An emotion recognition method based on frequency-domain features of PPG.

ABSTRACT:

Objective

This study aims to employ physiological model simulation to systematically analyze the frequency-domain components of PPG signals and extract their key features. The efficacy of these frequency-domain features in effectively distinguishing emotional states will also be investigated.

Methods

A dual windkessel model was employed to analyze PPG signal frequency components and extract distinctive features. Experimental data collection encompassed both physiological (PPG) and psychological measurements, with subsequent analysis involving distribution patterns and statistical testing (U-tests) to examine feature-emotion relationships. The study implemented support vector machine (SVM) classification to evaluate feature effectiveness, complemented by comparative analysis

SUBMITTER: Zhu Z 

PROVIDER: S-EPMC11893849 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature