Estimation of the centre of pressure (COP) is an important part of the gait analysis, for example, when evaluating the functional capacity of individuals affected by motor impairment. Inertial measurement units (IMUs) and force sensors are commonly used to measure gait characteristic of healthy and impaired subjects. We present a methodology for estimating the COP solely from raw gyroscope, accelerometer, and magnetometer data from IMUs using statistical modelling. We demonstrate the viability of the method using an example of two models: a linear model and a non-linear Long-Short-Term Memory (LSTM) neural network model. Models were trained on the COP ground truth data measured using an instrumented treadmill and achieved the average intra-subject root mean square (RMS) error between estimated and ground truth COP of 12.3 mm and the average inter-subject RMS error of 23.7 mm which is comparable or better than similar studies so far. We show that the calibration procedure in the instrumented treadmill can be as short as a couple of minutes without the decrease in our model performance. We also show that the magnetic component of the recorded IMU signal, which is most sensitive to environmental changes, can be safely dropped without a significant decrease in model performance. Finally, we show that the number of IMUs can be reduced to five without deterioration in the model performance.
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http://dx.doi.org/10.3390/s20216136 | DOI Listing |
BDJ Open
December 2024
Faculty of Odonto-Stomatology, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, 749000, Vietnam.
Introduction: Guided surgery for immediate anterior implants aims to reduce the time required for aesthetic and functional immediate loading. However, the limited surface area of anterior teeth for guide stabilization may affect the accuracy of implant positioning. This in vitro study evaluated the effect of the number of supporting teeth on the accuracy of immediate implants in the maxillary central incisor region.
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December 2024
Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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December 2024
Mahidol University Health Technology Assessment (MUHTA) Graduate Program, Mahidol University, Bangkok, 10400, Thailand.
No cost-effectiveness information of preventive strategies for mother-to-child transmission (MTCT) of hepatitis B virus (HBV) has existed for policy decision making. This study aimed to compare the cost-effectiveness of alternative strategies to prevent MTCT of HBV in Vietnam. Cost-utility analysis using a hybrid decision-tree and Markov model were performed from healthcare system and societal perspectives.
View Article and Find Full Text PDFBMC Med Educ
December 2024
Department of Orthopedics, Guru Gobind Singh Medical College and Hospital, Faridkot, Punjab, 151203, India.
Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory.
View Article and Find Full Text PDFRespir Res
December 2024
Department of Mechanical and Product Design Engineering, Swinburne University of Technology, Hawthorn, VIC, Australia.
By virtue of applying small tidal volumes, high-frequency ventilation is advocated as a method of minimizing ventilator-induced lung injury. Lung protective benefits are established in infants, but not in other patient cohorts. Efforts to improve and extend the lung protection potential should consider how fundamental modes of gas transport can be exploited to minimize harmful tidal volumes while maintaining or improving ventilation.
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