It is important to evaluate walking stability to improve people's health. There are many cases of unstable walking even if people think they are in good health. A small, light acceleration sensor was attached to subjects' center of gravity to measure the acceleration displacement while the subjects were walking. By using the seasonal adjustment model it was possible to predict the periodic fluctuations observed, decomposing the original data into factors representing stability and instability. We suggest the walking stability of each subject using the ratio of the variance of instability to the variance of stability. This study provides useful information for understanding walking systems in preventive medicine and rehabilitative medicine.
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http://dx.doi.org/10.1109/IEMBS.2007.4353460 | DOI Listing |
ACS Sens
January 2025
Department of Physics, Chalmers University of Technology, SE-41296 Göteborg, Sweden.
Rapidly detecting hydrogen leaks is critical for the safe large-scale implementation of hydrogen technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets under technically relevant conditions. Here, we demonstrate how a tailored long short-term transformer ensemble model for accelerated sensing (LEMAS) speeds up the response of an optical plasmonic hydrogen sensor by up to a factor of 40 and eliminates its intrinsic pressure dependence in an environment emulating the inert gas encapsulation of large-scale hydrogen installations by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware.
View Article and Find Full Text PDFNatl Sci Rev
January 2025
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
The high thermopower of ionic thermoelectric (-TE) materials holds promise for miniaturized waste-heat recovery devices and thermal sensors. However, progress is hampered by laborious trial-and-error experimentations, which lack theoretical underpinning. Herein, by introducing the simplified molecular-input line-entry system, we have addressed the challenge posed by the inconsistency of -TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an of 0.
View Article and Find Full Text PDFSci Rep
January 2025
Strathclyde Institute of Education, University of Strathclyde, Lord Hope Building, Glasgow, G4 0LT, UK.
Computational analysis of infant movement has significant potential to reveal markers of developmental health. We report two studies employing dynamic analyses of motor kinematics and motor behaviours, which characterise movement at two levels, in 9-month-old infants. We investigate the effect of preterm birth (< 33 weeks of gestation) and the effect of changing emotional and social-interactive contexts in the still-face paradigm.
View Article and Find Full Text PDFACS Sens
January 2025
College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
Greenhouse gases (GHGs) have caused great harm to the ecological environment, so it is necessary to screen gas sensor materials for detecting GHGs. In this study, we propose an ideal gas sensor design strategy with high screening efficiency and low cost targeting four typical GHGs (CO, CH, NO, SF). This strategy introduces machine learning (ML) methods based on density functional theory (DFT) to achieve accurate and rapid screening from a large number of candidate gas sensor materials.
View Article and Find Full Text PDFACS Sens
January 2025
Jiangsu Provincial Key Laboratory of Advanced Robotics, School of Mechanical and Electric Engineering, Soochow University, Suzhou 215137, China.
Atrial fibrillation (AF) as one of the most common cardiovascular diseases has attracted great attention due to its high disability and mortality rate. Thus, a timely and effective recognition method for AF is of great importance for diagnosing and preventing it. Herein, we proposed a novel intelligent sensing and recognition system for AF which combined Traditional Chinese Medicine (TCM), flexible wearable electronic devices, and artificial intelligence.
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