This study investigates the phenomenon of magnification illusion, where the perception of perspective and optical slant angles changes when a scene is magnified. Our findings indicate that magnification influences these angles differently depending on location, which suggests that the illusion might be caused by changes in three-dimensional (3D) interpretation. Our findings reveal that the change of perspective angle interpretation primarily occurred when the stimuli were on the ground and sidewall but not those on the ceiling. Specifically, stimuli on the ceiling exhibit a significant underestimation of optical slant angles, while the perspective angle remains relatively stable. We developed a mathematical model based on the hypothesis of changes in 3D interpretation, which aligns well with our data. It was found that the interpretation of the perspective angle and the optical slant angle changes when a scene is magnified as indicated by the proposed relationship. This research provides the characteristics underlying spatial perception and its alteration under magnification and relative location, with potential applications in virtual reality and augmented reality system designs.

Stress estimation is key to the early detection and mitigation of health problems, enhancing driving safety through driver stress monitoring, and improving human-robot interaction efficiency by adapting to user's stress levels. In this paper, we present a novel method for video-based remote stress estimation and categorization, which involves two separate experiments: one for stress task classification and another for multilevel stress classification. The method combines two deep learning approaches, the Compact Convolutional Transformer (CCT) and Long Short-Term Memory (LSTM), to form a CCT-LSTM pipeline. For each modality (facial expression and rPPG), a CCT model is used to extract features, followed by an LSTM block for temporal pattern recognition. In stress task classification, T1, T2, and T3 tasks from the UBFC-Phys dataset are used, utilizing sevenfold cross-validation. The results indicated a mean accuracy of 83.2% and an F1 score of 83.4%. For multilevel stress classification, the control (lower stress) and test (higher stress) groups from the same dataset were used with fivefold cross-validation, achieving a mean accuracy of 80.5% and an F1 score of 80.3%. The results suggest that our proposed model surpasses existing stress estimation methods by effectively using multimodal deep learning and the CCT-LSTM pipeline for precise, non-invasive stress detection and categorization, with applications in health monitoring, safety, and interactive technologies. © 2024 IEEE.