Wheelchair fencing (WF) is a Paralympic sport which is practised by athletes with physical disabilities and is classified into three categories according to the degree of activity limitation the impairment causes in the sport. All Paralympic sports are requested to develop their own evidence-based classification system to enhance the confidence in the classification process; however, this is yet to be achieved in WF. Research within WF is scarce; therefore, the aim of this study was to reach expert consensus on the physical characteristics that underpin performance of athletes competing in the sport as this is known as one of the initial steps required to achieve an evidence-based classification system. Sixteen Paralympic WF coaches were invited to take part in a three-round Delphi study, with experts drawing consensus on qualities of speed, strength, power, flexibility and motor control of the trunk and fencing arm being associated with increased athletic success. The required qualities of the non-fencing arm led to diverging opinions across the expert panel. This study provides clear guidance of the physical qualities to be developed to maximize athletic performance while also providing the initial framework to guide future WF classification research.
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http://dx.doi.org/10.1080/02640414.2021.1912454 | DOI Listing |
Sensors (Basel)
December 2024
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's disease (PD) on motor control, balance, and cognitive function. We assess the test-retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials.
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December 2024
College of Physical Education and Health Engineering, Taiyuan University of Technology, Jinzhong 030600, China.
The application of dynamic data in biomechanics is crucial; traditional laboratory-level force measurement systems are precise, but they are costly and limited to fixed environments. To address these limitations, empirical evidence supports the widespread adoption of portable force-measuring platforms, with recommendations for their ongoing development and enhancement. Taiyuan University of Technology has collaborated with KunWei Sports Technology Co.
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December 2024
Laboratoire d'Analyse et d'Architecture des Systèmes (LAAS), Université de Toulouse, CNRS, UPS, 7 Avenue du Colonel Roche, 31031 Toulouse, France.
The need for odor measurement and pollution source identification in various sectors (aeronautic, automobile, healthcare…) has increased in the last decade. Multisensor modules, such as electronic noses, seem to be a promising and inexpensive alternative to traditional sensors that were only sensitive to one gas at a time. However, the selectivity, the non-repetitiveness of their manufacture, and their drift remain major obstacles to the use of electronic noses.
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December 2024
School of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
Permanent magnet synchronous motors (PMSMs) are widely used in a variety of fields such as aviation, aerospace, marine, and industry due to their high angular position accuracy, energy conversion efficiency, and fast response. However, driving errors caused by the non-ideal characteristics of the driver negatively affect motor control accuracy. Compensating for the errors arising from the non-ideal characteristics of the driver demonstrates substantial practical value in enhancing control accuracy, improving dynamic performance, minimizing vibration and noise, optimizing energy efficiency, and bolstering system robustness.
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December 2024
College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios.
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