Related papers: PhysioZoo: The Open Digital Physiological Biomarke…
Earth Observation (EO) analysis is inherently interactive: resolving uncertainty often requires expanding the region of interest, retrieving historical observations, and switching across sensors such as optical and Synthetic Aperture Radar.…
Ubiquitous healthcare has become one of the prominent areas of research inorder to address the challenges encountered in healthcare environment. In contribution to this area, this study developed a system prototype that recommends…
This work focuses on tracking and understanding human motion using consumer wearable devices, such as VR/AR headsets, smart glasses, cellphones, and smartwatches. These devices provide diverse, multi-modal sensor inputs, including…
Photoplethysmography (PPG) is the leading non-invasive technique for monitoring biosignals and cardiovascular health, with widespread adoption in both clinical settings and consumer wearable devices. While machine learning models trained on…
Modern biomedical applications often involve time-series data, from high-throughput phenotyping of model organisms, through to individual disease diagnosis and treatment using biomedical data streams. Data and tools for time-series analysis…
Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ) relies on intracranial…
Cardiovascular diseases are a leading cause of fatalities worldwide, often occurring suddenly with limited time for intervention. Current healthcare monitoring systems for cardiac patients rely heavily on hospitalization, which can be…
Wearable devices that measure and record physiological signals are now becoming widely available to the general public with ever-increasing affordability and signal quality. The data from these devices introduce serious ethical challenges…
Facial-video based Remote photoplethysmography (rPPG) aims at measuring physiological signals and monitoring heart activity without any contact, showing significant potential in various applications. Previous deep learning based rPPG…
We introduce EgoLife, a project to develop an egocentric life assistant that accompanies and enhances personal efficiency through AI-powered wearable glasses. To lay the foundation for this assistant, we conducted a comprehensive data…
Digital sensors are increasingly being used to monitor the change over time of physiological processes in biological health and disease, often using wearable devices. This generates very large amounts of digital sensor data, for which, a…
Biobanks with genetics-linked electronic health records (EHR) have opened up opportunities to study associations between genetic, social, or environmental factors and longitudinal lab biomarkers. However, in EHRs, the timing of patient…
Electrocardiogram data, one of the most widely available biosignal data, has become increasingly valuable with the emergence of deep learning methods, providing novel insights into cardiovascular diseases and broader health conditions.…
BioStatFlow is a free web application, useful to facilitate the performance of statistical analyses of "omics", including metabolomics, data using R packages. It is a fast and easy on-line tool for biologists who are not experts in…
In this paper, we present DiscreteZOO, a project which illustrates some of the possibilities for computer-supported management of collections of finite combinatorial (discrete) objects, in particular graphs with a high degree of symmetry.…
A deep latent variable model is a powerful method for capturing complex distributions. These models assume that underlying structures, but unobserved, are present within the data. In this dissertation, we explore high-dimensional problems…
ZV-Sim is an open-source, modular Python framework for probabilistic simulation and analysis of pre-emergent novel zoonotic diseases using pervasive sensing data. It incorporates customizable Human and Animal Presence agents that leverage…
The creation of cinematic-quality animal effects necessitates the precise modeling of muscle and fur dynamics, a process that remains both labor-intensive and computationally expensive within traditional production workflows. While…
Physiological signals are increasingly relevant to estimate the mental states of users in human-robot interaction (HRI), yet ROS 2-based HRI frameworks still lack reusable support to integrate such data streams in a standardized way.…
This paper summarizes and presents PulsatioMech: an open-source MATLAB toolbox for seismocardiography (SCG) signal processing. The toolbox may be found here: https://github.com/nzavanelli/SCG_master_toolbox PulsatioMech is currently under…