Stress Detection in Video Feed: Utilizing Facial Action Units as Indicators in Various Machine Learning Algorithms
Stress has always been present in our daily lives. This has and always been essential in helping us grow. Yet, once stress becomes too much to handle and exhausts an individual to a point where there is no space for recovery, this stress may develop into the chronic stage. This harmful state of stre...
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2022
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ph-ateneo-arc.ecce-faculty-pubs-11292023-01-27T01:50:29Z Stress Detection in Video Feed: Utilizing Facial Action Units as Indicators in Various Machine Learning Algorithms Llanes, Rizzah Grace Reyes, Rosula SJ Stress has always been present in our daily lives. This has and always been essential in helping us grow. Yet, once stress becomes too much to handle and exhausts an individual to a point where there is no space for recovery, this stress may develop into the chronic stage. This harmful state of stress may lead to vulnerabilities in physical and mental health. It can also affect one's quality of life and can hinder productivity in their everyday tasks and responsibilities in school or at work. Multiple studies have been done to detect stress by gathering physiological signals but these invasive measures may be affected by multiple factors. This research aimed to detect stress using a non-invasive measure of detecting facial actions units in the video setting. This was done by taking a recorded footage of the participant while answering an arithmetic test in a time-limited and competitive environment. Stress levels were validated through the participant's own evaluation of how stressful the test was. Extraction of the facial action units was done by utilizing the OpenFace 2.0 interface. The following machine learning algorithms were used to classify the stress levels: multiple linear regression, support vector machine. Classifier performance was measured through accuracy and F1 score and found that random forest model performed the best in the classification using overall data and person specific data. 2022-11-17T08:00:00Z text https://archium.ateneo.edu/ecce-faculty-pubs/135 https://doi.org/10.1109/ECBIOS54627.2022.9945040 Electronics, Computer, and Communications Engineering Faculty Publications Archīum Ateneo automation Bluetooth localization Internet of Things mesh networks openHAB Biomedical Engineering and Bioengineering Computer Engineering Engineering |
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automation Bluetooth localization Internet of Things mesh networks openHAB Biomedical Engineering and Bioengineering Computer Engineering Engineering Llanes, Rizzah Grace Reyes, Rosula SJ Stress Detection in Video Feed: Utilizing Facial Action Units as Indicators in Various Machine Learning Algorithms |
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Stress has always been present in our daily lives. This has and always been essential in helping us grow. Yet, once stress becomes too much to handle and exhausts an individual to a point where there is no space for recovery, this stress may develop into the chronic stage. This harmful state of stress may lead to vulnerabilities in physical and mental health. It can also affect one's quality of life and can hinder productivity in their everyday tasks and responsibilities in school or at work. Multiple studies have been done to detect stress by gathering physiological signals but these invasive measures may be affected by multiple factors. This research aimed to detect stress using a non-invasive measure of detecting facial actions units in the video setting. This was done by taking a recorded footage of the participant while answering an arithmetic test in a time-limited and competitive environment. Stress levels were validated through the participant's own evaluation of how stressful the test was. Extraction of the facial action units was done by utilizing the OpenFace 2.0 interface. The following machine learning algorithms were used to classify the stress levels: multiple linear regression, support vector machine. Classifier performance was measured through accuracy and F1 score and found that random forest model performed the best in the classification using overall data and person specific data. |
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Llanes, Rizzah Grace Reyes, Rosula SJ |
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Llanes, Rizzah Grace Reyes, Rosula SJ |
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Llanes, Rizzah Grace |
title |
Stress Detection in Video Feed: Utilizing Facial Action Units as Indicators in Various Machine Learning Algorithms |
title_short |
Stress Detection in Video Feed: Utilizing Facial Action Units as Indicators in Various Machine Learning Algorithms |
title_full |
Stress Detection in Video Feed: Utilizing Facial Action Units as Indicators in Various Machine Learning Algorithms |
title_fullStr |
Stress Detection in Video Feed: Utilizing Facial Action Units as Indicators in Various Machine Learning Algorithms |
title_full_unstemmed |
Stress Detection in Video Feed: Utilizing Facial Action Units as Indicators in Various Machine Learning Algorithms |
title_sort |
stress detection in video feed: utilizing facial action units as indicators in various machine learning algorithms |
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Archīum Ateneo |
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2022 |
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https://archium.ateneo.edu/ecce-faculty-pubs/135 https://doi.org/10.1109/ECBIOS54627.2022.9945040 |
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