首页|New Machine Learning Study Findings Recently Were Reported by Researchers at California Institute of Technology (Caltech) (A Physicochemical-sensing Electronic Skin for Stress Response Monitoring)
New Machine Learning Study Findings Recently Were Reported by Researchers at California Institute of Technology (Caltech) (A Physicochemical-sensing Electronic Skin for Stress Response Monitoring)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Data detailed on Machine Learning have been presented. According to news reporting out of Pasadena, California, by NewsRx editors, research stated, “Approaches to quantify stress responses typically rely on subjective surveys and questionnaires. Wearable sensors can potentially be used to continuously monitor stress-relevant biomarkers.” Financial supporters for this research include National Aeronautics & Space Administration (NASA), Translational Research Institute for Space Health through NASA, Office of Naval Research, Army Research Office, National Institutes of Health (NIH) - USA, National Science Foundation (NSF), National Academy of Medicine Catalyst Award, Tobacco-Related Disease Research Program and Heritage Medical Research Institute, Amazon AI4Science Fellowship, Kavli Nanoscience Institute at Caltech. Our news journalists obtained a quote from the research from the California Institute of Technology (Caltech), “However, the biological stress response is spread across the nervous, endocrine and immune systems, and the capabilities of current sensors are not sufficient for condition-specific stress response evaluation. Here we report an electronic skin for stress response assessment that non-invasively monitors three vital signs (pulse waveform, galvanic skin response and skin temperature) and six molecular biomarkers in human sweat (glucose, lactate, uric acid, sodium ions, potassium ions and ammonium). We develop a general approach to prepare electrochemical sensors that relies on analogous composite materials for stabilizing and conserving sensor interfaces. The resulting sensors offer long-term sweat biomarker analysis of more than 100 h with high stability. We show that the electronic skin can provide continuous multimodal physicochemical monitoring over a 24-hour period and during different daily activities. With the help of a machine learning pipeline, we also show that the platform can differentiate three stressors with an accuracy of 98.0% and quantify psychological stress responses with a confidence level of 98.7%.”
PasadenaCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningCalifornia Institute of Technology (Caltech)
NETL
NSTL
