Key result
Fusing behavioral and rPPG facial video features boosts online meeting stress estimation accuracy to ~83%.
Why the study?
Attaching physiological sensors for stress monitoring requires physical presence, highlighting the need for remote stress measurement during online video meetings following the COVID-19 pandemic.
Does the fusion of remote physiological signal and behavioral features improve the accuracy of stress estimation in online meetings?
Does the fusion of remote physiological signal and behavioral features improve the accuracy of stress estimation in online meetings?
Combining remote physiological signals (rPPG) with behavioral features from facial videos provides a highly accurate method for non-contact stress estimation during online meetings.
May enable contactless stress monitoring in telehealth; leaves open validation in clinical CV populations.
Work stress impacts people’s daily lives. Their well-being can be improved if the stress is monitored and addressed in time. Attaching physiological sensors are used for such stress monitoring and analysis. Such approach is feasible only when the person is physically presented. Due to the transfer of the life from offline to online, caused by the COVID-19 pandemic, remote stress measurement is of high importance. This study investigated the feasibility of estimating participants’ stress levels based on remote physiological signal features (rPPG) and behavioral features (facial expression and motion) obtained from facial videos recorded during online video meetings. Remote physiological signal features provided higher accuracy of stress estimation (78.75%) as compared to those based on motion (70.00%) and facial expression (73.75%) features. Moreover, the fusion of behavioral and remote physiological signal features increased the accuracy of stress estimation up to 82.50%.
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Sun et al. (2022) studied Stress. Remote physiological signal features (rPPG) and behavioral features vs. Motion and facial expression features was evaluated on Accuracy of stress estimation. The fusion of behavioral and remote physiological signal features (rPPG) from facial videos increased the accuracy of stress estimation during online meetings up to 82.50%.
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