Automatic Detection of Acute Mental Stress With Camera-based Photoplethysmography
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Autonomic nervous system (ANS) activity reflects in vital signs that can be measured by means of camera-based photoplethysmography (cbPPG). This work investigates the automatic detection of acute mental stress with cbPPG.
Data from the Dresden Multimodal Biosignal Dataset for the Mannheim Multicomponent Stress Test (DMBD-MMST) covering > 40 h uncompressed facial RGB videos of 56 healthy participants were used for rest vs. stress classification on the basis of nine cbPPG vital signs with decision tree ensembles. Also, the impact of normalization, measurement duration, and color channel combination was investigated.
Best performance for rest (baseline and recovery) vs. stress classification (F1=0.81, Cohen's Kappa κ=0.61) was achieved with normalization, 30 s measurement duration, and vital signs from the green channel and the color channel combination called O3C. Without recovery (baseline vs. stress), this configuration achieved F1=0.97 and κ=0.94. Paired t-tests revealed significant changes from rest (baseline and recovery) to stress in eight of the nine vital signs and the maximum effect size amounted to d=0.73, indicating sympathetic excitation.
Findings from this work are central to the non-contact evaluation of ANS activity. Our results demonstrate that automatic detection of acute mental stress with cbPPG is feasible.
Data from the Dresden Multimodal Biosignal Dataset for the Mannheim Multicomponent Stress Test (DMBD-MMST) covering > 40 h uncompressed facial RGB videos of 56 healthy participants were used for rest vs. stress classification on the basis of nine cbPPG vital signs with decision tree ensembles. Also, the impact of normalization, measurement duration, and color channel combination was investigated.
Best performance for rest (baseline and recovery) vs. stress classification (F1=0.81, Cohen's Kappa κ=0.61) was achieved with normalization, 30 s measurement duration, and vital signs from the green channel and the color channel combination called O3C. Without recovery (baseline vs. stress), this configuration achieved F1=0.97 and κ=0.94. Paired t-tests revealed significant changes from rest (baseline and recovery) to stress in eight of the nine vital signs and the maximum effect size amounted to d=0.73, indicating sympathetic excitation.
Findings from this work are central to the non-contact evaluation of ANS activity. Our results demonstrate that automatic detection of acute mental stress with cbPPG is feasible.
Details
Original language | English |
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Title of host publication | Computing in Cardiology Conference (CinC) |
Publisher | IEEE |
Number of pages | 4 |
Volume | 50 |
ISBN (electronic) | 9798350382525 |
Publication status | Published - 26 Dec 2023 |
Peer-reviewed | Yes |
Conference
Title | 50th Computing in Cardiology conference |
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Abbreviated title | CinC 2023 |
Conference number | 50 |
Duration | 1 - 4 October 2023 |
Website | |
Degree of recognition | International event |
Location | Emory University |
City | Atlanta |
Country | United States of America |
External IDs
Scopus | 85182309956 |
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ORCID | /0000-0003-4012-0608/work/165451914 |
Keywords
Research priority areas of TU Dresden
DFG Classification of Subject Areas according to Review Boards
Subject groups, research areas, subject areas according to Destatis
Sustainable Development Goals
Keywords
- Mental stress, camera-based photoplethysmography, iPPG, non-contact measurement, stress assessment, stress detection