With cardiovascular disease as the leading cause of death worldwide, there is a need for improved wearable monitoring tools for assessing the health of the cardiovascular system. Photoplethysmography (PPG) is a continuous, non-invasive measurement that encodes a multitude of informative vital signs, including heart rate, heart rate variability, respiratory rate, cardiac output, and arterial stiffness. Although existing PPG sensing technologies record from the finger or wrist, the face presents a promising and underutilized location for wearable pulse sensing. This work presents a novel wearable PPG sensing system that records at multiple wavelengths and facial locations. As a proof-of-concept, we seek to evaluate a potential application of our system incorporated in a surgical face mask for use in intra-operative hemodynamic monitoring. By collecting data with our system alongside ground truth cardiovascular vital signs, we can build and test non-invasive inference algorithms. After validating our system’s heart rate detection accuracy with a standard error of 2.84 beats per minute, we now proceed to test our device’s ability to infer additional cardiovascular parameters. In addition to showing promise for novel non-invasive, continuous surgical monitoring, this work has broader implications for wearable health applications based on face-worn form factors such as glasses, helmets, and headsets.

Blood pressure (BP) serves as the primary indicator of a patient’s cardiovascular health. Today, cuff-based BP monitors are the gold standard for routine blood pressure monitoring. However, they are prone to inaccuracies and cannot provide continuous readings. Continuously monitoring BP would allow patients to observe their BP fluctuations from eating, medicine intake, and exercise, thus empowering individuals diagnosed with hypertension to make better-informed health decisions. This motivates the need for a non-invasive, continuous blood pressure monitor. Prior studies have already shown the potential for pulse transit time (PTT), which is the time for a pulse wave to travel between two arterial sites, to be used for non-invasively measuring BP. In this work, I hope to improve upon this technique. To do this, I focus on testing and improving the pulse detection accuracy of a system incorporating an optical sensor array in a surgical eye protection face mask. By getting a better resolution of the pulse waves, I believe the estimate of BP will be more accurate and, in turn, provide a valuable dataset to further investigate the relationship between PTT and BP.

Humans are incapable of regenerating a majority of their major tissues following traumatic injury. Tadpoles from the frog species Xenopus tropicalis have the ability to regenerate lost spinal cord, vasculature, muscle, and cartilage within a few days following injury. The regulatory mechanisms of gene expression necessary for regeneration have not yet been well defined. My primary interest is in understanding the relationship between stress signaling and gene expression during regeneration. The lab has shown that the stress responsive transcription factor Hypoxia Inducible Factor 1α (Hif1α) is necessary for the expression of Wnt target genes, one of the primary signaling processes necessary for regeneration. While we have found that Hif1α is necessary for Wnt target gene expression, we do not know the epistatic relationship between Hif1α and Wnt. In order to test this relationship, I utilized the drug IWR to antagonize Wnt and found that tadpoles treated with IWR have reduced tail regeneration 72 hours post amputation (hpa). I then supplemented these tadpoles with DMOG to stabilize Hif1α and found that DMOG is sufficient to rescue tail regeneration, suggesting that Hif1α is downstream of Wnt. In order to determine if Hif1α is sufficient for Wnt target gene expression, I extracted RNA from regenerating tails 24 hPa and used quantitative PCR (qPCR) to determine relative gene expression. I also utilized in situ hybridization to see if expression of these genes is restricted to regenerating tissues. As Wnt is a known regulator of neural and muscle development, I investigated how inhibiting Hif1α would impact complex tissue regeneration and found that Hif1α is necessary for regeneration of axons and muscle specifically. By determining the epistatic relationship between Hif1α and Wnt through the analysis of specific gene expression, we continue to improve our understanding of how regenerative organisms convert stress signals to cell fate signals.

During surgeries, constant blood pressure sensing is important to counteract the possibility of hypotension, which is a dangerously low drop in blood pressure. Although monitoring blood pressure with invasive arterial catheters can provide continuous information to the anesthesiologist, discomfort and health risks related to using an invasive method limit their use to only a few high-risk surgeries. While blood pressure cuffs to non-invasively measure blood pressure do exist, they are usually uncomfortable and can only periodically record blood pressure. This motivates the need for a tool to perform continuous, non-invasive blood pressure sensing. Here, we validate the use of facial photoplethysmography (PPG) signals to accurately infer blood pressure. Using our wearable eye face mask mounted with optical sensors, we collect PPG signals while the subject is undergoing surgery. Then, we can calculate blood pressure from the PPG signals and subsequently determine the accuracy of the blood pressure measurements. To infer blood pressure from non-invasive facial PPG signals, we apply temporal deep learning techniques that can model dynamic changes in the cardiovascular system. First, we test potential filtering methods by performing peak detection on noisy PPG data to determine which filtering method cleans the signals the best. Then, we incorporate several machine learning models, including autoencoders, to compress parts of the PPG signals into more featurized components. In the final step, we test the face mask sensor data to find the root mean square error (RMSE) of the predictive model compared to that of the ground truth. We expect that it is possible to infer blood pressure from noisy sensor data, as an alternative to invasive arterial catheters.