Exploring the Use of Granger Causality for the Identification of Chemical Exposure Based on Physiological Data

Wearable sensors offer new opportunities for the early detection and identification of toxic chemicals in situations where medical evaluation is not immediately possible. We previously found that continuously recorded physiology in guinea pigs can be used for early detection of exposure to an opioid (fentanyl) or a nerve agent (VX), as well as...

Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA

Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in...

Multi-Level Driver Workload Prediction Using Machine Learning and Off-The-Shelf Sensors

The present study aims to add to the literature on driver workload prediction using machine learning methods. The main aim is to develop workload prediction on a multi-class basis, rather than a binary high/low distinction as often found in litearature. The presented approach relies on measures that can be obtained unobtrusively in the driving...

Diffuse reflectance spectroscopy as a tool for real-time tissue assessment during colorectal cancer surgery

Colorectal surgery is the standard treatment for patients with colorectal cancer. To overcome two of the main challenges, the circumferential resection margin and postoperative complications, real-time tissue assessment could be of great benefit during surgery. In this ex vivo study, diffuse reflectance spectroscopy (DRS) was used to...