Webcam-captured facial expressions and head movements reflect a decline in performance during extended periods of being awake.
In a potential study, a combination of a Logitech C920 HD webcam and a platform equipped with Affectiva technology was used to estimate facial indices. The study aimed to evaluate the feasibility of these facial indices as detectors of diminished reaction time during the Psychomotor Vigilance Task (PVT), a measure of cognitive performance under prolonged wakefulness.
The study recorded facial video recordings for 20 participants over 25 hours, using a single Logitech C920 HD webcam placed in front of them, on top of the screen. These recordings were then analyzed using the platform with Affectiva technology to extract 34 facial indices.
Among the significant findings, eye-related facial expression indices showed especially strong correlations and higher feasibility as classifiers. Pitch, from the head movement indices, and four perceived facial emotions—anger, surprise, sadness, and disgust—exhibited significant correlations with indices of performance.
Interestingly, significantly correlated facial indices were shown to explain more variance than the other indices for most of the classifiers. This suggests that these facial indices could potentially serve as valuable indicators of cognitive decline during prolonged wakefulness.
However, it is important to note that a direct comparison of facial features obtained from a Logitech C920 HD webcam with vigilance-task performance during prolonged wakefulness, using Affectiva on a platform for facial index estimation, is not explicitly addressed in the publicly indexed search results.
While further research is necessary to confirm these findings, this study provides an intriguing insight into the potential use of facial indices as detectors of diminished reaction time during prolonged wakefulness. As Affectiva is specialized software for facial expression and emotion analysis often used in vigilance and fatigue research, it is plausible that more studies on this topic exist in academic or applied human factors fields.
For those interested in similar research, it may be beneficial to check specialized academic databases or journals in human factors, cognitive science, or affective computing for relevant studies.
The study found that facial indices, analyzed using Affectiva technology, showed strong correlations with physical indicators such as eye-related expressions, head pitch, and certain perceived emotions. These findings suggest that these facial indices could potentially serve as valuable indicators of cognitive decline during prolonged wakefulness, especially for classifiers.
Furthermore, since Affectiva is commonly used in vigilance and fatigue research, it's likely that more studies exist in academic or applied human factors fields, investigating the potential use of technology like science and industry-standard facial expression and emotion analysis software for detecting diminished reaction time during prolonged wakefulness.
