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Updated: Jun 6, 2026
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Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
Published on: July 22, 2022
Towards a smart glove: arousal recognition based on textile Electrodermal Response
Gaetano Valenza1, Antonio Lanata, Enzo Pasquale Scilingo
1Interdepartmental Research Centre "E. Piaggio" Faculty of Engineering, University of Pisa Via Diotisalvi 2-56126-Italy. g.valenza@iet.unipi.it
Summary
This study shows sensing fabric gloves can reliably detect emotions using Electrodermal Response (EDR). The system accurately classifies arousal states from physiological data, paving the way for practical emotion recognition applications.
Area of Science:
- Physiological computing
- Affective computing
- Wearable sensor technology
Background:
- Electrodermal Response (EDR) is a key psychophysiological indicator of emotional arousal.
- Sensing fabric gloves offer a non-invasive platform for acquiring physiological signals.
- Automatic emotion recognition systems require robust feature extraction and classification methods.
Purpose of the Study:
- To investigate the efficacy of Electrodermal Response (EDR) measured via a sensing fabric glove for emotion recognition.
- To develop and evaluate an automatic system for classifying emotional arousal states.
- To assess the reliability and accuracy of the proposed system.
Main Methods:
- Data acquisition using a sensing fabric glove with integrated textile electrodes.
- Physiological data collection from 35 healthy volunteers.
- Emotion elicitation using the International Affective Picture System (IAPS).
- Feature-based multiclass classification of electrodermal activity.
Main Results:
- The developed system demonstrated high discrimination accuracy in emotion recognition.
- Cross-validation over twenty steps confirmed the robustness of the classification model.
- The sensing fabric glove proved effective for capturing relevant electrodermal response signals.
Conclusions:
- Electrodermal Response (EDR) acquired through sensing fabric gloves is a viable method for emotion recognition.
- The described automatic system provides a reliable approach for classifying arousal-induced emotional states.
- This research contributes to the advancement of wearable technology for affective computing.
