4.6 Article

Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli

期刊

SENSORS
卷 21, 期 12, 页码 -

出版社

MDPI
DOI: 10.3390/s21124210

关键词

stress; wearable; sensor; physiological signals; galvanic skin response; GSR; electrodermal activity; EDA; pleasant and unpleasant stimuli; valence; correlation

资金

  1. REMIND project Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020 [734355]
  2. Marie Curie Actions (MSCA) [734355] Funding Source: Marie Curie Actions (MSCA)

向作者/读者索取更多资源

This study aimed to explore the measurement and correlation analysis of stress responses under different stimuli through galvanic skin response, finding that the left fingers and inner side of the right foot are the most promising sites for measuring galvanic skin response.
The galvanic skin response (GSR; also widely known as electrodermal activity (EDA)) is a signal for stress-related studies. Given the sparsity of studies related to the GSR and the variety of devices, this study was conducted at the Human Health Activity Laboratory (H2AL) with 17 healthy subjects to determine the variability in the detection of changes in the galvanic skin response among a test group with heterogeneous respondents facing pleasant and unpleasant stimuli, correlating the GSR biosignals measured from different body sites. We experimented with the right and left wrist, left fingers, the inner side of the right foot using Shimmer3GSR and Empatica E4 sensors. The results indicated the most promising homogeneous places for measuring the GSR, namely, the left fingers and right foot. The results also suggested that due to a significantly strong correlation among the inner side of the right foot and the left fingers, as well as the moderate correlations with the right and left wrists, the foot may be a suitable place to homogenously measure a GSR signal in a test group. We also discuss some possible causes of weak and negative correlations from anomalies detected in the raw data possibly related to the sensors or the test group, which may be considered to develop robust emotion detection systems based on GRS biosignals.

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