The present study analyzed the multi-location external workload profile in basketball players using a previously validated test battery and compared the demands among anatomical locations. A basketball team comprising 13 semi-professional male players was evaluated in five tests (abilities/skills/tests): (a) aerobic, linear movement, 30-15 IFT; (b) lactic anaerobic, acceleration and deceleration, 16.25 m RSA (c) alactic anaerobic, curvilinear movement, 6.75 m arc (d) elastic, jump, Abalakov test (e) physical-conditioning, small-sided game, 10' 3 vs.3 10 × 15 m. PlayerLoad was evaluated at six anatomical locations simultaneously (interscapular line, lumbar region, knees and ankles) by six WIMU PRO inertial devices attached to the player using an ad hoc integral suit. Statistical analysis was composed of an ANOVA of repeated measures and partial eta squared effect sizes. Significant differences among anatomical locations were found in all tests with higher values in the location nearer to ground contact ( < 0.01). However, differences between lower limb locations were only found in curvilinear movements, with a higher workload in the outside leg ( < 0.01). Additionally, high between-subject variability was found in team players, especially at lower limb locations. In conclusion, multi-location evaluation in sports movements will make it possible to establish an individual external workload profile and design specific strategies for training and injury prevention programs.
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http://dx.doi.org/10.3390/s21103441 | DOI Listing |
Sci Data
January 2025
Hochschule für Technik und Wirtschaft Berlin (HTW Berlin), Berlin, Germany.
Road unevenness significantly impacts the safety and comfort of traffic participants, especially vulnerable groups such as cyclists and wheelchair users. To train models for comprehensive road surface assessments, we introduce StreetSurfaceVis, a novel dataset comprising 9,122 street-level images mostly from Germany collected from a crowdsourcing platform and manually annotated by road surface type and quality. By crafting a heterogeneous dataset, we aim to enable robust models that maintain high accuracy across diverse image sources.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists.
Materials And Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLaVA network.
J Strength Cond Res
December 2024
Jayhawk Athletic Performance Laboratory-Wu Tsai Human Performance Alliance, University of Kansas, University of Kansas, Lawrence, Kansas.
Philipp, NM, Blackburn, SD, Cabarkapa, D, and Fry, AC. The effects of a low-volume, high-intensity pre-season micro-cycle on neuromuscular performance in collegiate female basketball players. J Strength Cond Res 38(12): 2136-2146, 2024-The use of stretch-shortening cycle (SSC)-based measures of vertical jump performance to monitor responses to training exposures is common practice in sport science.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Institute of Health System Science, Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.
: Positron emission tomography (PET) is a valuable tool for the assessment of lymphoma, while artificial intelligence (AI) holds promise as a reliable resource for the analysis of medical images. In this context, we systematically reviewed the applications of deep learning (DL) for the interpretation of lymphoma PET images. : We searched PubMed until 11 September 2024 for studies developing DL models for the evaluation of PET images of patients with lymphoma.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.
: Accurate determination of the natural head position (NHP) is essential in orthognathic surgery for optimal surgical planning and improved patient outcomes. However, traditional methods encounter reproducibility issues and rely on external devices or patient cooperation, potentially leading to inaccuracies in the surgical plan. : To address these limitations, we developed a geometric deep learning network (NHP-Net) to automatically reproduce NHP from CT scans.
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