Version 1
: Received: 28 June 2024 / Approved: 1 July 2024 / Online: 1 July 2024 (10:07:17 CEST)
How to cite:
Bamonte, M. F.; Risk, M.; Herrero, V. Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals. Preprints2024, 2024070058. https://doi.org/10.20944/preprints202407.0058.v1
Bamonte, M. F.; Risk, M.; Herrero, V. Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals. Preprints 2024, 2024070058. https://doi.org/10.20944/preprints202407.0058.v1
Bamonte, M. F.; Risk, M.; Herrero, V. Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals. Preprints2024, 2024070058. https://doi.org/10.20944/preprints202407.0058.v1
APA Style
Bamonte, M. F., Risk, M., & Herrero, V. (2024). Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals. Preprints. https://doi.org/10.20944/preprints202407.0058.v1
Chicago/Turabian Style
Bamonte, M. F., Marcelo Risk and Victor Herrero. 2024 "Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals" Preprints. https://doi.org/10.20944/preprints202407.0058.v1
Abstract
Automatic emotion recognition using portable sensors is gaining attention due to its potential use in real-life scenarios. Existing studies have not explored Galvanic Skin Response and Photoplethysmography sensors exclusively for emotion recognition using nonlinear features with machine learning (ML) classifiers such as Random Forest, Support Vector Machine, Gradient Boosting Machine, K-Nearest Neighbor, and Decision Tree. In this study, we proposed a genuine window sensitivity analysis on a continuous annotation dataset to determine the window duration and percentage of overlap that optimize the classification performance using ML algorithms and nonlinear features, namely Lyapunov Exponent, Approximate Entropy, and Poincaré’s indices. We found an optimum window duration of 11 seconds and 16 seconds for valence and arousal, respectively, with 50% overlap, and achieved an accuracy of 0.76 in both dimensions. In addition, we proposed a Strong Labeling Scheme that kept only the extreme values of the labels, which raised the accuracy score to 0.92. Under certain conditions mentioned, traditional ML models offer a good compromise between performance and low computational cost. Our results suggest that well-known ML algorithms can still contribute to the field of emotion recognition, provided that window duration, overlap percentage, and nonlinear features are carefully selected.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
