Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning Analysis
This study investigates the impact of patient stress on COVID-19 screening. An attempt was made to measure the level of anxiety of individuals undertaking rapid tests for SARS-CoV-2. To this end, a galvanic skin response (GSR) sensor that was connected to a microcontroller was used to record the ind...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Biosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6374/15/1/14 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588937541976064 |
|---|---|
| author | Antonios Georgas Anna Panagiotakopoulou Grigorios Bitsikas Katerina Vlantoni Angelo Ferraro Evangelos Hristoforou |
| author_facet | Antonios Georgas Anna Panagiotakopoulou Grigorios Bitsikas Katerina Vlantoni Angelo Ferraro Evangelos Hristoforou |
| author_sort | Antonios Georgas |
| collection | DOAJ |
| description | This study investigates the impact of patient stress on COVID-19 screening. An attempt was made to measure the level of anxiety of individuals undertaking rapid tests for SARS-CoV-2. To this end, a galvanic skin response (GSR) sensor that was connected to a microcontroller was used to record the individual stress levels. GSR data were collected from 51 individuals at SARS-CoV-2 testing sites. The recorded data were then compared with theoretical estimates to draw insights into stress patterns. Machine learning analysis was applied for the optimization of the sensor results. Classification algorithms allowed the automatic reading of the sensor results and individual identification as “stressed” or “not stressed”. The findings confirmed the initial hypothesis that there was a significant increase in stress levels during the rapid test. This observation is critical, as heightened anxiety may influence a patient’s willingness to participate in screening procedures, potentially reducing the effectiveness of public health screening strategies. |
| format | Article |
| id | doaj-art-d640674e37cb4f53a38e4980ec321dfb |
| institution | Kabale University |
| issn | 2079-6374 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj-art-d640674e37cb4f53a38e4980ec321dfb2025-01-24T13:25:26ZengMDPI AGBiosensors2079-63742025-01-011511410.3390/bios15010014Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning AnalysisAntonios Georgas0Anna Panagiotakopoulou1Grigorios Bitsikas2Katerina Vlantoni3Angelo Ferraro4Evangelos Hristoforou5Laboratory of Electronic Sensors, National Technical University of Athens, 15772 Athens, GreeceLaboratory of Electronic Sensors, National Technical University of Athens, 15772 Athens, GreeceLaboratory of Electronic Sensors, National Technical University of Athens, 15772 Athens, GreeceDepartment of History and Philosophy of Science, School of Science, National and Kapodistrian University of Athens, 15771 Athens, GreeceLaboratory of Electronic Sensors, National Technical University of Athens, 15772 Athens, GreeceLaboratory of Electronic Sensors, National Technical University of Athens, 15772 Athens, GreeceThis study investigates the impact of patient stress on COVID-19 screening. An attempt was made to measure the level of anxiety of individuals undertaking rapid tests for SARS-CoV-2. To this end, a galvanic skin response (GSR) sensor that was connected to a microcontroller was used to record the individual stress levels. GSR data were collected from 51 individuals at SARS-CoV-2 testing sites. The recorded data were then compared with theoretical estimates to draw insights into stress patterns. Machine learning analysis was applied for the optimization of the sensor results. Classification algorithms allowed the automatic reading of the sensor results and individual identification as “stressed” or “not stressed”. The findings confirmed the initial hypothesis that there was a significant increase in stress levels during the rapid test. This observation is critical, as heightened anxiety may influence a patient’s willingness to participate in screening procedures, potentially reducing the effectiveness of public health screening strategies.https://www.mdpi.com/2079-6374/15/1/14stress monitoringCOVID-19 screeninggalvanic skin response (GSR)machine learning analysisbiosensorsclassification algorithms |
| spellingShingle | Antonios Georgas Anna Panagiotakopoulou Grigorios Bitsikas Katerina Vlantoni Angelo Ferraro Evangelos Hristoforou Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning Analysis Biosensors stress monitoring COVID-19 screening galvanic skin response (GSR) machine learning analysis biosensors classification algorithms |
| title | Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning Analysis |
| title_full | Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning Analysis |
| title_fullStr | Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning Analysis |
| title_full_unstemmed | Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning Analysis |
| title_short | Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning Analysis |
| title_sort | stress monitoring in pandemic screening insights from gsr sensor and machine learning analysis |
| topic | stress monitoring COVID-19 screening galvanic skin response (GSR) machine learning analysis biosensors classification algorithms |
| url | https://www.mdpi.com/2079-6374/15/1/14 |
| work_keys_str_mv | AT antoniosgeorgas stressmonitoringinpandemicscreeninginsightsfromgsrsensorandmachinelearninganalysis AT annapanagiotakopoulou stressmonitoringinpandemicscreeninginsightsfromgsrsensorandmachinelearninganalysis AT grigoriosbitsikas stressmonitoringinpandemicscreeninginsightsfromgsrsensorandmachinelearninganalysis AT katerinavlantoni stressmonitoringinpandemicscreeninginsightsfromgsrsensorandmachinelearninganalysis AT angeloferraro stressmonitoringinpandemicscreeninginsightsfromgsrsensorandmachinelearninganalysis AT evangeloshristoforou stressmonitoringinpandemicscreeninginsightsfromgsrsensorandmachinelearninganalysis |