I investigate whether combining ultra-wideband (UWB) radar with inertial measurement units (IMUs) can produce more robust human pose estimations than using IMUs alone. UWB radar yields precise distance measurements, offering positional data that standard IMUs—sensitive mainly to angular velocity and acceleration—cannot capture. To test this approach, I built an embedded system that integrates a UWB radar module with wearable IMUs, then designed a user study involving everyday movements and targeted exercises performed by a small group of participants. This setup allowed me to collect a diverse dataset under realistic conditions. I processed these data using neural network models, including long short-term memory (LSTM) and transformer architectures, to generate accurate joint angles. I then fed those angles into a 3D skeleton reconstruction model. My preliminary findings suggest that the additional distance data from the UWB radar substantially improves tracking accuracy and reduces ambiguity in limb positioning. This enhanced estimation could lead to more realistic virtual reality avatars, improved fitness tracking, and better physical therapy tools. By overseeing the hardware design, data collection, and model development, I actively demonstrate how interdisciplinary methods can advance human-computer interaction through more precise and accessible pose estimation.

4.5 million adults in the United States are diagnosed with chronic liver disease. Over time this can lead to cirrhosis, an end-stage condition in which scarring occurs in the liver. Reduced liver function from cirrhosis results in accumulations of neurotoxic substances that induce a spectrum of neurological impairments known as hepatic encephalopathy (HE). The critical flicker frequency (CFF) test is a well-established screening test for HE. Previously we developed Beacon, a novel and portable CFF measuring device that can be administered at home via smartphone app, as an accessible alternative to current CFF measurement devices that are large, expensive, and not intended for at-home use. We found that Beacon produced a CFF measurement that aligned with commercially available devices. While the current Beacon reflects current commercial devices, the efficiency of measurement is bottlenecked by the fact that pairs of flickering light stimuli can only be presented sequentially due to the singular light source. We therefore propose a dual headed version of Beacon that gives the option of flashing two frequencies simultaneously. I designed and developed a version of this dual-headed Beacon with sliding heads as well as an accompanying user interface before conducting a series of user studies, beginning with a pilot study on healthy individuals and progressing to a clinical trial on chronic liver disease patients, to evaluate the impact of the number of light sources and the distance between them on CFF measurement time and repeatability. I hypothesize that the two-headed Beacon will produce a CFF measurement more quickly than the original Beacon and that a closer distance between heads will also produce quicker and more consistent measurements. These findings will help inform the development of future iterations of the Beacon, leading to improved outcomes for chronic liver disease patients.