_Applied sciences_  
_Article_  
Evaluating the Influence of Room Illumination on  
Camera-Based Physiological Measurements for the Assessment of Screen-Based Media  
[PERSON],*,+\({}^{\dagger}\), [PERSON] and [PERSON],+\({}^{\ddagger}\)  
reasons: screen-based media viewing is a significant sociological phenomenon, as evidenced by a 2022 survey conducted by Ofcom, which reported that individuals in the United Kingdom spend an average of 5 h and 16 min per day watching television and video content across all devices [5]; and screen technologies offer a convenient means of delivering experimental stimuli. Studies have demonstrated that psychophysiological methods can provide insights that traditional methods cannot when assessing audiovisual quality [6]. [PERSON] [7] observed a significant increase in heart rate (HR) and galvanic skin response associated with viewing screen-based media rendered at 5 frames per second (FPS), compared to 25 FPS. This was despite only 16% of the participants noting a degradation in quality. Moreover, [PERSON] also states that audio degradation was detectable through similar analyses of these physiological modalities. These results exemplify why many believe psychophysiological methods hold promise as a better approach for subjective audiovisual quality assessment, compared to customary methods which rely solely on self-assessment, and therefore may enable the development of more effective objective audiovisual quality assessment methods [8; 9; 10; 11; 12; 13]. Narrative engagement can also be effectively assessed using psychophysiological methods. Research has demonstrated that when individuals watch screen-based media, changes in physiological features such as heart rate and gaze position correlate between those who feel engaged [14; 15; 16]. Psychophysiological methods have also been used extensively for assessing experiences in the domain of extended reality research [17]. [PERSON] et al. [18] applied contact sensors to measure facial muscle activity, body movement, eye movements, skin temperature, and respiratory, cardiovascular, and neural activity. Various feature extraction techniques and machine learning algorithms were then applied to these input modalities, with the aim of developing a system capable of predicting emotion. The results suggested that emotions could reliably be predicted using this approach, however, the choice of features had a significant effect on the accuracy of prediction. Camera-based physiological measurement systems have also been used in psychophysiological research. In [PERSON] et al. 's 2023 review of the use of visual data in affective computing, 30 quality-screened studies associated with facial expression and/or body gesture-based emotion recognition were found [19]. The application of camera-based heart rate measurement systems has also been explored by researchers investigating emotional responses to playing games [20] and viewing pictures [21]. This camera-based approach to psychophysiological research may be considered advantageous when compared to methods which use contact sensors, as a camera can be a more discrete way of capturing insightful data. This suggests that camera-based methods have the potential to provide more ecologically valid results when compared to contact sensors. Another positive aspect of using a camera-based system is the ease of making a video recording, compared to a contact sensor recording. One negative aspect of using camera-based systems is that they are considered to have poor robustness to many interpersonal and environmental factors--such as low room illumination (see Section 2). Within the broader field of psychophysiological research, multiple sensors are often used to provide a rich dataset for gaining psychological insight [6; 7; 15; 17; 18; 22; 23; 24; 25; 26]. However, few studies use multimodal data extracted from a camera despite the technological feasibility.