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Stress State Classification Based on Deep Neural Network and Electrodermal Activity Modeling
Floriana Vasile1, Anna Vizziello1, Natascia Brondino2
1Department of Electrical, Biomedical and Computer Engineering, University of Pavia, 27100 Pavia, Italy.
Sensors (Basel, Switzerland)
|March 11, 2023
Summary
This study introduces a new method for analyzing electrodermal activity (EDA) signals to help caregivers understand the emotional states of autistic individuals. The approach uses synthetic data to train a deep neural network, achieving high accuracy in classifying emotional states and predicting potential aggression.
Area of Science:
- Psychophysiology
- Autism Spectrum Disorder Research
- Machine Learning Applications
Background:
- Electrodermal Activity (EDA) monitoring is increasingly used for remote patient health assessment.
- Accurate detection of emotional states like stress and frustration in autistic individuals is crucial for preventing aggressive behaviors, especially in non-verbal individuals or those with alexithymia.
- Existing EDA signal classification methods often rely on extensive datasets and manual feature extraction.
Purpose of the Study:
- To develop and validate a novel method for analyzing electrodermal activity (EDA) signals.
- To classify the emotional states of autistic individuals, focusing on stress and frustration, to aid caregivers in predicting and preventing aggression.
- To overcome limitations of traditional machine learning approaches by utilizing synthetic data generation for deep neural network training.
Main Methods:
- A novel method employing a model to generate synthetic electrodermal activity (EDA) data.
- Training a deep neural network using the generated synthetic EDA data.
- Classifying EDA signals automatically without requiring a separate feature extraction step.
Main Results:
- The deep neural network achieved 96% accuracy when trained and tested on synthetic EDA data.
- The model demonstrated 84% accuracy when tested on experimental EDA sequences after training on synthetic data.
- The proposed approach shows high performance and feasibility for real-world application.
Conclusions:
- The novel method effectively classifies electrodermal activity (EDA) signals using synthetic data for training deep neural networks.
- This approach offers an automated solution for emotional state assessment in autistic individuals, potentially aiding in aggression prevention.
- The high accuracy achieved demonstrates the viability and effectiveness of using generated synthetic data in psychophysiological research.
