Jesse Fine

TL;DR: In this article, a comprehensive review of the literature that aims to summarize these noise sources for future photoplethysmography (PPG) device development for use in health monitoring is presented.

TL;DR: It is shown that nanoscale physical properties of protein aggregates from the cerebral spinal fluid and blood of patients are altered during AD pathogenesis and that these properties can be used as a new class of “physical biomarkers” to impartially diagnose AD and predict its progression.

TL;DR: Design selection for a multimodal and fully implantable glucose biosensor is presented and it was determined that a stacked cylinder design, 0.43cm in length with 0.036cm thick repeating units provides the best fluorescent signal.

TL;DR: This work presents a computational workflow that combines Monte Carlo modeling (MC), gaussian combination, and additive noise to create synthetic dataset of volar fingertip PPG waveforms representative of a diverse cohort to improve the limitations of current synthetic PPG frameworks.

TL;DR: To predict the anticipated signal of a remote dual-photoplethysmogram (PPG) blood pressure monitor, parallelized multi-layer Monte Carlo modelling was utilized to estimate light transport through the index finger at the proximal phalange and results indicate that at a separation of 3.0mm, 700nm wavelength provides signal resolution that is indicative of contribution from the artery and not the arterioles, which could be beneficial for estimating pulse transit time toward calculating blood pressure.