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Area of Science:

Affective computing
Human-computer interaction
Psychophysiology

Background:

Negative mood states (tension, depression, anger, fatigue, confusion) impair rational decision-making.
Current negative mood state detection relies on inconvenient psychological scales.
A need exists for non-contact, efficient mood detection methods.

Purpose of the Study:

To propose a novel non-contact method for negative mood state detection.
To utilize affective computing principles for mood assessment.
To develop a convenient and effective alternative to traditional methods.

Main Methods:

A novel non-contact negative mood state detection method based on affective computing.
The POMS-net model for extracting temporal-spatial features from visible and infrared thermal videos.
Data reliability-focused multi-modal fusion for mood state detection.

Main Results:

The proposed method was validated on the HDT-BR and VIRI datasets.
Experimental results demonstrated superior performance compared to existing methods.
Successful non-contact detection of negative mood states was achieved.

Conclusions:

The developed non-contact method offers a practical solution for negative mood state detection.
The POMS-net model and multi-modal fusion effectively capture mood-related features.
This approach enhances convenience and efficiency in mood assessment applications.