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BACKGROUND: Advancements in health monitoring technologies are increasingly relying on capturing heart signals from video, a method known as remote photoplethysmography (rPPG). This study aims to enhance the accuracy of rPPG signals using a novel computer technique. METHODS: We developed a machine-learning model to improve the clarity and accuracy of rPPG signals by comparing them with traditional photoplethysmogram (PPG) signals from sensors. The model was evaluated across various datasets and under different conditions, such as rest and movement. Evaluation metrics, including dynamic time warping (to assess timing alignment between rPPG and PPG) and correlation coefficients (to measure the linear association between rPPG and PPG), provided a robust framework for validating the effectiveness of our model in capturing and replicating physiological signals from videos accurately. RESULTS: Our method showed significant improvements in the accuracy of heart signals captured from video, as evidenced by dynamic time warping and correlation coefficients. The model performed exceptionally well, demonstrating its effectiveness in achieving accuracy comparable to direct-contact heart signal measurements. CONCLUSIONS: This study introduces a novel and effective machine-learning approach for improving the detection of heart signals from video. The results demonstrate the flexibility of our method across various scenarios and its potential to enhance the accuracy of health monitoring applications, making it a promising tool for remote healthcare.
This research explores a new way to monitor health using video, which is less invasive than traditional methods that require direct skin contact. We developed a computer program that improves the accuracy of heart signals captured from video. This is done by comparing these video-based signals with standard clinical signals from physical sensors on the skin. Our findings show that this new method can match the accuracy of conventional clinical methods, enhancing the reliability of non-contact health monitoring. This advancement could make health monitoring more accessible and comfortable, offering a potential for doctors to track patient health remotely, making everyday medical assessments easier and less intrusive.
RESUMO
Temperature-responsive liquid chromatography (TRLC) offers a promising alternative to reversed-phase liquid chromatography (RPLC) for environmentally friendly analytical techniques by utilizing pure water as a mobile phase, eliminating the need for harmful organic solvents. TRLC columns, packed with temperature-responsive polymers coupled to silica particles, exhibit a unique retention mechanism influenced by temperature-induced polymer hydration. An investigation of the physicochemical parameters driving separation at high and low temperatures is crucial for better column manufacturing and selectivity control. Assessment of predictability using a dataset of 139 molecules analyzed at different temperatures elucidated the molecular descriptors (MDs) relevant to retention mechanisms. Linear regression, support vector regression (SVR), and tree-based ensemble models were evaluated, with no standout performer. The precision, accuracy, and robustness of models were validated through metrics, such as r and mean absolute error (MAE), and statistical analysis. At 45 ∘ C , logP predominantly influenced retention, akin to reversed-phase columns, while at 5 ∘ C , complex interactions with lipophilic and negative MDs, along with specific functional groups, dictated retention. These findings provide deeper insights into TRLC mechanisms, facilitating method development and maximizing column potential.
