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Background: Manual wheelchair propulsion is often associated with pain in the upper extremities. Recording spatio-temporal parameters can optimize movement patterns and prevent injuries. This study compares a marker-based camera system with inertial measurement units to validate their use in wheelchair propulsion on a test stand.
Methods: Spatio-temporal parameters of 27 manual wheelchair users propelling at three self-selected speeds (slow, normal, fast) were simultaneously recorded using a marker-based camera system and inertial measurement units, and subsequently compared between both systems.
Results: A high correlation was observed among all spatio-temporal parameters ( > 0.992). The biases for the start time of hand contact with the pushrim (-0.02 ± 0.02 s), hand release from the pushrim (-0.02 ± 0.01 s), and push length (-0.45 ± 21.45 ms) were slightly overestimated, while recovery length (0.54 ± 21.02 ms), cycle speed (2.37 ± 2.67°/s), and push angle (1.75 ± 4.14°) were slightly underestimated. No bias was found for propulsion frequency.
Conclusions: The spatio-temporal parameters recorded using inertial measurement units are suitable for the evaluation of manual wheelchair propulsion and can be used in a clinical context. The low acquisition costs and simple installation process may increase the use of inertial measurement units in the future.
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http://dx.doi.org/10.3390/s25154676 | DOI Listing |
Prev Vet Med
September 2025
World Organisation for Animal Health (WOAH) Sub-Regional Representation for South East Asia, Bangkok 10400, Thailand.
Foot and mouth disease (FMD) remains endemic in several countries across Southeast Asia, China, and Mongolia (SEACFMD region), posing an ongoing threat to livestock and trade. This study aimed to investigate the epidemiological characteristics and analyze the spatial and temporal distribution of FMD outbreaks reported across the SEACFMD region. FMD outbreak and virus lineage data from 2015 to 2023 were utilized.
View Article and Find Full Text PDFComput Methods Programs Biomed
August 2025
CardioVascular Systems Imaging and Artificial Intelligence Lab, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore; Department of Biomedical Engineering, National University of Singapore, Singapore. Electronic address:
Background And Objective: To develop an end-to-end artificial intelligence solution-video-based Multi-Point Tracking Network (MPTN), for detecting and tracking atrioventricular junction (AVJ) points from cardiovascular magnetic resonance and deriving AVJ motion parameters.
Methods: The MPTN model consists of two modules: AVJ point detection and AVJ motion tracking. The detection module utilizes convolutional-based feature extraction and elastic regression to detect all candidate AVJ points.
Imaging Neurosci (Camb)
September 2025
CEA, Joliot, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France.
We propose a new, modular, open-source, Python-based 3D+time realistic functional magnetic resonance imaging (fMRI) data simulation software. SNAKE or imulator from eurovascular coupling to cquisition of -space data for xploration of fMRI acquisition techniques. It is the first simulator to simulate the entire chain of fMRI data acquisition, from the spatio-temporal design of evoked brain responses to various 3D sampling strategies of k-space data with multiple coils.
View Article and Find Full Text PDFAnimals (Basel)
August 2025
School of Life Sciences, Nanchang University, Nanchang 330031, China.
Macroinvertebrates are a crucial part of aquatic ecosystems and significantly contribute to the maintenance of their health and stability. Our aims were to explore spatio-temporal patterns in macroinvertebrate communities and evaluate the ecological health of various parts of the Poyang Lake Basin during the early stage of a fishing ban. We collected samples using a Peterson grab sampler and conducted ecological evaluations using the B-IBI index.
View Article and Find Full Text PDFPLoS One
August 2025
School of Information Engineering, North China University of Water Resources and Electric Power, Henan, Zhengzhou, China.
Traditional knowledge graphs of water conservancy project risks have supported risk decision-making. However, they are constrained by limited data modalities and low accuracy in information extraction. A multimodal water conservancy project risk knowledge graph is proposed in this study, along with a synergistic strategy involving multimodal large language models Risk decision-making generation is facilitated through a multi-agent agentic retrieval-augmented generation framework.
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