The technological advancement in wireless health monitoring allows the development of light-weight wrist-worn wearable devices to be equipped with different sensors. Although the equipped photoplethysmography (PPG) sensors can measure the changes in the blood volume directly through the contact with skin, the motion artifact (MA) is possible to occur during an intense exercise. In this study, we attempted to perform heart rate (HR) estimation by proposing a post-calibration approach during the three possible states of average daily activity (resting, sleeping, and intense treadmill activity states) in 29 participants (130 minutes/person) on four popular wearable devices: Fitbit Charge HR, Apple Watch Series 4, TicWatch Pro, and Empatica E4. In comparison to the HR provided by Fitbit Charge HR (HR Fitbit ) with the highest error of 3.26 ± 0.34 bpm in resting, 2.33 ± 0.23 bpm in sleeping, 9.53 ± 1.47 bpm in intense treadmill activity states, and 5.02 ± 0.64 bpm in all states combined, our improving HR estimation model with rolling windows as feature reduced the mean absolute error (MAE) for 33.44% in resting, 15.88% in sleeping, 9.55% in intense treadmill activity states, and 18.73% in all states combined. Four machine learning (ML) algorithms (support vector regression (SVR), random forest (RF), Gaussian process (GP), and artificial neural network (ANN)) were formulated and trained with the tuned hyperparameters. This demonstrates the feasibility of our proposed methods in order to correct and provide HR monitoring post-calibrated with high accuracy, raising further awareness of individual fitness in the daily application.
The modulating effect of chemical compounds and therapeutics on gene transcription is well-reported and has been intensively studied for both clinical and research purposes. Emerging research points toward the utility of drug-induced transcriptional alterations in de novo molecular design and highlights the idea of phenotype-matching an expression signature of interest to the structures being designed. In this work, we build an autoencoder-based generative model, BiCEV, around this concept. Our generative autoencoder has demonstrably generated a set of new molecules from gene expression input with notable validity (96%), uniqueness (98%), and internal diversity (0.77). Further, we attempted to validate BiCEV by testing the model on gene-knockdown profiles and combined signatures of synergistic drug pairs. From these investigations, we found the designed structures to be consistently high in collective quality. However, when their similarities to the supposed functional equivalents as determined by shared targets were considered, the findings were somewhat mixed. In spite of this, we believe the generative model merits further development in conjunction with in vitro corroboration to lend itself to being an assistive tool for drug discovery experts, particularly to support the initial stages of hit identification and lead optimization.
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