The high transmission rate and fast mutation capacity of SARS-CoV-2 requires development of low-cost/widely deployable biosensor technologies. Here, we describe the development of an enzymatic biosensor to monitor SARS-CoV-2 and its emerging variants in human saliva based. Biotinylated angiotensin converting enzyme (ACE2) was immobilized on streptavidin-coated fiber optic probes and gold chips for biolayer interferometry (BLI) and surface plasmon resonance (SPR), respectively. Kinetics of ACE2-virion binding for wild-type, Delta, UK, and South Africa variants were analyzed and compared for informing the development of a mobile detection system.

Thermally-induced optical reflection of sound (THORS) allows for the manipulation of sound waves without the need for traditional acoustically engineered structures. By photo-thermally exciting a medium, with infrared laser light, a barrier can be formed due to abrupt changes in compressibility of the excited medium. In this work, we demonstrate for the first time, the ability to generate THORS barriers in ambient air. Additionally, the temporal dynamics of THORS barriers, in ambient air, were characterized by using ultrasonic transducers.

Home blood glucose monitoring devices (i.e., glucometers) have been an essential tool in managing diabetes mellitus as well as hypo- and hyper- glycemia events since the 1980s. These traditional glucometers allowed individuals to measure their blood glucose levels at home through a quick and simple procedure of extracting blood via a finger prick. In recent years, newer commercial devices have evolved to provide continuous glucose monitoring throughout the day in a less invasive method than older glucometers. In this contribution, we investigate the feasibility of developing a non-invasive method for continuous glucose monitoring by integrating wearable sensors and learning-based algorithms.

A self-adhesive, elastic fabric, nanocomposite skin-strain sensor (called Motion Tape) has been developed, tested in controlled laboratory environments, and validated through human subject studies. This study aimed to interpret Motion Tape data using deep learning methods to directly predict functional movement parameters (e.g., joint angles and limb positions) and verifying the results using optical motion capture. The approach was to obtain human participant Motion Tape testing data and training the datasets using ground truth values acquired from the optical motion capture system. Predictions of muscle engagement, strain, and range-of-movement of major joints were investigated to validate the proposed methods.