In a conversation with Physics World’s Margaret Harris, Center Director Amanda Randles explains how she uses physics-based, computationally intensive simulations to develop new ways to diagnose and treat human disease. She has also investigated how data from wearable devices such as smartwatches can be used identify signs of heart disease. Listen to the podcast
The Luck Lab developed an integrated data framework that links youth athletes’ reported activity and exposure with wearable head-impact kinematics to improve the ability to find, track, and treat mild traumatic brain injury.
Sleep monitoring is essential for assessing overall health and managing sleep disorders, yet clinical adoption of consumer wearables remains limited due to inconsistent performance and scarce open source datasets and transparent codebase. In this study, we introduce WatchSleepNet, a novel, open-source three-stage sleep staging algorithm. The model uses sequence-to-sequence architecture integrating Residual Networks (ResNet), Temporal…
Smartphones and wearable sensors offer an unprecedented ability to collect peripheral psychophysiological signals across diverse timescales, settings, populations, and modalities. However, open-source software development has yet to keep pace with rapid advancements in hardware technology and availability, creating an analytical barrier that limits the scientific usefulness of acquired data. We propose a community-driven, open-source peripheral…
Jessilyn Dunn gathers biometric data from smartwatches to study and predict health changes. Read the article
Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance…
Diabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies to improve monitoring of glycemic health are desperately needed. In this proof-of-concept study, we explore the relationship between non-invasive wearables and glycemic metrics and demonstrate the feasibility of using non-invasive wearables…
Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There…
Smart portable devices- smartphones and smartwatches- are rapidly being adopted by the general population, which has brought forward an opportunity to use the large volumes of physiological, behavioral, and activity data continuously being collected by these devices in naturalistic settings to perform research, monitor health, and track disease. While these data can serve to revolutionize…