Feature Fusion Classification for Emotional Intelligence Using Peripheral Signals

Title

Feature Fusion Classification for Emotional Intelligence Using Peripheral Signals

Creator

Preema, P.Y.; Chandra, J.; Angel, C. Steffi

Description

Real-time emotion identification is an innovation in the field of humancomputer interaction, which is an essential and challenging task. The existing studies methods for identifying emotions include face, audio, and physiological signals. The study aims to develop a model for emotion classification to identify and interpret human emotions through skin temperature, respiration, and plethysmography. The study also includes analyzing and interpreting emotional states through ensemble models. The classification is based on the frequency domain signal components extracted using the Fast Fourier Transform (FFT), such as amplitude and frequency, to identify emotional states. Ensemble-based machine learning algorithms such as XGBoost and LGBM achieved the highest accuracy in classifying various emotional states. The study involves unimodal and ensemble methods to analyze the signals. The comparative classification rate of unimodal results with ensemble shows that it is the highest at 85.99%, achieved for sad emotions by XGBoost. Fusing modules like respiration, skin temperature, and plethysmography maintains the accuracy level for all four emotions. The unimodal temperature has the highest accuracy of 86.1% for calm, whereas the fusion model has maintained accuracy for all the emotional states. The feature amplitude is the most promising feature for the classification method, which attains an average of 83.2% for XGBoost. The applications enhance user experiences and contribute valuable help in psychology, mental health care, and HumanComputer Interaction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1294 LNNS;pp.197-209

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

DEAP dataset; Emotion recognition; Frequency domain analysis; Peripheral signals; Signal processing

Coverage

Preema P.Y., Department of Computer Science, Christ Deemed to be University, Karnataka, Bangalore, India; Chandra J., Department of Computer Science, Christ Deemed to be University, Karnataka, Bangalore, India; Angel C.S., Department of Computer Science, Christ Deemed to be University, Karnataka, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981963252-7;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-3253-4_14

https://www.scopus.com/pages/publications/105010645489?origin=resultslist