Traditional cuff-based blood pressure (BP) measurement techniques, though widely employed, require a cuff that must be fitted and inflated and thus are limited by convenience issues. In contrast, contactless BP monitoring solutions offer a promising alternative. This study explored pulse transit time (PTT) as a feature for accurate BP monitoring using remote photoplethysmography (rPPG). The investigation examined the order of PTT (PTT Order) between the palm and forehead and their impacts on the accuracy of BP estimation. Our findings showed variation in the dominant order between the two sites among the subjects. Nevertheless, the inverse of mean PTT extracted from the two sites in dominant order (PTT Dominant Order) consistently showed a higher linear correlation with systolic blood pressure (SBP). The mean and standard deviation of R-squared derived from the inverse of mean PTT with the dominant order and SBP among the 16 subjects were 0.81 ± 0.13. Additionally, subgroup analysis identified significant differences in SBP across gender and exercise status. Furthermore, our data revealed a hysteresis phenomenon in 25% of the subjects, characterized by SBP returning to baseline levels during post-exercise resting while heart rate (HR) remained persistently elevated.

Blood pressure (BP) monitoring is crucial for health assessment, but existing contact-based methods face cost and comfort barriers. Remote photoplethysmography (rPPG) offers a promising contactless solution, yet research is hampered by limited rPPG datasets with corresponding BP labels. This paper presents a transfer learning methodology for BP measurement. This approach involves utilizing a base dataset comprising signals produced by PPG (PPG-signals) to acquire knowledge that can be transferred to a target dataset containing signals generated by rP PG (rPPG-signals). In our study, we trained diverse deep-learning models using publicly available datasets containing PPG-signals. Subsequently, these models were fine-tuned and evaluated using a public dataset that specifically consists of rP PG-signals. Additionally, we explored the relationship between BP and heart rate, and examined different loss functions and normalization approaches to optimize the performance of the deep learning models. The findings of our study demonstrate that our best model achieved a better performance than the state-of-the-art model, with mean absolute error (MAE) of 8.721 (reduced by 4.879) mmHg and 8.653 (reduced by 1.647) mmHgfor systolic blood pres-sure (SBP) and diastolic blood pressure (DBP) in a dataset with clinical settings, showing promising potential for remote BP estimation.

With advances in Virtual Reality (VR) technology, user expectation for a near-perfect experience is also increasing. The push for a wider field-of-view can increase the challenges of correcting lens distortion. Past studies on imperfect VR experiences have focused on motion sickness provoked by vection-inducing VR stimuli and discomfort due to mismatches in accommodation and binocular convergence. Disorientation and discomfort due to unintended optical flow induced by lens distortion, referred to as dynamic distortion (DD), has, to date, received little attention. This study examines and models the effects of DD during head rotations with various fixed gazes stabilized by vestibulo-ocular reflex (VOR). Increases in DD levels comparable to lens parameters from poorly designed commercial VR lenses significantly increase discomfort scores of viewers in relation to disorientation, dizziness, and eye strain. Cross-validated results indicate that the model is able to predict significant differences in subjective scores resulting from different commercial VR lenses and these predictions correlated with empirical data. The present work provides new insights to understand symptoms of discomfort in VR during user interactions with static world-locked / space-stabilized scenes and contributes to the design of discomfort-free VR headset lenses.