• URL: http://arxiv.org/abs/2410.09449v2
• Date: Mon, 25 Nov 2024 04:57:04 GMT
• Title: Interpretable Video based Stress Detection with Self-Refine Chain-of-thought Reasoning
• Authors: Yi Dai,
• Abstract summary: We propose a novel interpretable approach for video-based stress detection. Our method focuses on extracting subtle behavioral and physiological cues from video sequences that indicate stress levels. We evaluate our approach on several public and private datasets, demonstrating its superior performance in comparison to traditional video-based stress detection methods.
• Score: 4.541582055558865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stress detection is a critical area of research with significant implications for health monitoring and intervention systems. In this paper, we propose a novel interpretable approach for video-based stress detection, leveraging self-refine chain-of-thought reasoning to enhance both accuracy and transparency in decision-making processes. Our method focuses on extracting subtle behavioral and physiological cues from video sequences that indicate stress levels. By incorporating a chain-of-thought reasoning mechanism, the system refines its predictions iteratively, ensuring that the decision-making process can be traced and explained. The model also learns to self-refine through feedback loops, improving its reasoning capabilities over time. We evaluate our approach on several public and private datasets, demonstrating its superior performance in comparison to traditional video-based stress detection methods. Additionally, we provide comprehensive insights into the interpretability of the model's predictions, making the system highly valuable for applications in both healthcare and human-computer interaction domains.

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